Analysis of the Georgia add-on to the 2016-2017 National Household Travel Survey

GEORGIA DOT RESEARCH PROJECT 18-24 FINAL REPORT
ANALYSIS OF THE GEORGIA ADD-ON TO THE 20162017 NATIONAL HOUSEHOLD TRAVEL SURVEY
OFFICE OF PERFORMANCE-BASED MANAGEMENT AND RESEARCH
600 WEST PEACHTREE STREET NW ATLANTA, GA 30308

1. Report No.:

2. Government Accession No.:

FHWA-GA-21-1824

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4. Title and Subtitle:

Analysis of the Georgia Add-On to the 20162017 National

Household Travel Survey

7. Author(s): Gwen Kash, Ph.D. (https://orcid.org/0000-0002-6177-3491); Patricia L. Mokhtarian, Ph.D. (https://orcid.org/0000-0003-11190041); Giovanni Circella, Ph.D. (https://orcid.org/0000-0003-1832-396X)

3. Recipient's Catalog No.: N/A 5. Report Date: August 2021
6. Performing Organization Code: N/A 8. Performing Organization Report No.: 18-24

9. Performing Organization Name and Address:

10. Work Unit No.:

School of Civil and Environmental Engineering

N/A

Georgia Institute of Technology

11. Contract or Grant No.:

790 Atlantic Ave NW

PI#: 0015718

Atlanta, GA 30318

Phone: (404) 385-1443

Email: patmokh@gatech.edu

12. Sponsoring Agency Name and Address:

13. Type of Report and Period Covered:

Georgia Department of Transportation

Final; August 2018 August 2021

Office of Performance-based Management and Research 600 West Peachtree Street NW Atlanta, GA 30308

14. Sponsoring Agency Code: N/A

15. Supplementary Notes:

Prepared in cooperation with the U.S. Department of Transportation, Federal Highway Administration.

16. Abstract: Through an extensive analysis of the Georgia subsample of the 20162017 National Household Travel Survey, this report provides an in-depth snapshot of the travel behavior of Georgians of all ages. It documents differences in travel needs and behavior by region and between demographic groups, focuses on measurement challenges and improved techniques, and identifies areas where future data collection is needed. In addition to an overview of key travel trends in the state, the report includes chapters on work travel; work flexibility (teleworking and flexible scheduling); new technologies and services, including alternative-fuel vehicles, shared mobility, and online shopping; social inclusion and equity; nonmotorized and access/egress travel; and travel for its own sake.

17. Keywords: Travel behavior, travel patterns, vehicle ownership, commuting, work travel, teleworking, flexible work schedules, online shopping, shared mobility, new technology, alternative-fuel vehicles, nonmotorized travel, social inclusion, equity, travel for its own sake

18. Distribution Statement: No Restriction

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Unclassified

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Form DOT 1700.7 (8-69)

21. No. of Pages: 543

22. Price: Free

GDOT Research Project No. 18-24 / T.O. 2014-63
Final Report
ANALYSIS OF THE GEORGIA ADD-ON TO THE 20162017 NATIONAL HOUSEHOLD TRAVEL SURVEY By Gwen Kash Postdoctoral Fellow Patricia L. Mokhtarian
Susan G. and Christopher D. Pappas Professor and
Giovanni Circella Senior Research Engineer
Georgia Tech Research Corporation Georgia Institute of Technology
Contract with Georgia Department of Transportation
in cooperation with U.S. Department of Transportation Federal Highway Administration
August 2021
The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Georgia Department of Transportation or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation.
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Symbol
in ft yd mi
in2 ft2 yd2 ac mi2
fl oz gal ft3 yd3
oz lb T
oF
fc fl
lbf lbf/in2
Symbol
mm m m km
mm2 m2 m2 ha km2
mL L m3 m3
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oC
lx cd/m2
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SI* (MODERN METRIC) CONVERSION FACTORS

APPROXIMATE CONVERSIONS TO SI UNITS

When You Know

Multiply By

To Find

LENGTH

inches

25.4

millimeters

feet

0.305

meters

yards

0.914

meters

miles

1.61

kilometers

AREA

square inches

645.2

square millimeters

square feet

0.093

square meters

square yard

0.836

square meters

acres

0.405

hectares

square miles

2.59

square kilometers

VOLUME

fluid ounces

29.57

milliliters

gallons

3.785

liters

cubic feet

0.028

cubic meters

cubic yards

0.765

cubic meters

NOTE: volumes greater than 1000 L shall be shown in m3

MASS

ounces

28.35

grams

pounds

0.454

kilograms

short tons (2000 lb)

0.907

megagrams (or "metric ton")

TEMPERATURE (exact degrees)

Fahrenheit

5 (F-32)/9

Celsius

or (F-32)/1.8

ILLUMINATION

foot-candles foot-Lamberts

10.76 3.426

lux candela/m2

FORCE and PRESSURE or STRESS

poundforce

4.45

newtons

poundforce per square inch

6.89

kilopascals

APPROXIMATE CONVERSIONS FROM SI UNITS

When You Know

Multiply By

To Find

LENGTH

millimeters

0.039

inches

meters

3.28

feet

meters

1.09

yards

kilometers

0.621

miles

AREA

square millimeters

0.0016

square inches

square meters

10.764

square feet

square meters

1.195

square yards

hectares

2.47

acres

square kilometers

0.386

square miles

VOLUME

milliliters

0.034

fluid ounces

liters

0.264

gallons

cubic meters

35.314

cubic feet

cubic meters

1.307

cubic yards

MASS

grams

0.035

ounces

kilograms

2.202

pounds

megagrams (or "metric ton")

1.103

short tons (2000 lb)

TEMPERATURE (exact degrees)

Celsius

1.8C+32

Fahrenheit

ILLUMINATION

lux candela/m2

0.0929 0.2919

foot-candles foot-Lamberts

FORCE and PRESSURE or STRESS

newtons

0.225

poundforce

kilopascals

0.145

poundforce per square inch

Symbol
mm m m km
mm2 m2 m2 ha km2
mL L m3 m3
g kg Mg (or "t")
oC
lx cd/m2
N kPa
Symbol
in ft yd mi
in2 ft2 yd2 ac mi2
fl oz gal ft3 yd3
oz lb T
oF
fc fl
lbf lbf/in2

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TABLE OF CONTENTS
EXECUTIVE SUMMARY ...........................................................................................................1 About this Report........................................................................................................................ 2 Key Messages ............................................................................................................................. 4 Challenges and Recommendations ............................................................................................. 7 Leveraging NHTS Data to Understand Complex Travel Patterns ......................................... 7 Mainstreaming Equity............................................................................................................. 8 Assisting Georgians with Mobility Impairments..................................................................... 8 Achieving Sustainable Mobility for Low-income, Carless, and Vehicle-deficit Georgians ... 9 Promoting Transit, Walking, and Biking by Addressing Georgians' Concerns................... 10 Supporting Captive Transit Users ........................................................................................ 10 Improving Pedestrian and Cyclist Safety in Every Neighborhood ....................................... 10 Leveraging New Technologies and Services to Make Transportation More Effective and Equitable ........................................................................................................................... 11 Measuring Work Travel ........................................................................................................ 11 Identifying Data Needs ......................................................................................................... 12
CHAPTER 1. KEY TRAVEL PATTERNS IN THE STATE OF GEORGIA ......................13 Chapter 1 Summary ............................................................................................................... 13 Methodological Notes............................................................................................................... 15 Weighting .............................................................................................................................. 16 Adjustments to Data .............................................................................................................. 17 Assorted Definitions and Notes............................................................................................. 18 Key Terms ............................................................................................................................. 18 Geographic Divisions for Analysis ....................................................................................... 20 Overview................................................................................................................................... 25 Trip Purpose Overview ......................................................................................................... 31 Mode Overview ..................................................................................................................... 34 Trip Patterns by Location of Travel.......................................................................................... 38 Household and Personal Mobility............................................................................................. 40 Trip Purpose.............................................................................................................................. 45 Vehicle Availability and Usage ................................................................................................ 58 Vehicle Ownership ................................................................................................................ 58 Vehicle Usage ....................................................................................................................... 64 Vehicle Fleet Characteristics................................................................................................ 65 Transit Preferences and Use ..................................................................................................... 68 Transit Availability and Use ................................................................................................. 68
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Transit Service Preferences Among Workers ....................................................................... 69
Summary of Findings on Geographical Differences ................................................................ 77
Work Travel (Chapter 2Chapter 3)..................................................................................... 78 New Technology (Chapter 4) ................................................................................................ 80 Equity (Chapter 5) ................................................................................................................ 80 Nonmotorized Travel and Travel for its Own Sake (Chapter 6Chapter 7)......................... 81
CHAPTER 2. COMMUTE AND WORK PATTERNS ...........................................................83 Chapter 2 Summary ............................................................................................................... 83
Overview................................................................................................................................... 85
Definitions and Technical Notes ........................................................................................... 85 Worker Characteristics ......................................................................................................... 87 Overview of Commuters........................................................................................................ 93 Commute Mode by Person .................................................................................................... 98 Overview of Work Journeys ................................................................................................ 101
Defining the Commute............................................................................................................ 109
Purpose-based and Place-based Anchors........................................................................... 113 Measuring Complex Commutes: The Last-leg and Counterfactual Methods..................... 116 Methods for Definition and Measurement of Commutes .................................................... 119 Results ................................................................................................................................. 130 Summary of Methods Implications for Upcoming Report Sections .................................... 134
Work Travel Distance ............................................................................................................. 136
Region ................................................................................................................................. 138 Peak, Off-peak, and Weekend Travel.................................................................................. 142 Demographic Differences ................................................................................................... 146
Work Travel Distance by Mode.............................................................................................. 150
Commute Duration and Burden .............................................................................................. 152
Commute Times by Time of Day and MPO Tier ................................................................. 154 Commute Times by Mode and Vehicle Ownership ............................................................. 156 Demographic Differences in Commute Duration ............................................................... 158 Total Daily Commute Burden ............................................................................................. 159
Conclusion .............................................................................................................................. 162
CHAPTER 3. TELEWORKING AND WORKER SCHEDULE FLEXIBILITY .............165 Chapter 3 Summary ............................................................................................................. 165
Overview................................................................................................................................. 166
Types of Work Flexibility .................................................................................................... 166 Definitions and Technical Notes ......................................................................................... 168 Overview of Travel Day Telecommuting ............................................................................ 173
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Work Flexibility for Whom? .................................................................................................. 176 Descriptive Statistics on Access to Flexible Schedule and Work Location ........................ 176 Logistic Regression Analysis of Eligibility for Flexible Work Schedule ............................ 186 Logistic Regression Analysis of Eligibility for Flexible Work Location............................. 188
The Effects of Flexible Scheduling ........................................................................................ 192 Models of Work Arrival and Departure Times ................................................................... 200
Frequency of Teleworking...................................................................................................... 202 Telecommuting Frequency among Eligible Workers.......................................................... 202 Telecommuting Frequency and Distance to Work .............................................................. 209 Travel Day Telecommuting ................................................................................................. 213
CHAPTER 4. NEW TECHNOLOGIES AND SERVICES ...................................................219 Chapter 4 Summary ............................................................................................................. 219 Alternative-fuel Vehicles ........................................................................................................ 220 Shared Mobility ...................................................................................................................... 228 Methods ............................................................................................................................... 229 Shared Vehicles: Bikesharing and Carsharing................................................................... 231 Ridehailing .......................................................................................................................... 235 Ridehailing and Vehicle-for-Hire Trips.............................................................................. 239 Online Shopping ..................................................................................................................... 241 Online Shopping and Travel Behavior ............................................................................... 251
CHAPTER 5. SOCIAL INCLUSION AND EQUITY............................................................253 Chapter 5 Summary ............................................................................................................. 253 Overview................................................................................................................................. 255 Mainstreaming Equity......................................................................................................... 258 Key Equity Findings............................................................................................................ 259 Choice of Equity Indicators ................................................................................................ 269 Key Equitable Mobility Indicators ......................................................................................... 270 Vehicle Access........................................................................................................................ 279 Captive Travel..................................................................................................................... 279 Intra-Household Vehicle Allocation: Who Gets the Family Car? ...................................... 286 How Much and What For: The Interrelated Effects of Gender and Age on Mobility and Trip Purpose ........................................................................................................................ 294 Background ......................................................................................................................... 296 Methods ............................................................................................................................... 298 Results ................................................................................................................................. 304 Trip Purpose ....................................................................................................................... 307
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Discussion ........................................................................................................................... 315 A Compound Gendered Lens .............................................................................................. 321 Health and Disability .............................................................................................................. 322 Health and Physical Activity among Disabled and Nondisabled Georgians ..................... 324 Mobility Differences between Georgians with and without Mobility Impairments ............ 329 Children with Mobility Impairments................................................................................... 332 Variations among Adults with Mobility Impairments ......................................................... 333 Risk Factors for Immobility among Adults with Mobility Impairments.............................. 342
CHAPTER 6. NONMOTORIZED TRANSPORTATION AND ACCESS/EGRESS TRAVEL .....................................................................................................................................347
Chapter 6 Summary ............................................................................................................. 347 Overview................................................................................................................................. 349
Barriers to Walking and Biking More Frequently .............................................................. 352 Physical Activity.................................................................................................................. 355 Access and Egress Travel ....................................................................................................... 361 Measurement of Access/Egress Travel ............................................................................... 362 NHTS Access/Egress Questions and Instructions............................................................... 366 Access/Egress Travel by Georgia Residents ....................................................................... 367 Travel Day Walking and Biking by Georgia Adults .............................................................. 377 Time of Day ......................................................................................................................... 385 Captive and Choice Nonmotorized Travel ............................................................................. 390 Defining Captive Travel...................................................................................................... 390 Model Structure .................................................................................................................. 392 Results ................................................................................................................................. 394 Children's Nonmotorized and School Travel ......................................................................... 401 Identifying School Trips ...................................................................................................... 401 Children's School Travel .................................................................................................... 406 Children's Nonmotorized Travel for All Purposes ............................................................. 409 Frequency of Nonmotorized Travel by Children ................................................................ 414
CHAPTER 7. TRAVEL FOR ITS OWN SAKE ...................................................................421 Chapter 7 Summary ............................................................................................................. 421 Overview and Methods ........................................................................................................... 421 Challenges of Identifying and Measuring TFIOS ............................................................... 423 Measurement of Loop Trips and TFIOS in the 2017 and 2009 NHTS ............................... 426 Trip Purpose for Loop Trips ............................................................................................... 428 Loop Trips in the 2017 Georgia NHTS Subsample................................................................ 431
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APPENDIX: SAMPLE SIZE TABLES ...................................................................................439 Household and Vehicle Sample Tables .................................................................................. 442 Person Sample Tables ............................................................................................................. 450 Trip, Work Journey, and Access/Egress Travel Sample Tables............................................. 477
ACKNOWLEDGMENTS .........................................................................................................515 REFERENCES ...........................................................................................................................516
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LIST OF FIGURES
Figure 1. Map. Georgia MPO map. ...............................................................................................23 Figure 2. Classification. Urbanicity classifications. ......................................................................24 Figure 3. Stacked bar graph. Person trip purpose by MPO tier .....................................................34 Figure 4. Bar graph. Age of population with mobility impairments. ............................................44 Figure 5. Pie chart. Vehicle ownership among Georgia households. ............................................58 Figure 6. Bar graph. Vehicle sufficiency in Georgia households. .................................................60 Figure 7. Histogram. Distribution of annual household miles driven............................................64 Figure 8. Bar graph. Educational attainment of Georgia workers by gender, MPO tier,
household income, and race. ..................................................................................................91 Figure 9. Histogram. Work journeys by time of arrival to or departure from work
(weighted). ............................................................................................................................103 Figure 10. Stacked bar graph. Weekday trips in progress by time of day (all ages, weighted)...108 Figure 11. Stacked bar graph. Weekday vehicle trips in progress by time of day (weighted). ...109 Figure 12. Pie chart. Proportions of traditional and nontraditional commutes in Georgia. .........112 Figure 13. Diagram. Trip sequence with multiple potential commute patterns...........................115 Figure 14. Diagram. Alternate methods for identifying commutes within complex work
journeys. ...............................................................................................................................117 Figure 15. Diagram. Sequence of trips including a provisional work anchor. ............................122 Figure 16. Diagram. Example work journey classifications of trip sequences............................123 Figure 17. Maps. Example journey with a negative complexity increment. ...............................128 Figure 18. Pie chart. Telework category of Georgia workers and telecommuting frequency,
past 30 days. .........................................................................................................................170 Figure 19. Pie chart. Travel day working and teleworking among Georgia adults. ....................172 Figure 20. Bar graphs. Commute schedules of workers with and without flexible working
times. ....................................................................................................................................192 Figure 21. Histograms. Arrival times at work for service and nonservice workers by
schedule flexibility. ..............................................................................................................195 Figure 22. Histograms. Departure times from work for service and nonservice workers by
schedule flexibility. ..............................................................................................................196 Figure 23. Histograms. Arrival times at work for caregivers by schedule flexibility and
gender. ..................................................................................................................................198 Figure 24. Histograms. Departure times from work for caregivers by schedule flexibility and
gender. ..................................................................................................................................199
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Figure 25. Histograms. Annual miles driven by AFVs, all vehicles, and recent vehicles...........224 Figure 26. Pie chart. Number of online shoppers per household, past 30 days. ..........................243 Figure 27. Pie chart. Household online shopping by household size, past 30 days.....................247 Figure 28. Bar graph. Percent of adults with annual household income less than $25,000, by
race, gender, and mobility impairment.................................................................................257 Figure 29. Histogram. Average marginal effect of being female on probability of being
housebound, by age group (percentage points). ...................................................................306 Figure 30. Histogram. Probability of being housebound by gender and age...............................307 Figure 31. Bar graph. Average marginal effect of gender on probability of making trips of
various purposes on travel day (in percentage points). ........................................................312 Figure 32. Histograms. Average marginal effects of gender by age (in percentage points)........313 Figure 33. 3D histogram. Predicted probability of making a trip to transport others by gender
and age. .................................................................................................................................314 Figure 34. Stacked bar graph. Annual household income of Georgians with and without
mobility impairments............................................................................................................324 Figure 35. Bar graph. Health of nondisabled Georgia adults by income and level of physical
activity in a typical week. .....................................................................................................328 Figure 36. Bar graph. Mobility aids used by adults with mobility impairments. ........................336 Figure 37. Pie chart. Use of nonmotorized modes by Georgia adults (past 7 days). ...................349 Figure 38. Bar graphs. Gender differences in perceived barriers to walking and biking more
frequently among pedestrians and cyclists ages 18+............................................................354 Figure 39. Bar graphs. Weekday and weekend NMT by time of day. ........................................386 Figure 40. Diagram. Example child's travel itinerary with trips to and from school. .................402 Figure 41. Instructions. Excerpt from 2009 NHTS travel diary instructions...............................426 Figure 42. Instructions. Excerpt from 2017 NHTS travel diary instructions...............................427
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LIST OF TABLES
Table 1. NHTS MPO classification by county. .............................................................................21 Table 2. Classification of sample households by MPO tier and urbanicity...................................25 Table 3. Summary demographic statistics. ....................................................................................26 Table 4. Major travel indicators by MPO tier................................................................................27 Table 5. Summary demographic and travel statistics by MPO tier. ..............................................28 Table 6. Summary demographic and travel statistics by MPO......................................................28 Table 7. Summary statistics per household, daily..........................................................................29 Table 8. Summary statistics per capita, daily. ...............................................................................30 Table 9. Trip distance and percent of population immobile. .........................................................31 Table 10. Trip purpose by destination: Types and frequencies. ....................................................33 Table 11. Person trips by mode (detailed). ....................................................................................35 Table 12. Mode share and trip distance by MPO tier. ...................................................................36 Table 13. Annual trips and vehicle miles by Georgians and within Georgia. ...............................38 Table 14. Annual person trips within and between MPO tiers (thousands). .................................40 Table 15. Annual vehicle trips within and between MPO tiers (thousands). ................................40 Table 16. Daily household travel by demographic groups. ...........................................................42 Table 17. Daily per capita trips and immobility (persons ages 5+). ..............................................43 Table 18. Trip distance and daily PMT and VMT per person ages 5+..........................................46 Table 19. Annual person trips by purpose and income..................................................................48 Table 20. Gender and racial differences in annual person trips by purpose. .................................51 Table 21. Frequency of trip purposes by age and disability. .........................................................52 Table 22. Annual tripmaking by purpose, gender, and age. ..........................................................54 Table 23. Trip purpose by mode of travel......................................................................................56 Table 24. Mode of travel by trip purpose. .....................................................................................57 Table 25. Vehicle sufficiency by income, race, and number of household vehicles. ....................61 Table 26. Vehicle purchasing behavior by household income. .....................................................62 Table 27. Characteristics of vehicles owned by households of different income levels. ..............63 Table 28. Miles driven per vehicle by household vehicle sufficiency...........................................65 Table 29. Vehicle characteristics by vehicle age cohort................................................................66 Table 30. Characteristics of seldom-used vehicles. .......................................................................67
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Table 31. Ownership of seldom-used vehicles by annual household income. ..............................68 Table 32. County transit funding and transit usage by MPO.........................................................70 Table 33. County demographics and transit usage by transit funding category. ...........................71 Table 34. Perceived most important factors to make transit a good commute option by
geography, vehicle ownership, and transit access/use............................................................73 Table 35. Transit service quality priorities of Georgia workers by demographic group. ..............74 Table 36. Logistic regression: Transit service quality priorities of Georgia workers. ..................76 Table 37. Workforce participation. ................................................................................................88 Table 38. Job categories of Georgia workers.................................................................................89 Table 39. Job categories of Georgia workers by educational attainment. .....................................92 Table 40. Active commuter rates. .................................................................................................94 Table 41. Work journey complexity among active commuters. ...................................................96 Table 42. Number of work journeys per person, active commuters. .............................................97 Table 43. Usual commute mode as reported by NHTS-defined workers. .....................................99 Table 44. Travel day commute mode(s) for active commuters. ..................................................100 Table 45. Use of single-occupancy vehicles, high-occupancy vehicles (HOVs), and alternate
modes among active commuters. .........................................................................................102 Table 46. Total annual work journeys in Georgia. ......................................................................104 Table 47. Work journeys by MPO tier (all days).........................................................................105 Table 48. Work journeys by MPO tier (weekdays only). ............................................................106 Table 49. Work journey mode, vehicle occupancy status, and time of day by MPO tier
(percentages).........................................................................................................................107 Table 50. Initial anchor classifications based on location and primary activity at location. .......121 Table 51. Duration and purposes of stops in work journeys........................................................125 Table 52. Counterfactual match status of complex work journeys in sample. ............................126 Table 53. Weighted OLS model of work journey complexity increment distance in miles........131 Table 54. Predicted complexity increments.................................................................................132 Table 55. Comparison between full work journey, counterfactual, and last-leg commute
measurements. ......................................................................................................................133 Table 56. Distance to work, commute PMT, and work journey PMT.........................................137 Table 57. Average work journey and commute distance by MPO tier........................................138 Table 58. Percent of statewide work-related miles traveled by MPO tier. ..................................140 Table 59. Annual work-related PMT and VMT by MPO tier (millions of miles).......................141 Table 60. Annual work-related PMT and VMT per worker by MPO tier (miles).......................141
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Table 61. Work travel distance by time of day. ...........................................................................144 Table 62. Work travel distance by time of day and MPO tier. ....................................................145 Table 63. Average work travel distance by demographic and employment characteristics. .......147 Table 64. Average annual work travel distance per worker by demographic and employment
characteristics. ......................................................................................................................148 Table 65. Percent of total work travel distance by demographic and employment
characteristics. ......................................................................................................................149 Table 66. Work travel distance by mode. ....................................................................................151 Table 67. Work-related travel duration in minutes. .....................................................................154 Table 68. Travel day commute and work journey duration by time of day and MPO tier..........155 Table 69. Work travel duration by mode and vehicle ownership. ...............................................157 Table 70. Work travel duration by MPO tier, gender, education, and age. .................................160 Table 71. Work travel duration by income, race, occupation, and worker type. .........................161 Table 72. Total daily commute burden by MPO tier, gender, education, and age. .....................163 Table 73. Total daily commute burden by income, race, occupation, and worker type. .............164 Table 74. Eligibility for job flexibility among Georgia workers. ................................................167 Table 75. Travel day working and teleworking by MPO tier. .....................................................174 Table 76. Travel day teleworking and conventional commuting among active workers. ...........175 Table 77. Employment flexibility by MPO, job characteristics, educational attainment, and
income. .................................................................................................................................177 Table 78. Employment flexibility by sex, caregiver status, age, race, and mobility
impairment. ...........................................................................................................................179 Table 79. Employment flexibility by caregiver status. ................................................................180 Table 80. Employment flexibility for workers with a 4-year college degree by location and
job characteristics. ................................................................................................................182 Table 81. Employment flexibility for workers without a 4-year college degree by location
and job characteristics. .........................................................................................................183 Table 82. Employment flexibility among workers with a 4-year college degree by sex,
caregiver status, age, race, and mobility impairment. ..........................................................184 Table 83. Employment flexibility among workers without a 4-year college degree by sex,
caregiver status, age, race, and mobility impairment. ..........................................................185 Table 84. Logistic regressions: Eligibility for flexible work schedule among Georgia
workers. ................................................................................................................................187 Table 85. Teleworking status of Georgia workers (descriptive statistics)...................................190 Table 86. Multinomial logistic regression: Teleworking status of Georgia workers. ................191
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Table 87. Median work arrival and departure times by schedule flexibility, MPO tier, education, and employment characteristics. .........................................................................194
Table 88. Median work arrival and departure times by schedule flexibility, demographic characteristics, and caregiver status. ....................................................................................197
Table 89. Linear regression: Work arrival and departure times for travel day commutes. .........201 Table 90. Average number of days telecommuted in the past 30 days among eligible workers
by MPO tier, sex, caregiver status, age, race, and medical condition. .................................204 Table 91. Telecommuting 30-day frequency among eligible workers by MPO tier, sex,
caregiver status, age, race, and medical condition. ..............................................................205 Table 92. Average days telecommuted in the past 30 days among eligible workers by
employment characteristics. .................................................................................................206 Table 93. Telecommuting 30-day frequency among eligible workers by employment
characteristics. ......................................................................................................................207 Table 94. Average days telecommuted in the past 30 days among eligible workers by
caregiver status. ....................................................................................................................208 Table 95. Telecommuting 30-day frequency among eligible workers by caregiver status. ........209 Table 96. Distance to work in miles by MPO tier. ......................................................................210 Table 97. Average days telecommuted in the past 30 days among eligible workers by
distance to work and MPO tier. ............................................................................................211 Table 98. Telecommuting 30-day frequency among eligible workers by distance to work and
MPO tier. ..............................................................................................................................212 Table 99. Travel day commuting and telecommuting among workers ages 18+ by MPO tier,
sex, caregiver status, age, race, and medical condition. .......................................................214 Table 100. Travel day commuting and telecommuting among workers ages 18+ by
employment characteristics. .................................................................................................216 Table 101. Travel day commuting and telecommuting among workers ages 18+ by caregiver
status .....................................................................................................................................217 Table 102. Travel day commuting and telecommuting among workers ages 18+ by distance
to work and MPO tier. ..........................................................................................................218 Table 103. Number of alternative-fuel vehicles by MPO tier. ....................................................220 Table 104. Fuel type of alternative- and conventional-fuel vehicles...........................................221 Table 105. Vehicle characteristics: AFVs, all vehicles, and recent vehicles...............................223 Table 106. Annual miles driven by fuel type...............................................................................225 Table 107. Household characteristics for AFVs, all vehicles, and recent vehicles. ....................227 Table 108. Main driver characteristics for AFVs, all vehicles, and recent vehicles....................228 Table 109. Shared mobility questions in the NHTS. ...................................................................230
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Table 110. Demographics of bikeshare and carshare users ages 16+..........................................232 Table 111. Frequency of carshare and bikeshare use among users ages 16+, past 30 days. .......234 Table 112. Ridehailing use among Georgians ages 16+..............................................................237 Table 113. Demographics of ridehailing users (column percentages).........................................238 Table 114. Unweighted sample sizes for ridehailing and vehicle-for-hire trips by Georgians
ages 16+................................................................................................................................239 Table 115. Monthly and annualized estimates of ridehailing and vehicle-for-hire trips by
Georgians ages 16+. .............................................................................................................240 Table 116. Online orders by MPO tier, past 30 days...................................................................241 Table 117. Household online shopping by MPO tier, annual household income, vehicle
ownership, and race, past 30 days. .......................................................................................244 Table 118. Household online shopping by household composition. ...........................................246 Table 119. Online shopping by persons ages 16+, past 30 days. ................................................249 Table 120. Which household members placed online orders in the last 30 days for people
ages 16+................................................................................................................................250 Table 121. Percent of persons ages 16+ who made a trip to buy goods on the travel day. .........251 Table 122. Demographic characteristics by disability and national origin..................................274 Table 123. Demographics of nonimmigrant and immigrant Georgians by level of education....275 Table 124. Key mobility indicators for Georgia adults by geography, race, education,
income, vehicle ownership, gender, and disability...............................................................276 Table 125. Mobility indicators by national origin and educational attainment among Georgia
adults.....................................................................................................................................277 Table 126. Driver status and vehicle ownership by Georgia adults of different demographic
characteristics. ......................................................................................................................278 Table 127. Purpose by mode for captive, choice, and POV trips (weighted percent and
unweighted sample sizes). ....................................................................................................281 Table 128. Mean duration and distance of captive, choice, and POV trips. ................................282 Table 129. Linear regressions: Effect of captive travel on duration of transit and
nonmotorized trips................................................................................................................283 Table 130. Linear regression: Duration in minutes of trips by POV and transit. ........................285 Table 131. Weighted sample characteristics of drivers by household vehicle ownership...........287 Table 132. Unweighted sample characteristics of drivers by household vehicle ownership.......288 Table 133. Percent of drivers listed as the "main" driver for a household vehicle......................290 Table 134. Logistic regression: Vehicle allocation within vehicle-deficit households. ..............291 Table 135. Percent of drivers who are listed as main driver for newest household vehicle. .......292
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Table 136. Logistic regression: Intra-household allocation of new vehicles...............................293 Table 137. Percent of teen drivers who are the main driver for household vehicles, by sex.......294 Table 138. Sample characteristics................................................................................................299 Table 139. Annual trips per capita by purpose, gender, and age (weighted)...............................302 Table 140. Binomial probit model of likelihood of being chronically housebound (having
made no trips within the past 7 days). ..................................................................................305 Table 141. Multivariate probit model of likelihood of making trips for various purposes. ........308 Table 142. Summary statistics and indicators for multivariate probit model. .............................310 Table 143. Health and age of Georgia adults with and without mobility impairments. ..............325 Table 144. Driver status and income of Georgians with and without mobility impairments,
by health and age. .................................................................................................................326 Table 145. Georgians in poor or fair health by income, age, and disability. ...............................327 Table 146. Walking and physical activity among Georgians with and without mobility
impairments, by health and age. ...........................................................................................330 Table 147. Immobility by disability, health, and age. .................................................................331 Table 148. Average daily trips by disability, health, and age......................................................331 Table 149. Behavioral adaptations among disabled and older adults. .........................................332 Table 150. Comparison of children (ages 517) with and without mobility impairments. .........333 Table 151. Demographic characteristics of Georgians with mobility impairments. ...................335 Table 152. Driver status and income of Georgia adults with mobility impairments. ..................339 Table 153. Immobility and average daily trips of Georgians with mobility impairments...........340 Table 154. Walking and physical activity among Georgians with mobility impairments...........341 Table 155. Logistic regression: Immobility among Georgia adults with a mobility
impairment. ...........................................................................................................................344 Table 156. Selected average marginal effects on probability of being immobile among adults
with a mobility impairment, percentage points. ...................................................................345 Table 157. Walking and biking among Georgia adults (past 7 days). .........................................351 Table 158. Perceived barriers to walking and biking more frequently (pedestrians and
cyclists ages 18+). ................................................................................................................353 Table 159. Physical activity among Georgia adults.....................................................................356 Table 160. Usual physical activity of Georgia adults by walking and biking behavior (past
7 days). .................................................................................................................................359 Table 161. Total annual trips and access/egress legs by nonmotorized modes. ..........................361 Table 162. Mode share of walking and biking trips and legs. .....................................................363 Table 163. Instances of walking and biking per person ages 5+. ................................................364
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Table 164. Access and egress legs by mode and parent mode (unweighted). .............................365 Table 165. Mode share of legs to access/egress public transit and other modes. ........................368 Table 166. Mean duration in minutes of access/egress legs by mode. ........................................369 Table 167. Mode share of public transit access/egress legs by demographic characteristics
(row percentages). ................................................................................................................370 Table 168. Unweighted sample sizes of public transit access/egress legs...................................373 Table 169. Duration in minutes of public transit access/egress legs by demographic
characteristics. ......................................................................................................................376 Table 170. Nonmotorized trips/legs per capita and duration of NMT trips/legs. ........................378 Table 171. Mode and purpose of NMT trips/legs by demographic factors. ................................381 Table 172. Mean duration in minutes of leisure and instrumental NMT trips/legs by Georgia
adults.....................................................................................................................................383 Table 173. Purpose and duration of NMT trips/legs....................................................................384 Table 174. Unweighted trips/legs by mode and purpose. ............................................................385 Table 175. Weighted NMT trips/legs by time of day ..................................................................387 Table 176. Unweighted NMT trips/legs by time of day. .............................................................388 Table 177. Time of day of NMT trips/legs by day and MPO tier. ..............................................389 Table 178. Vehicle sufficiency of Georgia households and adults..............................................392 Table 179. Duration, quantity, and purpose of nonmotorized travel for captive and choice
travelers. ...............................................................................................................................395 Table 180. Linear regression: Daily total minutes of instrumental walking and biking..............398 Table 181. Linear regression: Daily total minutes of leisure walking and biking. .....................399 Table 182. Mode of travel-day trips to and from school by children ages 517 (unweighted)...405 Table 183. Usual mode of travel to and from school by children ages 517 (unweighted). .......405 Table 184. School travel: Mode split of trips and usual mode(s). ...............................................406 Table 185. Walking and biking as percentage of school travel by geography, age, driver
status, sex, race, income, and household vehicles. ...............................................................408 Table 186. Purpose of children's nonmotorized trips (weighted and unweighted). ....................411 Table 187. Purpose of children's nonmotorized trips by geography, age, driver status, sex,
race, income, and household vehicle ownership. .................................................................413 Table 188. Nonmotorized travel of children ages 517 (past 7 days). ........................................415 Table 189. Percentage of children who have walked or biked in the past 7 days and average
number of trips among mode users. .....................................................................................417 Table 190. Nonmotorized travel on the travel day: Percent of trips/legs and percent of
children. ................................................................................................................................418
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Table 191. Percent of children who walked/biked on the travel day and minutes of nonmotorized travel..............................................................................................................419
Table 192. Destination purpose of loop trips made by Georgians ages 5+ (unweighted). ..........429 Table 193. Loop trips as a percent of total trips by mode and purpose (Georgians ages 5+)......432 Table 194. Loop trips per capita in 2017 (ages 5+). ....................................................................433 Table 195. Mode and purpose of loop trips by Georgians ages 5+. ............................................434 Table 196. Mode, purpose, distance, and duration of loop and nonloop trips.............................436 Table 197. Distance and duration of loop and nonloop trips for fitness and recreation. .............438 Table 198. Overview of Georgia NHTS population types and samples used in this report. .......441 Table 199. Overview of household and vehicle subsamples. ......................................................442 Table 200. Household sample table. ............................................................................................443 Table 201. Sample table: Vehicles owned by Georgia households. ............................................447 Table 202. Overview of person subsamples. ...............................................................................450 Table 203. Person sample table: All persons ages 5+..................................................................451 Table 204. Person sample table: All adults ages 18+. .................................................................457 Table 205. Person sample table: All workers ages 18+ (demographic characteristics)...............463 Table 206. Person sample table: All workers ages 18+ (job and commute characteristics)........469 Table 207. Person sample table: Active commuters ages 18+. ...................................................471 Table 208. Person sample table: Persons ages 16+. ....................................................................473 Table 209. Person sample table: Workers ages 16+. ...................................................................475 Table 210. Summary of traveler populations and instances of travel..........................................478 Table 211. Overview of trip, work journey, and access/egress travel subsamples......................479 Table 212. Trip sample table: All trips by all persons ages 5+ by location.................................480 Table 213. Trip sample table: All trips by all persons ages 5+ by purpose.................................482 Table 214. Trip sample table: All trips by all persons ages 5+ by mode.....................................483 Table 215. Trip sample table: All trips by all persons ages 5+ by demographic factors.............484 Table 216. Trip sample table: All vehicle trips by all persons ages 5+ by location. ...................488 Table 217. Trip sample table: All vehicle trips by all persons ages 5+ by purpose. ...................490 Table 218. Trip sample table: All vehicle trips by all persons ages 5+ by demographic
factors. ..................................................................................................................................491 Table 219. Trip sample table: All trips by all persons ages 18+ by location...............................496 Table 220. Trip sample table: All trips by all persons ages 18+ by purpose...............................498 Table 221. Trip sample table: All trips by all persons ages 18+ by mode...................................499
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Table 222. Trip sample table: All trips by all persons ages 18+ by demographic factors...........500 Table 223. Trip sample table: All vehicle trips by all persons ages 18+ by location. .................504 Table 224. Trip sample table: All vehicle trips by all persons ages 18+ by purpose. .................506 Table 225. Trip sample table: All vehicle trips by all persons ages 18+ by demographic
factors. ..................................................................................................................................507 Table 226. Work journey/commute sample table: Location, distance, and mode. ......................511 Table 227. Work journey/commute sample table: Stops, driving, and time of day. ...................512 Table 228. Work journey/commute sample table: Traveler characteristics. ...............................513
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EXECUTIVE SUMMARY
Travel in Georgia is changing. Atlanta is growing, changing the balances of trips across the state. Advanced technologies are generating a number of new products and services with direct implications for travel demand in Georgia, including ridehailing services (e.g., Uber and Lyft), vehicle sharing (e.g., carsharing services such as Zipcar, bikesharing, and, more recently, electric scooters), and alternative-fuel vehicles (e.g., hybrid and electric cars). Working from home almost doubled between 2000 and 2010, and even before the coronavirus disease 2019 (COVID-19) pandemic, online shopping had become an increasingly significant part of Georgians' retail behavior. These new options are transforming individuals' and households' travel-related decision-making. They will substantially modify the demand for housing, vehicle sales, the amount of travel by private vehicles, and the resulting gasoline tax revenues and emissions of greenhouse gases and criteria pollutants.
The impacts of these changes--positive and negative--do not affect all Georgians equally. There is a risk of exacerbating existing inequality, locking Georgia into a two-tiered travel system-- literally and figuratively leaving people behind. To ensure a more equitable future, it is imperative to examine the needs of those who, through age, disability, economic disadvantage, gender, or race, are restricted in their mobility and access to opportunities. Further, climate change poses risks to Georgians' health and economic wellbeing. The state is preparing to invest more than $1 billion to mitigate the effects of sea level rise.1 Additionally, 48 percent of Georgia's population currently lives in areas at elevated risk of wildfires, which are expected to
1 See https://sealevelrise.org/states/georgia/.
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increase in frequency and intensity as elevated temperatures and droughts become more prevalent.2 It is, therefore, more urgent than ever to identify and enact sustainable transport solutions.
Travel demand forecasting models and transportation policy need to be updated to account for the latest trends, improving their accuracy and equity. This report provides a baseline and guidance for such work through extensive analysis of the Georgia add-on to the 20162017 National Household Travel Survey (NHTS).
ABOUT THIS REPORT This report provides an in-depth snapshot of the travel behavior of Georgians of all ages. It documents differences in travel needs and behavior by region and between demographic groups, focuses on measurement challenges and improved techniques, and identifies areas where future data collection is needed. More in-depth summaries and a few key findings are included at the beginning of each individual chapter; a brief synopsis of the key messages is provided in Key Messages of this executive summary.
Chapter 1 presents greater detail about the NHTS data and methods, provides an overview of general travel patterns in Georgia, and synthesizes findings about regional differences from throughout the report.
Chapter 2 focuses on work travel, including more accurate measurement of complex commutes (i.e., commutes including one or more stops between home and work). One in every four
2 See https://statesatrisk.org/georgia.
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Georgia commutes is complex. The common practice of using the last trip in the chain as a proxy for commute distance undercounts Georgia's annual commute person miles traveled (PMT) by 2.6 billion miles, about 10 percent of total commute PMT. This chapter presents a new, more accurate method of estimating commute distance.
Chapter 3 explores flexible work locations (i.e., teleworking) and schedules. It examines which workers' jobs allow for flexible time and/or location and how often workers take advantage of that flexibility.
Chapter 4 discusses new technologies and services, including alternative-fuel vehicles, shared mobility, and online shopping.
Chapter 5 focuses on social inclusion and equity. The chapter documents the mobility disadvantages among captive mode users and people with mobility impairments; examines the interrelated effects of gender and age on travel behavior; explores how vehicles are allocated within households; and synthesizes equity findings from throughout the report.
Chapter 6 provides a portrait of walking and biking in Georgia. Access and egress travel (i.e., travel to reach another mode of transportation such as public transit) account for a substantial portion of nonmotorized travel (NMT); thus, the chapter discusses how to incorporate this travel into the analysis. It also discusses nonmotorized travel by children and children's school travel by all modes.
Chapter 7 discusses travel for its own sake, or the intrinsic value of travel, beyond its utilitarian purpose of getting from A to B. Loop trips (i.e., trips with the same start and end location) are an easily identifiable form of travel for its own sake. This chapter discusses changes in the NHTS's
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methods of soliciting information about loop trips; provides an overview of the frequency, mode, and purposes of loop trips; and reviews continued measuring challenges.
KEY MESSAGES
Accounting for complexity is critical. Georgians' travel is often composed of chains rather than individual trips. For example, one in four Georgia commutes is complex (i.e., including one or more stops between home and work), making it important not to underestimate the full extent of commute travel. As teleworking rises, some workers are skipping the commute to work; others are still commuting to the office for part of the day and working an additional shift when they get home. The modes used to access public transit are not always included in mode share figures, which undercounts modes like walking and, to a lesser extent, biking. Decisions about whether, where, and how to travel vary by time of day and day of the week. Accurate measurement and modeling of Georgians' travel habits needs to reflect these sources of complexity.
New technologies and services are reshaping Georgians' travel. Less than a decade after the founding of Uber, 1 in 10 Georgians used a ridehailing app at least once in the past 30 days. Ridehailing apps accounted for 87 percent of vehicle-for-hire trips in Georgia, even more in small MPO areas and rural areas. Two thirds of Georgia households had likewise purchased something online within the past 30 days, ranging from 72 percent of Atlanta-region households to 52 percent of households in rural areas. Statewide, 1.9 percent of Georgia's vehicles were hybrid, electric, or powered by another alternative fuel. High-speed internet has facilitated the rise of teleworking. These trends have likely continued or accelerated in the years since the NHTS data were collected.
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Georgians' mobility and travel habits are bifurcated. Residents of the Atlanta region are more mobile than the national average, while residents of smaller metropolitan planning organization (MPO) areas and rural counties are less mobile. Emerging trends such as telecommuting, flexible work scheduling, alternative-fuel vehicles, and online shopping are more pronounced in the Atlanta region compared to the rest of the state. Ridehailing is also more common in Atlanta but has promise for improving mobility in small towns and rural communities. In addition to these geographic differences, there are wide gaps between wealthy and poor Georgians. Transportation-disadvantaged populations such as the elderly and Georgians with mobility impairments, especially those from poor households, are often stuck at home.
Three in ten Georgians do not have full vehicle access. Five percent of Georgians live in households with zero vehicles. An additional 26 percent live in vehicle-deficit households (i.e., households with at least one vehicle but fewer vehicles than potential drivers). On average, Georgians in vehicle-deficit households are more mobile than Georgians from zero-vehicle households, but household members who do not have access to the family car face many of the same barriers to mobility as travelers from carless households.
Captive travelers pay a double penalty. Georgia's current travel environment constitutes a two-tiered system divided not just by mode, but by the ability to choose between modes. The lowest-income Georgians (<$15,000 annual household income) and Georgians who live in vehicle-deficit households overall walk, bike, and take transit more than their wealthier counterparts. These captive travelers pay a double time penalty. The first penalty is not having the option to drive, which is the fastest mode in many cases. The second penalty is that the trips
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of captive transit users are also longer than those of choice transit users. The same is true of captive pedestrians and cyclists.
Low-income Georgians pay more for less. Low-income Georgians are less mobile than their wealthier counterparts. Only 43 percent of the lowest-income households (those making less than $15,000 per year) are vehicle sufficient, and nearly one third own no vehicles at all. These households purchase older vehicles with an average of 130,000 miles on the odometer at the time of purchase (see chapter 1). Vehicles near the end of their useful lives are financially more accessible to low-income households due to their lower purchase costs. However, these vehicles cost more to maintain and need to be replaced more frequently. Technological improvements in vehicle efficiency are disproportionately benefitting wealthy households.
Walking and biking are easy to undercount. In a typical week, 72.6 percent of Georgians will walk, ride a bike, or both, but this nonmotorized travel can be hidden in the data. In addition to the 950,000 nonmotorized trips Georgians make each year, they walk or bike as a way to access/egress another mode of transportation (e.g., public transit) 260,000 times per year. However, this access/egress travel is considered a part of the mode being accessed and is not included in typical mode share calculations. Incorporating these access/egress legs provides a more complete picture of Georgians' walking and biking.
Accuracy and equity go hand in hand. Many of the measurement issues identified in this report disproportionately affect the accuracy of the data for one or more marginalized populations. For example, since more women than men make complex commutes, better accounting for complex commutes can: (1) better capture the full extent of work travel in Georgia by identifying 2.6 billion PMT that would not have been included, and (2) improve
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measurement of women's travel patterns and avoid underestimating their commute distances. Similarly, incorporating transit access and egress trips into estimates of nonmotorized travel improves estimates of walking and biking across the board, but particularly for transit-dependent Georgians.
CHALLENGES AND RECOMMENDATIONS
Leveraging NHTS Data to Understand Complex Travel Patterns This report provides analysis and examples of how to effectively leverage NHTS data to more accurately analyze complex travel patterns, as follows:
The NHTS contains a rich array of data about Georgians' backgrounds and usual travel habits, as well as a snapshot of their travel. Chapter 1, Methodological Notes provides future analysts with an orientation to NHTS data.
There is no one "typical" traveler. To understand Georgians' travel behavior, it is necessary to account for diverse needs and experiences. Disaggregating data is vital for uncovering regional variations and demographic differences in mobility.
For many topics, the NHTS contains data about "usual" practices and observed travel on the travel day. Comparing the two can identify nuances. For instance, nonmotorized travel is the "usual" commute mode of just 2.3 percent of Georgians, but 3.7 percent of observed commute trips were by NMT, suggesting that a number of Georgians who usually drive to work sometimes walk or bike (see chapter 1, Household and Personal Mobility). When commuters who walk or bike to public transit are included, the total amount of walking and biking is higher than the initial figure would suggest.
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In addition to the substantive analysis, the techniques presented here, such as our methods of identifying and measuring work travel (see chapter 2, Defining the Commute), school travel (see chapter 6, Identifying School Trips) or more completely documenting NMT (see chapter 6, Access and Egress Travel), are designed to be useful to future analysts.
Where appropriate, the report also discusses the limitations of NHTS data. Identifying Data Needs at the end of this section discusses future data needs.
Mainstreaming Equity Many forms of social inequality and exclusion affect Georgians' mobility. Low-income people, older adults, and people with disabilities face the strongest barriers; differences by gender and race were also documented. While these problems come from outside the transportation system, if pre-existing inequalities are not taken into account, transportation professionals can inadvertently exacerbate them, leaving disadvantaged communities even farther behind. To prevent this, transportation agencies should mainstream equity by evaluating differential needs or policy outcomes by gender, race, disability, and other sources of social exclusion as a standard part of planning. One critical first step is disaggregating data to ensure that differences between groups are detected. A valuable approach is to partner with organizations outside of the transportation sector, such as senior services centers, healthcare providers, and community groups, for studying and/or implementing projects to help underserved groups.
Assisting Georgians with Mobility Impairments Georgians with mobility impairments have a critical need for transportation. Many people with mobility impairments are impoverished. A mobility impairment does not disqualify someone
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from employment. However, it is worth noting that for some Georgians with disabilities, the biggest obstacle to getting their foot in the door to a new career might be getting their foot to the door (see chapter 1, Trip Purpose).
Current paratransit services are inadequate; only 10 percent of adults with disabilities reported using them, while 70 percent reported reducing day-to-day travel. An assessment of the challenges facing local paratransit systems and the experiences of paratransit users is needed. In addition to improving paratransit service, complementary alternatives such as ridehailing should be explored. Fulton County, for example, recently began offering subsidized ridehailing for elderly residents (see chapter 5, Discussion).
Children with mobility impairments may be especially vulnerable. However, the NHTS sample of children with mobility impairments is small; more targeted data collection and study are needed (see chapter 5, Children with Mobility Impairments).
Achieving Sustainable Mobility for Low-income, Carless, and Vehicle-deficit Georgians The mobility of low-income, carless, and vehicle-deficit Georgians can be improved by improving the level of service for transit and nonmotorized trips. Ridehailing and car-, bike-, and scooter-sharing services can also help carless people, provided they are affordable and technologically accessible. Community carpooling and microtransit could provide more flexible transportation to Georgians without cars.
Helping low-income Georgians acquire cars could have consequences for sustainability and congestion, but automobile access improves mobility, quality of life, and economic
9

opportunities. To maximize the social and environmental benefits of new vehicle technology, policies are needed to help low-income families purchase fuel-efficient and electric vehicles.
Promoting Transit, Walking, and Biking by Addressing Georgians' Concerns The NHTS provides data on Georgians' preferences for transit service (see chapter 1, Transit Service Preferences Among Workers) and perceived barriers to walking and biking more frequently (see chapter 6, Barriers to Walking and Biking More Frequently). These data provide actionable information by identifying high-priority issues. Captive and choice travelers sometimes have different service priorities; it is important to balance the needs of both groups.
Supporting Captive Transit Users Whatever the causes, transit-dependent travelers are experiencing a worse quality of service for commutes and other trips than their wealthier neighbors. To reduce this inequality, it is therefore important to examine potential discrepancies in vehicle frequency and route density, and to examine whether current transit routings match the needs of low-income commuters (see chapter 5, Key Equitable Mobility Indicators).
Improving Pedestrian and Cyclist Safety in Every Neighborhood Unsafe pedestrian and cyclist environments discourage travelers with a choice from walking and biking, and leave captive pedestrians and bicyclists to walk in unsafe environments. Improving the quality of the walking and biking environments encourages more Georgians to choose NMT (see chapter 6, Barriers to Walking and Biking More Frequently). Captive pedestrians and cyclists, who are already walking and biking, need safe infrastructure on the routes they are already using to access their homes, work, shops, schools, and transit stations (see chapter 5, Key Equitable Mobility Indicators, and chapter 6, Captive and Choice Nonmotorized Travel).
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Walking is also an important mode of transportation for people with mobility impairments, making safety and accessible road design even more important (see chapter 6, Travel Day Walking and Biking by Georgia Adults).
Leveraging New Technologies and Services to Make Transportation More Effective and Equitable Teleworking, ridehailing, and online shopping are most common among the most mobile and tech-savvy Georgians, centered in the Atlanta region. Room for growth exists with other segments of the state's population. Teleworking, for example, could benefit workers in rural locations and facilitate employment for Georgians with disabilities. Ridehailing services can improve transportation options in rural areas and increase the mobility of disadvantaged groups. Online shopping can provide homebound or busy low-income Georgians better access to goods at an affordable price. Facilitating internet access and technological literacy is key to all three of these trends. Education on using ridehailing apps and smartphones in general is particularly valuable for older adults (see chapter 5, Discussion).
Measuring Work Travel One in four Georgia commutes is complex (i.e., including one or more stops between home and work). The common practice of using the last trip in the commute as a proxy for commute distance undercounts Georgia's annual commute PMT by 2.6 billion miles, about 10 percent of the total commute PMT. This report presents a new, more accurate method of estimating commute distance and correctly identifying trip anchors based on a combination of purpose and location (see chapter 2, Defining the Commute). It also provides data on the time of day of work journeys and how work and nonwork travel interact over the course of a day (see chapter 2, Overview of Work Journeys). Commute distances also vary by time of day. Finally, there are
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differences in the employment characteristics of Atlanta, smaller MPO regions, and rural counties; incorporating employment data can inform our understanding of work travel (see chapter 2, Overview of Work Journeys). Data on telework and mixed telework can also inform projections of travel demand (see chapter 3, The Effects of Flexible Scheduling).
Identifying Data Needs Some of the topics covered by the NHTS change more rapidly than others. Given technological changes and the COVID-19 pandemic, more recent data on alternative-fuel vehicles, teleworking, and online shopping would be beneficial. While the NHTS is a strong data source overall, some topics are less robust in the number or format of questions. For example, telecommuting is undermeasured (see chapter 3, Definitions and Technical Notes). The NHTS measurements of physical activity are flawed (see chapter 6, Physical Activity) and while the measurement of loop trips is significantly improved from the 2009 NHTS, the techniques for recording the purposes of loop trips and other forms of travel for its own sake (TFIOS) need improvement (see chapter 7, Challenges of Identifying and Measuring TFIOS). The occupation categories used by the NHTS are overly broad, limiting their predictive ability for modeling work travel. While educational attainment can be used as a partial proxy to subdivide the categories, a more detailed set of categories would be useful for predicting work travel locations and likely schedules. Finally, the 2017 NHTS does not disaggregate ridehailing data from other types of vehicles for hire (i.e., taxis and limos). While this report provides a tentative estimate for what percentage of vehicle-for-hire trips are made through a ridehailing app (see chapter 4, Ridehailing and Vehicle-for-Hire Trips), more direct data would be valuable. Additionally, new mobility services, such as shared scooters, entered the market after the completion of NHTS data collection.
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CHAPTER 1. KEY TRAVEL PATTERNS IN THE STATE OF GEORGIA
CHAPTER 1 SUMMARY
This chapter uses analysis of the 2017 National Household Travel Survey (NHTS) to provide an overview of travel patterns in the state of Georgia. It is intended to serve as a reference text. Detailed statistical tables are accompanied by text that provides context and draws attention to notable findings and patterns. Analysis is presented at the state level, and disaggregated by factors such as size of community, sociodemographic characteristics, and vehicle ownership. The chapter is organized in the following sections:
Methodological Notes provides the methodological background on the NHTS itself, and notes on how the data have been processed for this report. It discusses the integration of the regular NHTS and the add-on sample commissioned by the Georgia Department of Transportation (GDOT) and instances where the analytic categories used for this report vary from the default categories provided as part of the NHTS's public use dataset. The section also discusses the geographic divisions used for analysis.
Overview provides summary statistics at the state level, comparisons with national patterns, and regional differences within the state. Georgians' travel habits are bifurcated. Residents of the Atlanta MPO are more mobile than the national average, while residents of smaller MPOs and rural counties are less mobile.
While most of this paper reports travel by residents of Georgia or of MPOs within Georgia, Trip Patterns by Location of Travel examines travel that occurs within
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Georgia, regardless of the residence of the traveler. Most travel is local; 75 percent of trips stay within a single county, and 95 percent stay within a single MPO. Inter-MPO trips are generally balanced. In other words, the number of trips from A to B is comparable to the number of trips from B to A. However, this does not take time of day into account; there are certainly temporal imbalances, as would be expected on the basis of directional commute and other flows. Household and Personal Mobility examines differences in household and personal mobility by gender, race, age, income, and disability. Elderly Georgians and Georgians with disabilities have strongly reduced mobility compared to their neighbors. Lowincome residents also make fewer trips than other Georgians. Trip Purpose examines trip purpose across different demographic groups and modes. Why people travel gives insight into what utility they derive from their trips; trip purpose can illuminate patterns that may be obscured by raw trip numbers. For example, men who have reached retirement age make somewhat fewer trips than younger adults, but make more discretionary trips. Their overall lower mobility is accompanied by more trips dedicated to personal fulfillment. Senior women do not seem to enjoy this benefit. In fact, the age-related reduction in mobility falls heavily on senior and elderly women. Gendered differences are strong throughout all age groups. Adult and teenage women, and even female children, devote more of their travel to household-serving trips than do their male counterparts.
Income also affects trip purpose. Moderate-income households are more mobile than lower-income households, but make more trips to transport someone else than any other group; this suggests moderate-income households' increased mobility over lower-income
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groups may yield a smaller dividend in terms of utility than would be expected from the raw trip numbers, person miles traveled (PMT), or vehicle miles traveled (VMT).
Vehicle Availability and Usage discusses household vehicle ownership. Vehicle ownership differs substantially by household income, not only in terms of the number of household vehicles, but also in terms of the quality. Low-income vehicle owners typically purchase older, high-mileage vehicles that must be replaced more frequently. The section also discusses differences in the quantity of driving between households that are vehiclesufficient and those that have fewer vehicles than potential drivers.
Transit Preferences and Use analyzes data about Georgia workers' preferences for transit service using a question included in the add-on commissioned by GDOT.
Finally, Summary of Findings on Geographical Differences synthesizes findings on regional differences from throughout the report.
METHODOLOGICAL NOTES
This report is based on analysis of the Georgia sample from the NHTS, including 8,005 households from the add-on sample commissioned by GDOT and 606 households that were obtained as part of the national sampling design. Households in both subsamples were asked the same survey questions, and survey weights provided by NHTS were calibrated to include both subsamples. Therefore, this report pools the national and add-on subsamples.
Because of the add-on module, GDOT also has access to more detailed and disaggregated variables. The dataset used for this report integrates these confidential variables with the updated public use dataset (version 1.1). Extensive technical documentation of NHTS itself can be found
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on the official website.3 This section will give additional information on the integration of the two datasets, as well as other technical matters. Readers more interested in contents than methodology may wish to proceed to Overview in this chapter.
Weighting NHTS uses expansion weights to allow for the production of representative national estimates. Because of the add-on, the publicly available weights applied to Georgia respondents have also been made to produce representative estimates at the state level--something that is not guaranteed for states that did not commission an add-on. Weights are not guaranteed to be representative for small geographical entities or subpopulations. As suggested in the 2017 NHTS Weighting Report (Roth, Dai, and DeMatteis 2017), this report is cautious with analysis of small geographies and notes when a sample size may be too small for a reliable estimate.
Unless otherwise noted, all results and statistics are weighted using 7-day weights provided by NHTS. Person weights are used for persons and trips4 and household weights are used for households and vehicles.5 This approach is used in the summary of travel trends commissioned by NHTS itself (McGuckin and Fucci 2018). That report was used as a methodological reference for the present document and is drawn upon for some national comparisons. Unweighted sample tables can be found in the appendix.
3 See https://nhts.ornl.gov/documentation. 4 NHTS-provided trip weights are an annualized version of the person-weights. For normalized figures such as trips per household, the numerator and denominator are each calculated using the appropriate weight (in this case, person weights for trips and household weights for households). 5 When household-level variables are applied to people, person-weights are used. For example, 22.4 percent of Georgia households have an annual income of at least $100,000 (household weights), but those households contain 25.9 percent of Georgia's population (person weights).
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Adjustments to Data This project integrates the confidential dataset provided to GDOT in 2017 with updates to the public-use dataset released in August 2018.6 Responses of "Other, specify" from the confidential dataset were used to update public variables such as race, homeownership, mode, and internet capability to match NHTS's categories. For example, an "other" response of "iPhone" to a question about what device is used to access the internet would be recoded as "smart phone." Where gender, race, or age are missing, NHTS's imputed values were used.
Race was more comprehensively recoded, as follows:
To reflect Georgia's racial makeup, the default racial categories are: (1) white nonHispanic, (2) Black and Black multiracial (including Black-Hispanic), and (3) other. For consistency, respondents listed as "multiracial" in the public dataset were reclassified based on their more detailed responses, available in the private dataset.
"Other, specify" responses were used to recategorize respondents who did not identify with any of the census-style categories used in the NHTS. For example, a number of Latino respondents selected only "other" and wrote "Latino" or their nationality.7 Others listed countries of origin that they were not sure how to classify, for example, in southwest Asia.
6 Details of what modifications were made from version 1.0 of the dataset are available in the Version 1.1 Release Notes at https://nhts.ornl.gov/documentation.
7 Some disagreement exists over whether to treat Hispanic as an ethnicity or Latino as a race. This report adopts the latter approach. The NHTS questionnaire did not include Latino as an option for race. Accordingly, the write-in "Latino" responses were grouped with respondents who selected white-Hispanic into the created racial category of Latino only. In the three-category definition used for race in this report's analysis, Latinos are included under (3) other.
17

Incorrect or extraneous "other" responses were recoded. For example, a person who selected "white" and wrote "Irish" under "other" is classified by NHTS as multiracial. Cases like this were reclassified in this report as white.
Assorted Definitions and Notes NHTS collects trip information only about respondents ages 5 years and older. Following McGuckin and Fucci (2018), reported per capita results are more specifically per person ages 5 years and up, unless otherwise noted. NHTS does not ask about drivers' licensing; rather, they ask "do you/does this person drive?" This creates some ambiguity as to how a respondent might classify a teenager with a learner's permit; 56 respondents under the age of 16 were listed as drivers. Unless otherwise stated, drivers is defined to mean driver of legal driving age (16+).8 At a household level, NHTS defines race based on the race of the person who was the first respondent to the survey. Since 7 percent of households sampled are multiracial, this approach has obvious limitations. This report defines a household's race based on its overall composition, typically dividing between all-white, non-Hispanic households and households with one or more nonwhite and/or Latino members.
Key Terms Though terms such as person miles traveled and vehicle trips are commonly used, a brief reminder may be helpful for some readers:
8 The exception is that, to match the methods used to calculate national descriptive statistics by McGuckin and Fucci (2018), drivers of all ages were used to calculate the totals presented in chapter 2, Overview.
18

Trip: A single trip from one origin to one destination, no matter how brief the amount of time spent there. For example, if someone stops for coffee on the way to work, the journey to work would contain two trips. Two types of trips are considered.
Person trip: A trip made by a person. Vehicle trip: A trip made by a personal occupancy vehicle (POV). More precisely, the
trip is measured as the trip made by the driver of that vehicle. If a father drives his daughter to school, that action would be counted as one vehicle trip (just the father) and two person trips (both the father and the daughter). Vehicle miles traveled (VMT): Similar to vehicle trip, this measure of distance is calculated based just on the distance traveled by the driver of a POV for a trip that occurred in a POV. Person miles traveled (PMT): Miles traveled by a person, regardless of mode or driver status. In the previous example, if the daughter's school was 1 mile away from home, the trip would generate 2 PMT and 1 VMT. A crucial difference is that PMT also captures travel by other modes, such as transit, walking, and biking. However, the method used by NHTS to calculate trip distance, namely the shortest network distance, is likely to understate the mileage of nonmotorized modes, especially cycling, where travelers are likely to choose a more circuitous route. Work journey (WJ): All the trips and intermediate stops a commuter makes between home and work. A work journey is unidirectional (either from home to work, or from work to home). WJs can be simple (proceeding directly between home and work) or complex (containing at least one internal stop).
19

Geographic Divisions for Analysis In addition to statewide figures, this report examines differential patterns between different metropolitan planning organizations (MPOs) and kinds of MPOs within Georgia. Counties are classified into four MPO tiers based on size:
Tier 1: Counties within the Atlanta MPO. Tier 2: Counties within MPOs other than Atlanta, with an MPO population over 200,000
(based on American Community Survey [ACS] 2015 5-year estimates). These include Georgia counties that are part of the Tennessee-based Chattanooga MPO. Tier 3: Counties within MPOs with population < 200,000. Tier 4: Counties that are not in any MPO.
For analytical purposes, MPO boundaries are defined at the county level, following the practice used for GDOT Research Project 16-31, Impact of Emerging Technologies and Trends on Travel Demand in Georgia (Kim, Mokhtarian, and Circella 2019). All households in a county that is partially within an MPO are considered MPO households. Table 1 shows the full classification for each county, and figure 1 shows a map of MPO boundaries across county lines.
The sample size for most individual counties is too small to allow for a county-level breakdown of results; several counties are represented by fewer than 10 households in the survey sample (see the appendix). Most of the comparative geographic analysis in this report focuses instead on types of counties, using the MPO tier system.
20

Table 1. NHTS MPO classification by county.

MPO

MPO

ID MPO Name Tier

County Name

1 Albany

3

1 Albany

3

2 Athens

2

2 Athens

2

2 Athens

2

2 Athens

2

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

3 Atlanta

1

Table continues on next page.

Dougherty Lee Clarke Madison Oconee Oglethorpe Barrow Carroll Cherokee Clayton Cobb Coweta DeKalb Douglas Fayette Forsyth Fulton Gwinnett Henry Newton Paulding Rockdale Spalding

County Portion in

FIPS

MPO

NHTS Sample Size, unweighted
households

13095

Total

168

13177

Partial

61

13059

Total

318

13195

Partial

63

13219

Partial

90

13221

Partial

32

13013

Partial

26

13045

Partial

46

13057

Total

119

13063

Total

97

13067

Total

378

13077

Total

71

13089

Total

406

13097

Total

59

13113

Total

65

13117

Total

98

13121

Total

536

13135

Total

369

13151

Total

74

13217

Partial

33

13223

Total

53

13247

Total

36

13255

Partial

34

21

Continued from previous page.

MPO

MPO

ID MPO Name Tier

Table 1. (Continued).

County Name

County Portion in

FIPS

MPO

NHTS Sample Size, unweighted
households

3 Atlanta

1 Walton

13297

Partial

33

5 Augusta

2 Columbia

13073

Partial

326

5 Augusta

2 Richmond

13245

Total

420

6 Brunswick

3 Glynn

13127

Total

245

7 Cartersville

3 Bartow

13015

Total

167

8

Chattanooga/ Catoosa

2 Catoosa

13047

Total

39

8

Chattanooga/ Catoosa

2 Dade

13083

Partial

6

8

Chattanooga/ Catoosa

2 Walker

13295

Partial

25

Total;

9 Columbus

2 Chattahoochee 13053 military

4

presence

9 Columbus

2 Harris

13145

Partial

88

9 Columbus

2 Muscogee

13215

Partial

411

9 Dalton

3 Whitfield

13313

Total

128

10 Gainesville

2 Hall

13139

Total

384

10 Gainesville

2 Jackson

13157

Partial

128

11 Hinesville

3 Liberty

13179

Total

96

11 Hinesville

3 Long

13183

Partial

19

12 Macon

3 Bibb

13021

Total

292

12 Macon

3 Jones

13169

Partial

58

13 Rome

3 Floyd

13115

Partial

167

14 Savannah

2 Bryan

13029

Partial

75

14 Savannah

2 Chatham

13051

Total

611

14 Savannah

2 Effingham

13103

Partial

125

15 Valdosta

3 Lowndes

13185

Total

202

16

Warner Robins

3 Houston

13153

Total

283

16

Warner Robins

3 Peach

13225

Partial

45

99 Non-MPO

4 Other counties

None

931

Because each county contains a wide variety of built environments, a measure of neighborhood type known as urbanicity is sometimes used instead of or in addition to MPO tier. This measure,
22

which was created for the NHTS by consultants at the Claritas company, was released as part of the August 2018 update to the NHTS dataset.9 The urbanicity measure combines built environment measures, such as density, with data about residents' tendencies to travel within or outside of their communities. Claritas describes the community types as in figure 2.
Figure 1. Map. Georgia MPO map. Retrieved 9-25-18 from the Georgia Association of Metropolitan Planning Organizations http://www.gampo.org/members.html. The map shows MPO boundaries within the state of Georgia; the Columbus and Chattanooga MPOs
extend into neighboring states.
9 Claritas also updated the census-derived built environment variables, e.g., housing density, and percent renters. More details can be found in the 2017 NHTS Version 1.1 Release Notes (NHTS 2018).
23

Figure 2. Classification. Urbanicity classifications. Source: Claritas (2018, pp. 45) As shown in table 2, each MPO tier contains neighborhoods that fall into several different categories. Using urbanicity allows a way to capture these intra-county geographic differences.
24

Table 2. Classification of sample households by MPO tier and urbanicity.
OVERVIEW This section compares major travel indicators for the state of Georgia with national figures. (Data will also be compared across different MPO tiers in the Trip Purpose section.) It also summarizes statistics relating to household travel, person travel, trip purpose, and mode choice. Subsequent chapters will examine the relationships between these topics, as well as relationships to demographic traits. The issue of vehicle ownership will also be visited in greater detail. To set the stage for the statistics to follow, table 3 presents the number of households, people, and vehicles in Georgia and nationally. The Atlanta MPO accounts for more than half of the state's census on all indicators, while the non-MPO portions of the state comprise about a fifth of its census. The remaining one quarter is divided between mid-size and small MPO regions.
25

Table 3. Summary demographic statistics.

Households

National 118,208,251



Persons, all ages 321,419,000



Persons, ages 5+ 301,599,169



Vehicles

222,578,926



Drivers, all ages

223,277,172



Drivers, ages 16+ 222,780,478



Workers

156,988,243



Source: NHTS weighted totals.10 indicates data not available.

Georgia (Statewide)
3,651,249 100%
10,204,581 100%
9,555,773 100%
6,997,337 100%
7,036,938 100%
7,000,240 100%
4,778,570 100%

Tier 1 Atlanta 1,956,521
53.6% 5,518,955
54.1% 5,181,569
54.2% 3,691,992
52.8% 3,848,238
54.7% 3,832,515
54.7% 2,759,079
57.7%

Tier 2 Medium
MPOs
593,833

Tier 3 Small MPOs
373,866

Tier 4 Non-MPO Counties
727,029

16.3%

10.2%

19.9%

1,656,452 1,055,347 1,973,827

16.2%

10.3%

19.3%

1,530,993

973,023 1,870,188

16.0%

10.2%

19.6%

1,121,151

707,766 1,476,427

16.0%

10.1%

21.1%

1,096,159

698,309 1,394,231

15.6%

9.9%

19.8%

1,091,691

694,927 1,381,108

15.6%

9.9%

19.7%

752,233

477,924

789,334

15.7%

10.0%

16.5%

Table 4 presents major travel indicators. Georgia households are slightly larger than the national average, but they are statistically similar to the national average for most indicators. Following the national pattern (McGuckin and Fucci 2018), there are as many vehicles as drivers. However, vehicle ownership is not universal; 6.9 percent of households own zero vehicles, and 18.9 percent have a vehicle deficit (fewer vehicles than potential drivers). See Vehicle Availability and Usage in this chapter for more details about vehicle ownership.

Within Georgia, some differences exist between Atlanta (tier 1) and other areas (tiers 24). Households in Atlanta are somewhat larger, and the ratios of vehicles to households, drivers, and

10 NHTS weights were benchmarked to 2015 American Community Survey 1-year estimates where available and 20112015 5-year estimates otherwise. See Roth, Dai, and DeMatteis (2017) for further details on the weighting process.
26

workers are lower. Rural counties (tier 4) have the highest ratios of vehicles per household, driver, and worker. However, the marked difference in the number of vehicles per worker is driven in part by the lower number of workers per household as compared to the rest of the state.

Table 4. Major travel indicators by MPO tier.

National

Georgia (Statewide)

Tier 1 Atlanta

Tier 2 Mid-sized
MPOs

Tier 3 Small MPOs

Tier 4 Non-MPO Counties

Persons (ages 5+) per HH Workers per HH

2.551 1.328

2.617 1.309

2.648 1.410

2.578 1.267

2.603 1.278

2.572 1.086

Drivers per HH

1.889

1.916

1.887

1.888

1.893

2.031

Vehicles per HH

1.883

1.916

1.887

1.888

1.893

2.031

Vehicles per Driver* Vehicles per Driver Aged 16+ Vehicles per Worker

0.997 0.999 1.418

0.994 1.000 1.464

0.959 0.963 1.338

1.023 1.027 1.490

1.014 1.018 1.481

1.059 1.069 1.870

* NHTS respondents were asked "Do you/does this person drive," and not about licensing. As a result, some people too young to receive a drivers license are recorded as drivers. Unless otherwise specified, this report will be analyzing drivers of driving age (ages 16+).

HH = Household.

Table 5 gives information about household composition and trip generation at the level of MPO tier, and table 6 gives the same information about individual MPOs. These data are designed to illuminate local differences. However, caution should be employed when examining estimates for MPOs with a small sample size in the NHTS data. In particular, Chattanooga is represented by only 70 households. Hinesville and Rome are represented by fewer than 200 households each (see the appendix). The sample sizes for the remaining small and medium MPOs range from 201 to 811 households (table 1). Due to these sample size constraints, most analysis will proceed at the level of MPO tier.

27

Table 5. Summary demographic and travel statistics by MPO tier.

Place Statewide MPO TIER 1. Atlanta
2. Medium-size MPOs
3. Small MPOs
4. Non-MPO

Demographics

Persons aged

Households

5+

3,651,249

9,555,773

Annual Travel by Residents

Vehicle Trips VMT Person Trips

Vehicles (Thousands) (Millions) (Thousands)

6,997,336

6,882,190

71,984

11,073,916

PMT (Millions)
131,078

1,956,521 53.6%
593,833 16.3%
373,866 10.2%
727,029 19.9%

5,181,569 54.2%
1,530,993 16.0%
973,023 10.2%
1,870,188 19.6%

3,691,992 52.8%
1,121,151 16.0%
707,766 10.1%
1,476,427 21.1%

3,844,926 55.9%
1,051,526 15.3%
707,440 10.3%
1,278,298 18.6%

40,499 56.3% 9,698 13.5% 6,687 9.3%
15,100 21.0%

6,172,253 55.7%
1,746,745 15.8%
1,115,020 10.1%
2,039,899 18.4%

75,945 57.9%
20,596 15.7%
10,672 8.1%
23,864 18.2%

Place 1. Albany 2. Athens 3. Atlanta 4. Augusta 5. Brunswick 6. Cartersville 7. Chattanooga 8. Columbus 9. Dalton 10. Gainesville 11. Hinesville 12. Macon 13. Rome 14. Savannah 15. Valdosta 16. Warner Robins

Table 6. Summary demographic and travel statistics by MPO.

Demographics

Persons aged

Households

5+

43,355

101,328

91,767

229,894

1,956,521

5,181,569

137,340

361,997

40,721

97,161

28,688

75,736

41,322

83,631

98,590

262,221

40,440

110,803

85,636

229,842

24,637

75,667

68,827

185,303

28,072

63,558

139,178

363,409

39,787

107,097

59,340

156,370

Annual Travel by Residents

Vehicle Trips VMT Person Trips

Vehicles (Thousands) (Millions) (Thousands)

72,507

76,265

558

113,246

176,565

149,989

1,356

262,718

3,691,992

3,844,926

40,499

6,172,253

236,760

257,795

2,061

427,870

75,241

94,922

929

148,967

66,791

61,532

798

93,980

84,019

56,642

560

80,159

173,178

179,854

1,556

285,955

78,853

65,930

501

118,350

190,512

165,397

1,853

268,332

49,648

56,391

851

77,342

123,776

119,437

1,006

198,211

52,625

50,416

407

82,502

260,118

241,850

2,312

421,711

75,688

80,130

506

117,902

112,636

102,416

1,129

164,520

PMT (Millions)
1,052 4,830 75,945 3,502 1,726 1,099
857 2,708
779 3,384 1,015 1,841
704 5,315
882 1,574

28

Compared to the U.S. average, Georgians make fewer trips per household and per capita (table 7 and table 8). The exception is the Atlanta MPO, where tripmaking levels and mileage are above average for Georgia, and close to the U.S. average for most indicators. Additionally, Atlanta residents are less likely to be immobile than residents of other MPO tiers (i.e., make no trips on their travel day; see table 9).
Table 7. Summary statistics per household, daily.

Person

Vehicle

Geography

Trips

PMT

Trips

Georgia

8.31

98.35

5.16

National

8.60

99.46

5.11

MPO TIER 1. Atlanta

8.64

106.35

5.38

2. Medium-size MPOs

8.06

95.02

4.85

3. Small MPOs

8.17

78.20

5.18

4. Non-MPO

7.69

89.93

4.82

MPO

1. Albany

7.16

66.45

4.82

2. Athens

7.84

144.21

4.48

3. Atlanta

8.64

106.35

5.38

4. Augusta

8.54

69.86

5.14

5. Brunswick

10.02

116.10

6.39

6. Cartersville

8.98

104.94

5.88

7. Chattanooga

5.31

56.84

3.76

8. Columbus

7.95

75.24

5.00

9. Dalton

8.02

52.74

4.47

10. Gainesville

8.58

108.26

5.29

11. Hinesville

8.60

112.90

6.27

12. Macon

7.89

73.28

4.75

13. Rome

8.05

68.75

4.92

14. Savannah

8.30

104.64

4.76

15. Valdosta

8.12

60.77

5.52

16. Warner Robins

7.60

72.69

4.73

From McGuckin and Fucci (2018, 12)

MPO represented by small sample

VMT 54.01 53.81
56.71 44.74 49.01 56.90
35.28 40.49 56.71 41.12 62.54 76.25 37.10 43.23 33.96 59.29 94.60 40.04 39.77 45.50 34.87 52.15

29

Table 8. Summary statistics per capita, daily.

Per Person (ages 5+)

Per Driver*

Geography Georgia National

Person Trips 3.17
3.37

Vehicle

PMT Trips

37.58

1.97

38.98

2.00

Vehicle VMT Trips
20.64 2.68
19.13 2.70

MPO TIER 1. Atlanta

3.26 40.16

2.03 21.41 2.74

2. Medium MPOs

3.13 36.86

1.88 17.35 2.63

3. Small MPOs

3.14 30.05

1.99 18.83 2.78

4. Non-MPO

2.99 34.96

1.87 22.12 2.51

MPO 1. Albany

3.06 28.43

2.06 15.10 2.96

2. Athens

3.13 57.56

1.79 16.16 2.42

3. Atlanta

3.26 40.16

2.03 21.41 2.74

4. Augusta

3.24 26.50

1.95 15.60 2.93

5. Brunswick

4.20 48.66

2.68 26.21 3.55

6. Cartersville 7. Chattanooga

3.40 39.75 2.63 28.08

2.23 28.88 2.73 1.86 18.33 2.29

8. Columbus

2.99 28.29

1.88 16.25 2.73

9. Dalton

2.93 19.25

1.63 12.39 2.53

10. Gainesville 11. Hinesville

3.20 40.33 2.80 36.76

1.97 22.09 2.69 2.04 30.81 2.97

12. Macon 13. Rome

2.93 27.22 3.56 30.37

1.77 14.87 2.50 2.17 17.57 3.13

14. Savannah

3.18 40.07

1.82 17.43 2.47

15. Valdosta

3.02 22.57

2.05 12.95 2.82

16. Warner Robins

2.88 27.58

1.79 19.79 2.42

* To match national figures, drivers of all ages are used.

From McGuckin and Fucci (2018, p, 13), except for vehicle trips and VMT per person.

MPO represented by small sample.

VMT 28.03 28.49
28.83 24.24 26.24 29.67
21.66 21.87 28.83 23.46 34.80 35.47 22.60 23.65 19.20 30.11 44.77 21.02 25.27 23.56 17.82 26.64

30

Table 9. Trip distance and percent of population immobile.

Average

Person Trip

Geography

Length (mi)

Georgia

11.85

MPO TIER

1. Atlanta

12.31

2. Medium-size MPOs

11.80

3. Small MPOs

9.58

4. Non-MPO

11.71

MPO

1. Albany

9.29

2. Athens

18.40

3. Atlanta

12.31

4. Augusta

8.19

5. Brunswick

11.58

6. Cartersville

11.69

7. Chattanooga

10.69

8. Columbus

9.48

9. Dalton

6.59

10. Gainesville

12.62

11. Hinesville

13.13

12. Macon

9.30

13. Rome

8.54

14. Savannah

12.61

15. Valdosta

7.49

16. Warner Robins

9.57

MPO represented by small sample.

Average Vehicle Trip Length (mi)
10.46
10.53 9.22 9.45
11.81
7.32 9.04 10.53 8.00 9.79 12.98 9.88 8.65 7.60 11.21 15.09 8.42 8.08 9.56 6.32 11.03

Percent Immobile (0 trips on travel day)
19.4%

Percent Immobile, weekdays
only 16.1%

18.2% 20.0% 20.7% 21.7%

15.1% 16.0% 17.3% 18.2%

22.8% 24.9% 18.2% 18.4% 12.3% 14.7% 20.5% 22.9% 21.1% 19.3% 25.8% 18.9% 18.1% 16.7% 26.3% 23.9%

21.6% 17.0% 15.1% 14.5% 14.1% 14.7% 12.3% 17.7% 18.9% 19.9% 16.1% 16.1% 19.7% 14.6% 22.1% 12.5%

Trip Purpose Overview The NHTS collects detailed information about trip purpose. In most cases, it is desirable, or even necessary (due to small sample sizes), to collapse the NHTS's detailed taxonomy into a smaller number of categories. Table 10 shows descriptive statistics for the most detailed measure of trip purpose--primary activity at destination--and color-coded categories into which these activities are classified.
Broadly, trip purpose is classified into three categories for this report. Mandatory trips support required life activities such as working and attending school. Household-serving trips contribute

31

to the maintenance of the household. They include transporting other household members, shopping, errands, and medical services. Household-serving trips benefit the household as a whole but may not directly benefit the traveler (for example, picking up dry cleaning for a family member). Discretionary trips, in contrast, facilitate activities that are intrinsically valuable to the travelers themselves (e.g., socializing, engaging in recreation, and exercising), allow them to participate in a broader community, or allow them to volunteer their time to a good cause. Trips to return home are the final leg of any circuit of trips. Because of this, they are a good proxy for how often someone leaves the house, without the complications of trip chaining.
Trip purpose (level 1) will be the primary measure used in this report. When analysis requires the use of a smaller number of categories, the summarized purposes or purpose categories will be used. The classification categories used here are nested and mutually compatible. However, they differ slightly from NHTS's predefined categories.11
The NHTS classifications base purpose determinations on the purpose at destination, not factoring in origin or physical location. As a result, a trip with the purpose of "work" will be a work trip, regardless of whether the destination was the respondent's official work address. Conversely, trips from work to home are classified as "return home" trips rather than "work commute" trips because the purpose at destination will be regular home activities. However, even when trips both to and from work are accounted for, commuting represents a minority of trips: 17.3 percent of person trips and 23.9 percent of vehicle trips.
11 The NHTS combines commuting with other work travel. This study separates them to allow analysis specifically of the work commute versus other related trip purposes. The other difference is the creation of a community activities category. The NHTS classifies religious and community activities with attending school and daycare and volunteer activities with "other," whereas this report combines these three types of community activities into a new category. The categorization of some loop trips were also adjusted (see footnote to table 10).
32

Table 10. Trip purpose by destination: Types and frequencies.

Level Zero: Primary Activity at Destination

Percent of Percent of

Person Vehicle

Trips

Trips

Level 1: Trip Purpose

Level 2: Purpose, summarized

Category 1: Mandatory Travel Work

11.1%

Work-related meeting / trip

1.0%

Attend school as a student

3.7%

Attend child care

0.3%

Attend adult care

0.1%

14.9% Work Commute Other Work-related
1.2% Travel Attend School or
1.1% Day Care Attend School or
0.1% Day Care Attend School or
0.1% Day Care

Work Commute Other Work-related Travel Attend School or Day Care Attend School or Day Care Attend School or Day Care

Category 2: Household-Serving Travel

Drop off /pick up someone

6.7%

Buy goods (groceries, clothes, appliances, gas)
Buy services (dry cleaners, bank, service a car, pet care)
Other general errands (post office, library)

14.1% 1.9% 2.6%

Health care visit (medical, dental, therapy)

1.4%

8.4% Transport Someone 15.4% Shopping/Errands
2.3% Shopping/Errands 2.9% Shopping/Errands 1.4% Medical Services

Transport Someone Other Household-Serving Other Household-Serving Other Household-Serving Other Household-Serving

Category 3: Discretionary Travel
Recreational activities (visit parks, movies, bars, museums)

3.0%

1.9% Social/Recreational Personal Enrichment

Exercise (go for a jog, walk, walk the dog, go to the gym)

2.9%

2.5% Social/Recreational Personal Enrichment

Visit friends or relatives Buy meals (go out for a meal, snack, carry-out)

3.8% 7.9%

3.4% Social/Recreational 7.5% Meals

Personal Enrichment Personal Enrichment

Religious or other community activities

2.5%

2.1% Community Activities Community

Volunteer activities (not paid)

0.5%

0.6% Community Activities Community

Other Travel Change type of transportation

1.0%

0.4% Other

Other

Something else
Regular home activities (chores, sleep)

0.3% 34.3%

0.1% Other 32.9% Return Home

Other Return Home

Work from home (paid)

0.7%

0.9% Return Home

Return Home

To better capture the purpose of loop trips, loop trips where the primary activity at destination was home or school were

reclassified as exercise for nonmotorized and recreation for all other modes.

33

As figure 3 shows, although where, how, and how much Georgians travel may vary from region to region, why they travel is strikingly constant. Statewide, half of all person trips are devoted to the day-to-day business of going to work, school, or daycare, and returning home from all kinds of trips. Just over half of the remainder go to household-serving travel (27 percent of total trips), and 22 percent of trips are discretionary.
Figure 3. Stacked bar graph. Person trip purpose by MPO tier (color-coded per table 10). Mode Overview Privately operated vehicles dominate travel in Georgia, receiving 10 times the share of any other mode (table 11). POVs are predominantly privately owned; only 0.2 percent of POV trips were in rental cars.
34

Table 11. Person trips by mode (detailed).

Mode Privately Operated Vehicle Pedestrian (walk/wheelchair)

Reported Trips,
unweighted

Estimated Annual Trips,
weighted (millions)

Percent of Trips,
weighted

Mode Category

52,675

9467.0

85.5% POV

4,201

887.2

8.0% Nonmotorized

Bike

287

68.2

0.6% Nonmotorized

School Bus

1,213

324.8

2.9% Bus or train

Public Transit (bus, heavy rail, light rail, and streetcar)

515

157.1

1.4% Bus or train

Paratransit

56

15.3

0.1% Other ground or water

Other Bus

141

22.5

0.2% Bus or train

Taxi/Ridehail/Limo

222

63.2

0.6% Other ground or water

Air

105

19.0

0.2% Air

Other

289

49.5

0.4% Other ground or water

POV includes car, van, pickup truck, motorcycle/moped, RV, rental car, and trips in a "company vehicle"

originally reported under the "something else" category. "Pedestrian" combines walking with 29 wheelchair trips that were originally reported under the "something

else" category.

"Other" includes golf cart, Segway, boat, and responses originally reported under the "something else" category

(large trucks, agricultural and construction equipment, skateboard/scooter/skates).

Two trips by skateboard/scooter/skates were classified as nonmotorized.

Pedestrian trips, including both walking and wheelchair use, are the second most common, followed by school buses and public transit. No other single mode accounts for more than 1 percent of trips. As a result, while future targeted analysis will focus specifically on less common modes such as ridehailing, paratransit, and biking, analysis of geographic patterns will focus on the mode categories described in table 11, as shown in table 12.

35

Table 12. Mode share and trip distance by MPO tier.

Annual Person Trips (millions)

Mode

Statewide

POV

9,467.0

Nonmotorized (walk, bike, wheelchair)

955.5

Bus or train

504.4

Other ground or water transportation

127.8

Air

19.0

Mode

Statewide

POV

85.5%

Nonmotorized (walk, bike, wheelchair)

8.6%

Bus or train

4.6%

Other ground or water transportation 1.2%

Air

0.17%

Mode

Statewide

POV

103,800

Nonmotorized (walk, bike, wheelchair)

842

Bus or train

4,058

Other ground or water transportation

1,634

Air

20,120

Mode

Statewide

POV

11.0

Nonmotorized (walk, bike, wheelchair)

0.9

Bus or train

8.1

Other ground or water transportation

12.9

Air

1,059

Tier 1 (Atlanta)

Tier 2 (Medium MPOs)

Tier 3 (Small MPOs)

Tier 4 (Non-MPO)

5,165.6 1,507.5

989.6 1,804.4

562.9

160.2

80.8

151.6

347.7

56.9

29.8

69.9

80.9

19.9

13.7

13.3

15.1

2.03

1.22

0.65

Percent of Person Trips

Tier 1

Tier 2

Tier 3

Tier 4

83.7%

86.3%

88.7%

88.5%

9.1%

9.2%

7.2%

7.4%

5.6%

3.3%

2.7%

3.4%

1.3%

1.1%

1.2%

0.65%

0.24%

0.12%

0.11%

0.03%

Annual Person Miles (millions)

Tier 1

Tier 2

Tier 3

Tier 4

56,869

15,273

9,476

22,181

464

196

58

124

2,637

430

194

797

923

167

293

251

15,052

3,905

651

511

Average Trip Distance (miles)

Tier 1

Tier 2

Tier 3

Tier 4

11.0

10.1

9.6

12.3

0.8

1.2

0.7

0.8

7.6

7.6

6.5

11.4

11.5

8.5

21.4

18.8

996

1,927

536

790

POVs account for a smaller proportion of trips in the Atlanta MPO compared to the rest of the state. POV use is offset by comparatively high mode shares for nonmotorized transportation and buses/trains. Nonmotorized mode use is most common in medium and large MPOs (tiers 12).

36

Unsurprisingly, motorized trips comprise the majority of person miles. Motorized trips are the longest in rural, non-MPO counties and the shortest in small MPO counties. Air travel exerts an outsized influence on PMT. Although air travel comprises less than 1 percent of trips made by Georgians, it represents 15 percent of total person miles,12 making it the second largest component of PMT. Interestingly, tier 2 MPO households make proportionally fewer air trips than Atlanta MPO households do (as might be expected), but the ones they do make are about twice as long, on average. A similar comparison holds between tier 4 and tier 3 households.
According to NHTS figures, nonmotorized transit accounts for a small share of PMT. The average reported nonmotorized trip distance is less than 1 mile. However, these figures are likely misleading. The NHTS determines trip distance by calculating the shortest path between the origin and destination. This is likely to underestimate the true length of nonmotorized trips; pedestrians and cyclists often follow circuitous routes to avoid safety hazards, find a pleasant walking environment, or to follow a scenic route (Lu, Scott, and Dalumpines 2018; Misra and Watkins 2017). Cyclist trip lengths are particularly prone to being underestimated; 8 percent of trips by adult cyclists have computed speeds of less than 2 mph based on shortest path distance and reported duration. Further, route choices differ by age and gender; on average, as compared to young male cyclists, women and elderly cyclists choose longer trajectories that are perceived to be safer (Misra and Watkins 2018). Therefore, the shortest path distance is likely to be inaccurate for all cyclists, but it is systematically less accurate for female and elderly cyclists than it is for young males. A time-based measure of cycling is likely more accurate.
12 The fact that air trips are extreme outliers in terms of length is the reason air has been left as its own category, despite the small quantity of trips.
37

TRIP PATTERNS BY LOCATION OF TRAVEL

This report focuses primarily on travel by residents of Georgia. However, when considering demand for transportation infrastructure, it is important also to account for trips by nonresidents within Georgia and trips that Georgia residents make outside of the state. This section describes trip patterns by location of travel. Table 13 compares trips made by residents of Georgia and trips by nonresidents that took place partly or entirely in Georgia.

Table 13. Annual trips and vehicle miles by Georgians and within Georgia.

Person Trips (thousands) Entirely Within Georgia
Partly Within Georgia
Entirely Outside of Georgia
Subtotal, Entirely or Partly in Georgia Subtotal, Entirely or Partly Outside of Georgia Total

By Georgia Residents

By Out-of-State U.S. Residents

10,610,653 162,441 300,822
10,773,094
463,263
11,073,916

205,771 254,385 460,156



Total
10,816,424 416,826
11,233,250



Vehicle Trips (thousands)
Entirely Within Georgia
Partly Within Georgia
Entirely Outside of Georgia
Subtotal, Entirely or Partly in Georgia Subtotal, Entirely or Partly Outside of Georgia Total

6,687,867 85,361
108,961 6,773,228
194,322
6,882,189

98,115 137,011 235,126


6,785,982 222,372
7,008,354


VMT (thousands)
Entirely Within Georgia
Partly Within Georgia
Entirely Outside of Georgia
Subtotal, Entirely or Partly in Georgia Subtotal, Entirely or Partly Outside of Georgia Total
indicates no data.

60,858,290 8,144,777 2,981,012
69,003,067
11,125,789
71,984,079

1,714,764.65 11,062,151 12,776,915



62,573,055 19,206,928 81,779,982



38

Georgians' out-of-state travel is largely offset by trips nonresidents make in Georgia. Georgians make 463 million out-of-state person trips per year, while residents of other U.S. states make 460 million person trips entirely or partially in the state of Georgia.13
Motorized travel is less balanced. After accounting for out-of-state trips made by Georgians elsewhere, Georgia roadways receive 40.8 million net additional trips from out-of-state motorists. However, while Georgia is a net recipient of interstate trips (between Georgia and another location), Georgia is a net donor of trips that are entirely out-of-state. Georgians make 109 million vehicle trips per year that take place entirely outside of Georgia, versus 98 million trips entirely within Georgia made by nonresidents.14
The distinction between trips in a particular place and trips by residents of a particular place is also relevant when examining travel within different parts of Georgia (table 14). Forty percent of person trips by Georgians cross city lines (i.e., the origin and destination are in different cities or towns). Twenty-four percent cross county lines. However, the overwhelming majority of trips stay within a single MPO; only 5.3 percent of trips cross MPO boundaries.
Trips that cross between different MPO tiers account for 5 percent of total person trips. The directionality of these trips is balanced, with approximately the same number of trips from A to B as from B to A (table 14 and table 15). Therefore, when discussing trips in spatial terms, this report will classify them by origin rather than double-counting them or apportioning them between origin and destination.
13 Trips by international visitors and residents of U.S. territories are not accounted for in the survey data. 14 The remainder of the report excludes non-Georgians' travel, but includes Georgians' out-of-state travel, unless otherwise specified.
39

Table 14. Annual person trips within and between MPO tiers (thousands).

Detail may not sum to totals because of rounding.

Table 15. Annual vehicle trips within and between MPO tiers (thousands).

Origin

Destination

2. Medium 1. Atlanta MPOs

1. Atlanta

3,691,232

31,356

2. Medium MPOs

33,341 938,663

3. Small MPOs

14,287

5,406

4. Non-MPO

37,986

33,337

5. Out of State

7,018

20,215

Total Attracted Trips

3,783,864 1,028,977

Detail may not sum to totals because of rounding.

3. Small MPOs 4. Non-MPO

15,208

40,120

5,062

34,259

660,930

44,669

46,123 1,055,889

5,802

10,152

733,125 1,185,089

5. Out of State 7,200 21,425 3,634 9,916 108,961 151,135

Total Originating
Trips
3,785,115 1,032,749
728,926 1,183,251
152,148 6,882,189

While trips between two geographic categories may be balanced on the whole, this does not necessarily apply to trips at a specific time of day. When focusing on rush hour loads or work commutes, a more detailed method for assigning trips to jurisdictions should be considered.

HOUSEHOLD AND PERSONAL MOBILITY The Overview section provided an overview of household and personal mobility and discussed regional differences. This section focuses on demographic-based patterns.

40

As shown in table 16, the amount of travel generated by a household differs by gender of household head(s), race,15 income, and vehicle ownership. However, these patterns are complicated by correlations with household size. For a clearer picture of individuals' mobility, it is important to consider person-level travel, as well.
Table 17 reveals that many household patterns are also observable at the level of individual household members. Several populations have markedly reduced mobility compared to the state average. While some racial discrepancies exist, the largest differences are by income, age, and disability. The two groups with the most restricted mobility are elderly people (ages 80+) and people with disabilities. Over the course of the year, a Georgia resident with a mobility impairment will make 327 fewer person trips than the average Georgian. An elderly Georgian will make 414 fewer trips, a reduction of more than one trip per day. However, younger seniors are, on average, relatively mobile. The declines in tripmaking associated with aging are mainly seen in the elderly population (ages 80+). Nearly 45 percent of this group made no trips on their travel day. People with disabilities (of all ages) face similar constraints; 40 percent were immobile on their travel day. It should be noted that there is considerable overlap between the elderly and disabled populations; 38.5 percent of elderly respondents reported having a medical condition or handicap that impedes travel (compared to 19.2 percent of younger seniors and 6.8 percent of adults). However, the majority of Georgians with disabilities are younger (figure 4). Therefore, the two groups' needs should not be automatically conflated.
15 See Methodological Notes for more details on how race is defined.
41

Table 16. Daily household travel by demographic groups. 42

Table 17. Daily per capita trips and immobility (persons ages 5+).

Subpopulation

Person Trips

All

3.17

Gender

Male

3.16

Female

3.18

Race

White non-Hispanic

3.25

Black & Black multiracial

3.10

Other race

3.05

Age

Children (515)

2.45

Teens (1617)

2.54

Adults (1864)

3.44

Seniors (6579)

3.08

Elderly (80+)

2.04

Disability

No impairment

3.25

Impairment present

2.28

Annual Household Income

<$15,000

2.85

$15,000 to $24,999

2.82

$25,000 to $34,999

3.14

$35,000 to $49,999

3.33

$50,000 to $74,999

3.20

$75,000 to $99,999

3.32

$100,000+

3.42

* Vehicle trips as driver only.

Immobile is defined as zero trips on travel day.

Vehicle Trips*
1.97
2.02 1.93
2.10 1.88 1.72
0.01 0.76 2.49 2.24 1.16
2.05 1.09
1.26 1.64 2.03 2.21 2.21 2.20 2.14

Percent Immobile
19.4%

Percent Immobile, weekdays
16.1%

17.8% 21.0%

13.8% 18.3%

17.8% 21.3% 21.4%

14.5% 18.1% 17.5%

22.7% 22.2% 16.2% 27.6% 44.7%

16.3% 16.1% 13.2% 25.7% 47.8%

17.6% 40.3%

14.1% 37.7%

25.9% 23.2% 20.7% 18.5% 18.7% 15.5% 15.7%

23.5% 18.8% 18.1% 17.3% 14.6% 11.6% 12.1%

Calculated using 5-day person weights, excluding households whose travel day fell on a weekend or federal holiday. Vehicle trips for this age group stem from minors learning to drive under the supervision of an adult. Defined by NHTS as "a condition or handicap that makes it difficult to travel outside the home."

43

56.5%

4.0%

1.2%

26.3%

12.0%

Children (5-15) Teens (16-17) Adults (18-64) Seniors (65-79) Elderly (80+)

Figure 4. Bar graph. Age of population with mobility impairments.

Interestingly, the elderly and disabled are the only populations for which weekday immobility was higher than overall immobility. One possible explanation for this phenomenon is a difference in trip purpose (e.g., emphasis on visiting family or attending church; fewer or no work trips on weekdays). However, the difference also suggests that elderly and disabled residents' travel is sometimes contingent on the availability of a friend or family member to assist them; 35 percent of elderly and disabled residents reported asking others for rides. Even if still somewhat independently mobile, some elderly and disabled individuals may curtail their travel during peak periods and, thus, defer some trips to weekends or eliminate them altogether; 63 percent reported reducing their travel.16 On average, men and women make similar numbers of trips (table 17). However, as will be shown in chapter 5, this is not true across all age groups; elderly women's mobility is much lower than that of men of a comparable age. Additionally, trip purposes vary by gender.
In addition to making fewer trips, elderly and disabled residents also make shorter trips (table 18). Though women make as many trips as men, their trips are also shorter. Georgians who earn

16 Individuals with disabilities were asked about changing travel behavior as a result of their disability; elderly respondents with no reported disability were asked if they had changed their behavior over the last year.
44

more than $50,000 travel a greater distance than those who earn less, but within the lower income tiers, the relationship does not appear to be linear; there is a spike in trip length and PMT among residents earning between $15,000 and $24,999 as compared to the income groups above and below. It is possible this is linked to employment, e.g., the relative location of low-wage jobs and low-income housing. Average person trip distance is somewhat longer than that of the average vehicle trip. This result likely reflects the outsized influence of long-distance air travel. Note, for example, that the difference diminishes or reverses in households with income of less than $50,000. An additional reason to treat person trip distances with caution is that, with the exception of loop trips, the NHTS determines trip distance by calculating the shortest path between the origin and destination. While this will understate the distance for at least some motorized trips, it is likely to disproportionately underestimate distance for modes that often follow less direct routes (e.g., walking, biking, and transit).
TRIP PURPOSE This section examines demographic differences in trip purpose. Specifically, the tables in this section disaggregate by trip purpose the patterns in person trips presented in the previous section, Household and Personal Mobility. This section also examines the relationship between trip purpose and mode.
45

Table 18. Trip distance and daily PMT and VMT per person ages 5+.

Subpopulation

PMT

VMT

Average Person Average Vehicle Trip Length (mi) Trip Length (mi)

All

37.58

20.64

11.85

10.44

Gender

Male

40.53

23.66

12.82

11.71

Female

34.80

17.79

10.93

9.18

Race White non-Hispanic Black & Black multiracial Other race

40.31 34.01 35.54

22.07 20.01 16.54

12.39 10.98 11.68

10.52 10.60
9.64

Age Children (515) Teens (1617) Adults (1864) Seniors (6579) Elderly (80+) Disability

19.36 20.32 44.52 32.77 13.13

0.07 4.56 26.91 20.50 6.91

7.90 8.00 12.96 10.66 6.46

6.61 5.97 10.78 9.14 5.95

No impairment Impairment present

39.49 16.05

21.67 8.98

12.14 7.07

10.57 7.82

Annual Household Income

<$15,000

23.17

11.94

8.14

$15,000 to $24,999

28.85

16.47

10.25

$25,000 to $34,999

24.94

17.41

7.95

$35,000 to $49,999

29.68

20.75

8.89

$50,000 to $74,999

43.66

24.62

13.67

$75,000 to $99,999

46.18

23.91

13.91

$100,000+

46.07

24.09

13.49

Vehicle trips for this age group stem from minors learning to drive under the supervision of an adult.

9.24 10.04
8.56 9.40 11.15 10.89 11.26

Defined by NHTS as "a condition or handicap that makes it difficult to travel outside the home."

Table 19 shows person trips by purpose and income. Over all, higher income groups make more trips, but these trips are not always spread proportionally across all types. Discretionary travel is a good example. Dining out and takeout generally increase with income. However, trips for community and religious activities are highest in high- and low-income categories, with a dip in moderate income groups. People in the lowest income bracket make a relatively large number of

46

social and recreational trips; approximately the same number as households earning $50,000$74,999. However, social trips drop markedly among households earning $15,000$34,999. These groups also make more work trips than members of very low-income households, so part of the difference may relate to the availability of time for recreation among the working poor. There is a notable spike in trips to transport someone in the $35,000$49,999 bracket. This likely reflects the travel patterns of households that have acquired at least one car, but may still not have enough vehicles for every potential driver. These households can be characterized as having a vehicle deficit (Blumenberg et al. 2018; see also chapter 5). The presence of trips to transport someone raises the issue of how much utility travelers obtain from their trips, or whether utility for one household member is obtained at a cost of disutility for another. On average, someone in a household earning $35,000$49,999 will make 73 more person trips per year than someone in the next income category down, but 22 of those trips, or 30 percent, will be to transport someone else.
47

Table 19. Annual person trips by purpose and income.

Annual Trips per Person (Ages 5+)

Household Income

Trip Purpose

$15,000 to $25,000 to $35,000 to $50,000 to $75,000 to <$15,000 $24,999 $34,999 $49,999 $74,999 $99,999 $100,000+

All Purposes

1,041

1,028

1,145

1,217

1,166

1,211

1,247

Mandatory Travel

120

166

209

198

204

226

205

Work commute

71

104

149

143

144

160

137

Other work-related travel

6

8

9

9

13

15

19

Attend school or daycare

42.7

53.8

51.1

46.3

46.4

51.0

49

Household-Serving Travel

324

288

314

355

306

321

294

Transport someone

69.0

69.7

74.5

96.2

74.4

76.9

84

Shopping or errands

228

197

223

246

219

225

197

Medical/dental services

27

22

16

13

13

18

13

Discretionary Travel

227

212

216

251

250

260

310

Social/recreational

123

87.3

102

134

122

136

158

Dining

72.0

80.0

85.7

88.1

92.5

87.3

115

Community/religious

32.7

44.5

28.9

29.1

35.7

36.0

36.5

Other Travel

10.2

9.1

12.7

13.5

19.0

14.8

19.4

Other

10.2

9.1

12.7

13.5

19.0

14.8

19.4

Return Home

359.9

353.0

392.6

399.6

388.5

390.5

419.5

Return home

360

353

393

400

388

390

420

Table continues on next page.

48

Table 19. (Continued).

Continued from previous page.

Percent of Trips (Persons Ages 5+)

Trip Purpose Mandatory Travel Work commute Other work-related travel Attend school or daycare Household-Serving Travel Transport someone Shopping or errands Medical/dental services Discretionary Travel Social/recreational Dining Community/religious Other Travel Other Return Home Return home

$15,000 to $25,000 to $35,000 to $50,000 to $75,000 to <$15,000 $24,999 $34,999 $49,999 $74,999 $99,999 $100,000+

11.5%

16.1%

18.3%

16.3%

17.5%

18.7%

16.4%

6.8%

10.1%

13.1%

11.7%

12.4%

13.2%

11.0%

0.6%

0.8%

0.8%

0.7%

1.1%

1.2%

1.5%

4.1%

5.2%

4.5%

3.8%

4.0%

4.2%

3.9%

31.1%

28.0%

27.4%

29.2%

26.2%

26.5%

23.6%

6.6%

6.8%

6.5%

7.9%

6.4%

6.4%

6.7%

21.9%

19.1%

19.5%

20.2%

18.7%

18.6%

15.8%

2.6%

2.1%

1.4%

1.0%

1.1%

1.5%

1.1%

21.8%

20.6%

18.9%

20.7%

21.4%

21.4%

24.8%

11.8%

8.5%

8.9%

11.0%

10.4%

11.3%

12.7%

6.9%

7.8%

7.5%

7.2%

7.9%

7.2%

9.2%

3.1%

4.3%

2.5%

2.4%

3.1%

3.0%

2.9%

1.0%

0.9%

1.1%

1.1%

1.6%

1.2%

1.6%

1.0%

0.9%

1.1%

1.1%

1.6%

1.2%

1.6%

34.6%

34.3%

34.3%

32.8%

33.3%

32.2%

33.6%

34.6%

34.3%

34.3%

32.8%

33.3%

32.2%

33.6%

49

As shown in table 20, white respondents make more discretionary trips than do people of color. This tendency holds for all three categories of discretionary travel. Conversely, white respondents make fewer trips to transport someone else. Homebound ("return home") trips provide a proxy for the number of times people leave the house, regardless of any trip chaining that may happen once they leave. Examining the total tripmaking of the elderly (ages 80+) and people with disabilities underscores how people in these groups are accessing fewer destinations, while examining their home trips shows that they also leave the house less frequently (table 21). Unsurprisingly, elderly respondents make very few work-related trips. However, people with mobility impairments also made very few work trips. In fact, people with mobility impairments made nearly 40 percent fewer work trips than did teenagers, most of whom are full-time high school students. NHTS does not contain information about whether participants' disabilities also impose restrictions on their ability to work, so it is not possible to definitively distinguish between unemployment resulting from an inability to work and unemployment resulting from a travel difficulty. However, it is worth noting that for some Georgians with disabilities, the biggest obstacle to getting their foot in the door to a new career might be getting their foot to the door. Chapter 5, Health and Disability will return to the needs of Georgians with mobility impairments.
50

Table 20. Gender and racial differences in annual person trips by purpose.

Annual Trips per Person (Ages 5+)

Gender

Race

Trip Purpose All Purposes Mandatory Travel Work commute Other work-related travel Attend school or daycare Household-Serving Travel Transport someone Shopping or errands Medical/dental services Discretionary Travel Social/recreational Dining Community/religious Other Travel Other Return Home Return home

All 1,158
188 129
12 47.3 310 77.6 216
17 256 129 91.6 35.2 15.1 15.1 388.7 389

Male 1,155
219 155
14 50.7 269 61.9 196
12 259 132 95.6 30.9 16.2 16.2 391.8 392

White Non- Black & Black Female Hispanic Multiracial

1,162

1,187

1,131

159

184

184

104

132

120

11

14

11

44.1

37.8

52.7

349

301

343

92.4

67.4

96.5

236

218

226

21

15

21

253

288

219

126

144

108

87.8

106

76.7

39.4

38.1

34.0

14.0

14.6

15.4

14.0

14.6

15.4

385.8

399.9

369.6

386

400

370

Percent of Trips

Other Race
1,112 217 136 9 71.5 269 71.8 186 12 219 122 69.5 27.3 15.9 15.9
391.1 391

Trip Purpose Mandatory Travel Work commute Other work-related travel Attend school or daycare Household-Serving Travel Transport someone Shopping or errands Medical/dental services Discretionary Travel Social/recreational Dining Community/religious Other Travel Other Return Home

All 16.3% 11.1% 1.0% 4.1% 26.8% 6.7% 18.7% 1.4% 22.1% 11.1%
7.9% 3.0% 1.3% 1.3% 33.6%

Male Female 19.0% 13.7% 13.4% 9.0% 1.2% 0.9% 4.4% 3.8% 23.3% 30.1% 5.4% 8.0% 16.9% 20.3% 1.0% 1.8% 22.4% 21.8% 11.4% 10.9%
8.3% 7.6% 2.7% 3.4% 1.4% 1.2% 1.4% 1.2% 33.9% 33.2%

White Non- Black & Black Hispanic Multiracial

15.5%

16.2%

11.2%

10.6%

1.1%

1.0%

3.2%

4.7%

25.3%

30.4%

5.7%

8.5%

18.4%

19.9%

1.3%

1.9%

24.3%

19.4%

12.1%

9.6%

9.0%

6.8%

3.2%

3.0%

1.2%

1.4%

1.2%

1.4%

33.7%

32.7%

Other Race
19.5% 12.2%
0.9% 6.4% 24.2% 6.5% 16.7% 1.0% 19.7% 11.0% 6.2% 2.5% 1.4% 1.4% 35.2%

51

Table 21. Frequency of trip purposes by age and disability.

Trip Purpose All Purposes Mandatory Travel Work commute Other work-related travel Attend school or daycare Household-Serving Travel Transport someone Shopping or errands Medical/dental services Discretionary Travel Social/recreational Dining Community/religious Other Travel Other Return Home Return Home

Children 515 895 179 2 2 175.1 147 36 103 7 203 117 54.0 31.3 24.8 24.8 341.7 342

Annual Trips per Person (Ages 5+)

Age

Mobility Impairment

Teens Adults Seniors Elderly 1617 1864 6579 80+ Present Absent

928 1,255 1,123

744

831 1,187

200

218

60

12

34

202

31

181

53

9

19

139

0

17

6

1

3

13

168.3 20.0

1.6

1.1

12.5

50.4

155

343

402

277

324

309

43 94.5 57.0 26.6

42.1

80.8

108

232

315

221

228

215

4

17

30

30

54

14

216

269

280

203

181

262

123

133

128

102

84.2

133

68.6

102

100 55.0

65.8

93.8

24.2 33.6 51.5 46.5

31.5

35.4

14.3 13.9 10.0

6.8

8.3

15.7

14.3 13.9 10.0

6.8

8.3

15.7

342.5 410.7 370.6 244.6 282.9 398.1

342

411

371

245

283

398

Percent of Trips

Trip Purpose
Mandatory Travel Work commute Other work-related travel Attend school or daycare Household-Serving Travel Transport someone Shopping or errands Medical/dental services Discretionary Travel Social/recreational Dining Community/religious Other Travel Other
Return Home Return Home

Children 515
20.0% 0.2% 0.2%
19.6% 16.4%
4.1% 11.6%
0.8% 22.7% 13.1%
6.0% 3.5% 2.8% 2.8%
38.2% 38.2%

Teens 1617
21.5% 3.3% 0.0%
18.1% 16.8%
4.7% 11.6%
0.5% 23.3% 13.2%
7.4% 2.6% 1.5% 1.5%
36.9% 36.9%

Adults Seniors Elderly 1864 6579 80+ Present

17.4%
14.4% 1.3% 1.6%
27.4% 7.5%
18.5% 1.4%
21.4% 10.6%
8.1% 2.7% 1.1% 1.1%

5.3%
4.7% 0.5% 0.1% 35.8% 5.1% 28.1% 2.7% 24.9% 11.4% 8.9% 4.6% 0.9% 0.9%

1.6%
1.3% 0.2% 0.2% 37.3% 3.6% 29.7% 4.0% 27.3% 13.7% 7.4% 6.3% 0.9% 0.9%

4.1%
2.2% 0.4% 1.5% 39.0% 5.1% 27.5% 6.5% 21.8% 10.1% 7.9% 3.8% 1.0% 1.0%

32.7% 33.0% 32.9% 32.7% 33.0% 32.9%

34.0% 34.0%

Absent
17.0% 11.7%
1.1% 4.2% 26.1% 6.8% 18.1% 1.1% 22.1% 11.2% 7.9% 3.0% 1.3% 1.3%
33.5% 33.5%

52

As shown in table 22, age-related patterns in mobility are highly gendered. For example, women's mobility decreases at age 65. Men ages 6579, on the other hand, experience little reduction in their overall mobility; a decrease in work travel is largely offset by increases in discretionary and household-serving travel. The differences are even more stark among the elderly (ages 80+). Elderly men make 77 percent as many trips as younger adult men (ages 1864). Elderly women, on the other hand, make only half as many trips as younger adult women, making them substantially less mobile than children.17 Elderly women with disabilities fare even worse, making just 371 trips per year (versus 673 for elderly men with disabilities). Gendered differences in travel manifest at earlier ages, as well. Among adults, women make 62 more trips per year than men overall. However, an even larger gap occurs for household-serving travel in particular; compared to adult men, adult women make 113 more household-serving trips per year, or 2.2 per week. The overall gap shrinks from 113 to 62 largely because adult women make 66 fewer commute trips than adult men do.
17 Some of this difference may come from the fact that women live longer, and a higher percentage of elderly women report having a medical condition that interferes with their mobility (43 percent versus 29 percent). However, the gender gap persists even after accounting for disability. Nondisabled elderly men make 1,062 trips, versus 839 for women.
53

Table 22. Annual tripmaking by purpose, gender, and age.

Children 5-15

Trip Purpose

Male

Female

All Purposes

925

862

Mandatory Travel

177

181

Work commute

3

1

Other work-related travel

3

Attend school or daycare

170

180

Household-Serving Travel

144

150

Transport someone

33

40

Shopping or errands

104

102

Medical/dental services

7

8

Discretionary Travel

222

182

Social/recreational

134

100

Dining

60

48

Community/religious

28

35

Other Travel

32

17

Other

32

17

Return Home

350

332

Return Home

350

332

Note: detail may not sum to totals because of rounding.

Teens 16-17

Male

Female

945

912

225

176

36

26

0

0

188

150

114

194

18

67

91

123

4

5

234

198

137

110

72

65

25

23

18

11

18

11

354

332

354

332

Adults 18-64

Male

Female

1,223 1,285

256

182

215

149

18

15

23

18

286

399

72

116

203

260

11

23

262

275

130

136

103

100

29

38

13

15

13

15

407

414

407

414

Seniors 65-79

Male

Female

1,196 1,062

83

41

73

35

8

4

1

2

405

399

61

53

318

312

26

33

306

259

139

119

117

86

49

53

14

7

14

7

389

355

389

355

Elderly 80+

Male

Female

948

636

28

4

26

1

2

1

2

345

242

23

28

284

187

37

26

256

175

133

85

74

45

49

45

7

7

7

7

312

209

312

209

54

This gendered division of household-serving travel begins early; teenaged girls (ages 1617) make 1.7 times as many household-serving trips as teenaged boys. It would be a mistake to attribute this difference entirely to social outings to the local mall. Teen girls make 1.3 times as many shopping and errands trips as teen boys, but they make 3.6 times as many trips to transport someone else as do teen boys.18 In a typical week, a teen boy will make 0.4 trip to transport someone, while a teen girl will make 1.4 trips to transport someone. At the same time, teen girls make markedly fewer recreational trips than do teen boys. Chapter 5, How Much and What For: The Interrelated Effects of Gender and Age on Mobility and Trip Purpose will revisit these issues.
Table 23 and table 24 respectively show trip purpose by mode of travel and mode of travel by trip purpose. So, for example, 11.8 percent of POV trips have a purpose of work commute (table 23), and 90.7 percent of work commute trips are by POV (table 24).
Some uncommon modes have a relatively small number of trips in the NHTS Georgia data. For greater transparency when interpreting estimates, sample sizes by mode are included in table 23. In particular, the reader should be extremely cautious when interpreting statistics about the purpose of trips by paratransit and bus transportation (aside from school bus and public transit).
18 Under Georgia's graduated driver's license program, teenagers of all genders face limitations on transporting nonfamily members. Teens are prohibited from transporting nonfamily members for the first 6 months after receiving their license, then limited to one nonfamily passenger for the next 6 months. While the data do not have licensing information about the teen drivers surveyed, these restrictions by definition apply to all 16-year-old drivers and some portion of 17-year-old drivers. Teen drivers who have had their license for at least a year are still limited to three nonfamily passengers under age 21, and no driver under 18 is permitted to drive between midnight and 5 a.m. For details, see: https://dds.georgia.gov/teen-drivers.
55

Table 23. Trip purpose by mode of travel.

Purpose

Privately Pedestrian Operated (walk or Vehicle wheelchair) Bike

School Bus

Public Transit

Taxi,

Other

Other Ridehail, or

Ground or

Bus

Limo Paratransit Water

Air

Work commute Other work-related travel

11.8% 1.0%

6.2% 0.8%

6.1%


0.6% 0.1%

21.0% 1.0%

3.8% 5.6%

11.4% 4.1%

9.0%

20.2% 3.0%

5.2% 17.4%

Attend school or daycare Transport someone

2.7% 7.3%

3.9% 2.1%

1.8%

44.8% 6.7%

7.4% 4.2%

4.3% 4.1%

1.9%

3.8% 0.1%

1.9% 3.6%

5.1%

Shopping or errands

20.3%

11.3% 15.3%

11.8% 1.5%

6.9%

13.6%

10.5%

Medical/dental services

1.5%

0.4%

0.1%

4.6% 6.9%

2.6%

23.6%

0.7%

Social/recreational

8.8%

38.3% 26.2%

3.4%

6.5% 36.8%

9.3%

3.3%

14.4% 4.9%

Dining

8.3%

7.4%

6.8%

0.0%

4.5% 7.5%

9.8%

5.7% 0.9%

Community/religious

3.3%

1.4%

1.0%

0.3%

1.4% 4.3%

10.8%

6.1%

0.1%

Other

0.8%

2.9%

2.9%

3.6%

2.8% 6.2%

7.1%

4.7%

3.3% 64.6%

Return home

34.1%

25.3% 39.8% 40.5%

34.8% 19.0%

36.1%

35.8%

36.7% 1.9%

Total annual trips, in thousands 9,466,980 887,175 68,190 324,798 157,103 22,487

63,151

15,302

49,478 19,005

85.5%

8.0%

0.6%

2.9%

1.4% 0.2%

0.6%

0.1%

0.4% 0.2%

Sample size (unweighted trips)

52,675

4,201

287

1,213

515

141

222

56

289

105

Including rental cars. The most common trip purpose for air was to change to a different mode of transportation. This is likely because most air travelers will take a different form of transportation from

the airport to their final destination. Blank cells indicate combinations that were not found in the dataset (e.g., no participant reported using a school bus to go shopping).

56

Table 24. Mode of travel by trip purpose.

Purpose

Privately
Operated Vehicle

Pedestrian (walk or
wheelchair)

Bike

School Bus

Public Transit

Other Bus

Taxi,

Other

Ridehail, or

Ground or

Limo Paratransit Water

Work commute Other work-related travel

90.7% 84.6%

4.5% 6.3%

0.3%


0.2% 0.3%

2.7% 1.4%

0.1% 1.1%

0.6% 2.2%

0.1%

0.8% 1.3%

Attend school or daycare

56.3%

7.7%

0.3% 32.2%

2.6% 0.2%

0.3%

0.1%

0.2%

Transport someone

93.4%

2.5%

2.9%

0.9% 0.1%

<0.1%

0.2%

Shopping or errands

93.2%

4.9%

0.5%

0.9% <0.1%

0.2%

0.1%

0.3%

Medical/dental services

88.5%

2.5% <0.1%

4.5% 1.0%

1.0%

2.3%

0.2%

Social/recreational

67.4%

27.6%

1.4%

0.9%

0.8% 0.7%

0.5%

<0.1%

0.6%

Dining

89.9%

7.5%

0.5% <0.1%

0.8% 0.2%

0.7%

0.3%

Community/religious

92.6%

3.7%

0.2%

0.3%

0.7% 0.3%

2.0%

0.3%

0.0%

Other

55.6%

17.6%

1.4%

8.2%

3.0% 1.0%

3.1%

0.5%

1.1%

Return home

86.8%

6.0%

0.7%

3.5%

1.5% 0.1%

Including rental cars.

Blank cells indicate combinations that were not found in the dataset (e.g., no participant reported using a school bus to go shopping).

0.6%

0.1%

0.5%

Air 0.1% 2.8% 0.2%
0.1% 0.0%
8.5% <0.1%

57

POVs account for the majority of trips for every purpose. They account for more than 80 percent of trips for every purpose except attending school or daycare, social and recreational trips, and trips whose purposes could not be classified. Non-POV trips to school were primarily by school bus. A further 8 percent of trips to school were by walking and biking, and 2.6 percent were by transit (table 24).
Aside from home trips, social and recreational trips make up the largest share of nonmotorized modes, with shopping and errands in second place. The largest proportion of transit trips are work trips, followed by shopping and errands.
VEHICLE AVAILABILITY AND USAGE
Vehicle Ownership Georgia households own an average of 1.92 vehicles. As figure 5 shows, 93 percent of households own at least one vehicle.

Number of Household Vehicles

Three or more, 24.9%

Zero, 6.9%
One, 33.6% Two, 34.6%

Figure 5. Pie chart. Vehicle ownership among Georgia households.

58

In addition to raw numbers of vehicles, it is important to consider the number of vehicles available in relationship to the number of potential drivers. As shown in figure 6, 19 percent of Georgia households have at least one vehicle, but not as many vehicles as potential drivers. These households can be described as having a vehicle deficit (Blumenberg et al. 2018). Approximately half of vehicle-deficit households have what this report refers to as a hard deficit, where the number of vehicles is smaller than the number of household members listed as drivers. The other half have a soft deficit, with enough vehicles available for each listed driver, but not enough for other household members of driving age.19
Nondeficit households include vehicle-sufficient households (i.e., an equal number of vehicles as potential drivers), and vehicle-surplus households (i.e., more vehicles than potential drivers). About 5 percent of vehicle-sufficient households have a "soft" surplus because one or more people of driving age is listed as a nondriver.
19 Some writers, including Blumenberg et al., (2018) calculate vehicle deficit based purely on the number of drivers. This report considers nondriving adults and teenagers for several reasons. First, the NHTS's determination of "driver" is somewhat ambiguous with regard to whether teenagers with learner's permits and elderly people who are licensed to drive but refrain from doing so should be considered drivers. Basing vehicle sufficiency measures on the number of people of driving age provides a more consistent measure. Second, nondriving adults present additional, competing travel needs that must be met with available vehicles. Third, some adults and teenagers may fail to learn to drive if they do not expect to have access to a car; it is more consistent with the purpose of these classifications to consider households with such individuals as being vehicle-deficit rather than vehicle-sufficient.
59

Figure 6. Bar graph. Vehicle sufficiency in Georgia households.
Researchers debate whether households with zero vehicles should be referred to as "carless" or "car free," the latter to suggest that not owning a vehicle is a valid lifestyle choice.20 However, in Georgia, the demographics of zero-vehicle and vehicle-deficit households suggest that these households have reduced vehicle ownership out of economic necessity (table 25). Nearly one third of households in the lowest income bracket are carless, while less than 1 percent of households earning $75,000 or above are car-free. The proportion of nondeficit households increases steadily at higher income levels. There are also notable racial discrepancies; households with nonwhite members are more likely to have a vehicle deficit or be carless.
Lower-income households were less likely than higher-income households to have purchased a vehicle within the past year (table 26). However, among only vehicle-owning households, the
20 For the same reason, this research team was reluctant to imply that households with fewer vehicles than driving-age members are "deficient," but use the admittedly similar term "deficit" in keeping with Blumenberg et al. (2018).
60

lowest-income households are nearly as likely as the wealthiest households to have purchased a vehicle within the past year.

Table 25. Vehicle sufficiency by income, race, and number of household vehicles.

All Households Annual Household Income 1. <$15,000 2. $15,000 to $24,999 3. $25,000 to $34,999 4. $35,000 to $49,999 5. $50,000 to $74,999 6. $75,000 to $99,999 7. $100,000+ Number of Vehicles in Household 1 2 3+ Race of Household Members White non-Hispanic Some or all nonwhite

Zero-Vehicle 6.9%
32.5% 6.9% 2.9% 1.1% 1.1% 0.4% 0.1%
n/a n/a n/a
2.9% 11.6%

Deficit

Nondeficit

18.9%

74.1%

24.3% 30.5% 23.8% 22.0% 17.4% 12.9%
9.2%

43.2% 62.6% 73.3% 76.8% 81.5% 86.7% 90.7%

36.4% 14.4%
6.9%

63.6% 85.6% 93.1%

13.0% 25.9%

84.1% 62.5%

It is likely that low-income households' frequent vehicle purchases are by obligation rather than choice; the average vehicle purchased by the lowest-income households already has 133,973 miles on the odometer, nearly 40,000 more miles than any other income group. Although vehicles nearer the end of their life spans are cheaper to purchase, maintenance costs may become prohibitive, leading to faster turnover. Nearly one quarter of vehicles being driven by the lowest-income households were purchased in the past 12 months (table 26). Table 27 also shows the condition of vehicles that were not recently purchased.

61

Table 26. Vehicle purchasing behavior by household income.

Annual Household Income

Percent of Households that Purchased a Vehicle Recently

All Households

Vehicle-Owning Households Only

Condition of Newly Purchased Vehicles

Mean Age of Vehicle (years)

Mean Mileage of Vehicle

All income levels

26.1%

28.0%

7.3

69,333

1. <$15,000

20.7%

30.6%

11.4

133,973

2. $15,000 to $24,999

17.4%

18.7%

9.2

86,610

3. $25,000 to $34,999

23.6%

24.3%

8.8

94,393

4. $35,000 to $49,999

25.1%

25.4%

8.3

74,867

5. $50,000 to $74,999

27.0%

27.3%

6.6

61,638

6. $75,000 to $99,999

31.5%

31.7%

6.4

55,073

7. $100,000+

33.8%

Odometer reading at time of survey, 011 months after date of purchase.

33.8%

5.0

43,019

62

Table 27. Characteristics of vehicles owned by households of different income levels.

Mean Vehicle Annual Household Income Age (years)

Average Vehicle Odometer Reading

<$15,000

14.2

138,001

$15,000 to $24,999

12.7

124,183

$25,000 to $34,999

11.8

118,761

$35,000 to $49,999

11.1

111,126

$50,000 to $74,999

11.0

110,765

$75,000 to $99,999

10.4

100,295

$100,000+

9.1

87,436

Odometer reading at time of survey, 011 months after date of purchase.

Percent of Vehicles that
are Newly Purchased

Mean Age of Newly
Purchased Vehicles

Mean Mileage of
Newly
Purchased Vehicles

24.1%

11.4

133,973

13.2%

9.2

86,610

17.0%

8.8

94,393

15.5%

8.3

74,867

14.5%

6.6

61,638

15.5%

6.4

55,073

15.2%

5.0

43,019

63

Vehicle Usage On average, each vehicle in a household was driven 11,939 miles over the course of the year. In total, each household used their vehicles to drive an average of 22,472 miles, as shown in figure 7. The median was 18,048 miles. The figure is top-coded at 100,000 miles for clarity; the 99th percentile for miles driven was 102,166 miles.
Figure 7. Histogram. Distribution of annual household miles driven. The relationship between number of vehicles and household VMT is not constant across household type. Vehicle-deficit households, in particular, drive each vehicle more than other households do (table 28).
64

Table 28. Miles driven per vehicle by household vehicle sufficiency.

Vehicle Ownership Status All vehicle-owning households Nondeficit, single potential driver Nondeficit, multiple potential drivers Vehicle-deficit households

Percent of Households 93.1% 26.4% 47.7% 18.9%

Annual Miles Driven Per Household Vehicle
12,169 11,145 11,889 14,304

Vehicle Fleet Characteristics Georgia's fleet of personal occupancy vehicles is diverse and evolving. The median age of vehicles is 10 years, but 5.6 percent are less than 2 years old and 1.3 percent have been on the road for 40 years or more.
Table 29 divides vehicles into cohorts based on the passage of California's Low Emissions Vehicle standards, which serve as a proxy for tightening emissions standards. In addition to differences in age and mileage, body types vary between age cohorts. Contemporary vehicles include a larger proportion of SUVs and fewer pickup trucks and motorcycles. New vans are also somewhat less common than vans from the two preceding decades.
Alternative-fuel vehicles now account for 4.2 percent of contemporary vehicles and 1.8 percent of the overall fleet. Of these, 63 percent are hybrid vehicles, 29 percent are electric, and 5.0 percent are plug-in hybrids. Alternative-fuel Vehicles in chapter 4 further discusses alternative-fuel vehicles.

65

Table 29. Vehicle characteristics by vehicle age cohort.

Number of vehicles Percent of fleet Average age Newly purchased by household (within past 12 months) Vehicle Mileage Mean odometer reading 049,999 mi 50,00099,999 mi 100,000149,999 mi 150,000199,999 mi 200,000+ mi Vehicle Type Auto/wagon SUV Pickup truck Van or minivan Motorcycle Other Alternative fuel (any body type)

Pre-LEV All Vehicles (before 1993)

6,997,337

319,663

100%

4.6%

10.9

34.7

LEV I (19932003)
1,854,254
26.5%
17.3

LEV II

Contemporary

(20042014) (20152017)

3,909,762

856,277

55.9%

12.2%

7.9

1.5

15.8%

8.6%

10.2%

11.8%

49.3%

105,434 28.6% 24.1% 20.6% 14.4% 12.3%
49.5% 23.6% 17.5%
5.4% 2.4% 1.6% 1.8%

154,431 13.0% 15.8% 20.6% 19.9% 30.6%
42.2% 8.1%
40.1% 2.5% 3.3% 3.8% 0.8%

166,628 6.1%
10.6% 23.8% 28.0% 31.5%
44.3% 20.2% 24.1%
5.6% 3.1% 2.7% 0.2%

94,991 24.2% 35.3% 23.9% 11.4%
5.3%
52.7% 24.8% 13.6%
5.8% 2.1% 0.9% 2.2%

19,453 95.1%
3.9% 0.4%
0.6%
49.9% 32.3% 12.0%
3.7% 0.9% 1.2% 4.2%

66

In addition to "normal" vehicles used in day-to-day travel, some residents have seldom-used vehicles. These vehicles are not frequently driven because their owners have primary vehicles that are used for most household trips (as differentiated from owners who have a single vehicle but drive infrequently). These secondary, seldom-used vehicles may be hobby or leisure vehicles (e.g., motorcycles, campers, antique cars, etc.). Others are special-use vehicles, such as a pickup truck kept on hand for occasional freight hauling.

These seldom-used vehicles account for approximately 4 percent of vehicles in Georgia. As shown in table 30, these vehicles tend to be older, and are more likely to include leisure vehicles such as RVs, motorcycles, and miscellaneous vehicles such as campers.

Table 30. Characteristics of seldom-used vehicles.

Type of Vehicle

Regular

Seldom Used

1. Car/Wagon

50.8%

33.7%

2. Van

5.5%

2.9%

3. SUV

24.6%

9.2%

4. Pickup

17.7%

18.6%

5. Other Truck

0.3%

0.7%

6. RV

0.3%

4.3%

7. Motorcycle

0.8%

27.2%

97. Something Else

0.0%

3.4%

Age

Mean Age

10.56

16.64

Age Cohort

0. Pre-LEV (pre-1993)

4.1%

14.2%

1. LEV1 (19932003)

25.6%

45.1%

2. LEV2 (20042014)

57.4%

36.8%

3. Contemporary (20152017)

12.8%

3.9%

A seldom-used vehicle is defined as a vehicle with fewer than 1,050 annual miles that is

driven less than half as many miles as would be expected given the household annual miles

driven and number of vehicles.

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Seldom-used vehicles are predominantly owned by higher-income households (table 31). Households earning less than $50,000 annually account for half of all households and 33.6 percent of seldom-used vehicles. Those earning more than $100,000 account for 22.4 percent of all households and 34.7 percent of the vehicles.

Table 31. Ownership of seldom-used vehicles by annual household income.

Annual Household Income 1. <$15,000 2. $15,000 to $24,999 3. $25,000 to $34,999 4. $35,000 to $49,999 5. $50,000 to $74,999 6. $75,000 to $99,999 7. $100,000+

Percent of Households
16.8% 10.4% 11.0% 12.1% 16.4% 10.9% 22.4%

Percent of Regular Vehicles
8.2% 7.6% 9.4% 12.0% 18.5% 13.7% 30.6%

Percent of Seldom-Used
Vehicles
6.3% 7.3% 7.3% 12.7% 17.2% 14.5% 34.7%

TRANSIT PREFERENCES AND USE
While personal occupancy vehicles are the dominant form of transportation in Georgia, there is a societal interest in promoting the use of public transit. This section summarizes transit availability and then analyzes Georgia workers' preferences about transit quality.
Transit Availability and Use Table 32 summarizes county-level transit funding within each MPO.21 Twelve MPOs receive funding to offer fixed-route public transit in at least one county. Four MPOs offer only rural

21 Data on transit funding generously provided by Garrow et al. (2018). Preliminary report is available at http://garrowlab.ce.gatech.edu/sites/default/files/20191027%20Rural%20Transit%20in%20Georgia.pdf
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(on-demand) service. Thirty-day transit usage also varies, from a low of 3.4 percent of the population ages 5+ (Hinesville) to 17.3 percent (Athens).
Transit access can vary considerably within an MPO. Table 33 classifies counties by their level of access to transit. The level of access is based on transit offerings in the county, and in the MPO of which the county is part. The majority of the population resides in counties with a full fixed-route transit system (though this does not take into account quality of the system or proximity to a transit station). The majority of counties, which are home to 36.5 percent of the population, have partial access to transit. Some of these counties are rural counties that provide on-demand service. The remainder are counties in MPOs that do not offer fixed-route service. However, residents of these counties can theoretically access transit in other counties within the MPO. Unsurprisingly, transit use is highest in counties with full access. Nevertheless, 89 percent of users in counties with partial access have used transit within the past 30 days.
Transit Service Preferences Among Workers As part of the NHTS add-on module, workers in Georgia were asked an additional question about transit preferences. From a list of seven possibilities, they were asked to select the "three most important factors that would make [your/their]22 public transit system a good option for [your/their] commute." Participants' selections were not ranked against each other--each was a value of either yes or no.
22 This is one of the few opinion questions in the NHTS that was still asked of subjects whose responses were recorded by a proxy.
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Table 32. County transit funding and transit usage by MPO.

MPO

Counties in MPO

Number of Counties in Each Transit Funding Category*

None

Rural (OnDemand)

Urban

Urban & Rural

City Only

Fixed Route Transit

Albany

2

0

1

0

1

0

Athens

4

3

0

1

0

0

Atlanta

18

4

5

6

2

1

Augusta

2

0

1

0

1

0

Cartersville

1

0

0

0

1

0

Chattanooga

3

0

3

0

0

0

Columbus

3

2

0

1

0

0

Gainesville

2

0

1

0

1

0

Hinesville

2

0

1

0

1

0

Macon

2

0

1

1

0

0

Rome

1

0

0

1

0

0

Savannah

3

0

2

0

1

0

Rural (On-Demand) Service Only

Brunswick

1

0

1

0

0

0

Dalton

2

0

2

0

0

0

Valdosta

1

0

1

0

0

0

Warner Robins

2

1

1

0

0

0

Non-MPO Counties

Non-MPO

110

27

78

0

0

5

* As reported by Garrow et al. (2018).

Fixed route service for an entire county (as compared to city, which covers only part of a county).

Percent of population ages 5+, weighted.

Usage
Transit Use, Past 30 Days
14.7% 17.3% 15.7% 10.3%
7.9% 11.0% 11.4%
8.3% 3.4% 13.0% 8.1% 10.7%
4.3% 9.2% 12.1% 8.0%
6.9%

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Table 33. County demographics and transit usage by transit funding category.

County Information

Transit* Use (past 30 days)

Transit Funding Status

County

MPO*

Demographics
Number of Percent of Counties Population**

Statewide

Population Workers

Ages 5+

Only

MPO Tier 1 (Atlanta)

Population Workers

Ages 5+

Only

MPO Tiers 24

Population Workers

Ages 5+

Only

No Access

None

None

27

4.3%

2.9%

0.3%

2.9%

0.3%

None None

Partial Access Rural
Fixed-route

1

1.4%

8.5%

6.2%

8.5%

6.2%

9

5.3%

8.5%

5.6%

9.5%

6.9%

5.6%

2.4%

Rural

Rural

83

18.1%

8.3%

3.1%

8.3%

3.1%

Rural

Fixed-route Full Access

15

11.7%

9.2%

8.5%

11.3%

11.5%

6.5%

4.5%

Fixed-route Fixed-route

24

59.2%

15.8%

15.6%

16.9%

17.5%

12.8%

* For counties not in an MPO, county status is used for both columns.

** Defined by the NHTS as public or commuter bus, rail (Amtrak, commuter, elevated, light rail), and street car; does not include paratransit/dial-a-ride.

Funding for rural (on-demand) transit only.

Funding for fixed-route service in part or all of county jurisdiction, with or without funding for rural on-demand service.

Indicates access to fixed-route transit service at county level; does not consider individual distance to transit or level of service.

9.4%

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In general, proximity and cost were the two most common responses, though the relative frequencies varied by geography, vehicle ownership, and current transit access and use (table 34). Proximity and other convenience measures were more highly valued by residents of MPO tiers 1 and 2 and counties with full transit access. Residents of small MPOs and non-MPO counties selected cost more frequently. The most common response for zero-vehicle households was cost; this was the only group for whom proximity was not either the first or second choice. Occasional transit users were more likely to focus on both proximity and cost than either nonusers or moderate/frequent users; these groups' preferences were more heterogeneous. Workers whose "usual commute mode" was transit, paratransit, or some other bus represent 3.4 percent of the workforce. The top concern of this group, by 9 percentage points, was cost. The second- and third-most common concerns for these commuters were proximity and consistent on-time performance. For most other groups, the third-most common concern was compatibility with their schedule. While both characteristics are time-based, nontransit commuters were more likely to focus on frequency, whereas transit commuters were more focused on reliability. Preferences also varied across demographic groups. Table 35 shows responses by income, sex, age, disability, and race.
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Table 34. Perceived most important factors to make transit a good commute option by geography, vehicle ownership, and transit access/use.

Close to Work and
Home

Fits Faster than Reasonable Consistently

Schedule Driving

Cost

on Time

Avoids Travel Stress

Safety

All Workers MPO Tier 1. Atlanta 2. Medium MPOs 3. Small MPOs

55%
58% 55% 47%

45%
43% 55% 45%

38%
43% 33% 27%

48%
45% 48% 54%

36%
35% 36% 41%

29%
32% 22% 23%

27%
25% 27% 35%

4. Non-MPO County Access to Transit None

47% 61%

41% 54%

27% 20%

59% 64%

36% 23%

25% 25%

27% 24%

Partial

51%

Full

57%

Household Vehicle Ownership

Zero-vehicle

42%

Vehicle-deficit

54%

Nondeficit

56%

44% 46%
32% 42% 47%

34% 40%
21% 29% 41%

52% 46%
51% 50% 48%

38% 36%
45% 40% 34%

25% 31%
19% 26% 30%

30% 26%
28% 32% 25%

Actual Transit Usage (past 30 days)

None

55%

15 days

62%

46% 43%

38% 47%

49% 34%

35% 34%

28% 31%

28% 21%

6+ days Usual Commute Mode

54%

41%

39%

47%

41%

37%

22%

Public transit, other bus, or paratransit

44%

39%

40%

53%

42%

39%

20%

Something else

56%

46%

37%

48%

37%

27%

26%

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Table 35. Transit service quality priorities of Georgia workers by demographic group.

Close to Work and
Home

Fits Faster than Reasonable Consistently

Schedule Driving

Cost

on Time

Avoids Travel Stress

Safety

All workers

55%

45%

38%

48%

36%

29%

27%

Annual Household Income

<$15,000

41%

$15,000 to $24,999

53%

$25,000 to $34,999

50%

$35,000 to $49,999

53%

$50,000 to $74,999

54%

$75,000 to $99,999

56%

$100,000+

62%

37% 40% 42% 38% 43% 48% 52%

19% 30% 32% 32% 32% 41% 51%

55% 48% 55% 52% 53% 47% 42%

42% 43% 36% 39% 36% 34% 33%

30% 25% 31% 32% 33% 27% 27%

37% 33% 29% 29% 27% 26% 21%

Sex Male Female

56% 54%

46% 44%

40% 36%

48% 49%

36% 36%

30% 28%

22% 32%

Age Teens 1617 Adults 1864 Seniors 6579 Elderly 80+

52% 56% 52% 61%

68% 45% 42%
9%

22% 39% 32% 12%

67% 48% 52% 53%

12% 36% 39% 37%

6% 29% 40% 35%

49% 27% 22% 23%

Presence of Mobility Impairment

Disability

47%

33%

29%

50%

37%

34%

35%

No disability

56%

45%

38%

48%

36%

29%

27%

Race White non-Hispanic

59%

50%

43%

47%

34%

28%

21%

Black and Black multiracial
Other race

50% 52%

40% 38%

32% 32%

51% 50%

41% 34%

30% 32%

33% 36%

To isolate the effects of these interrelated factors, responses were modeled using logistic regression, with a separate model estimated for each possible response. Table 36 presents the

74

odds ratios23 associated with each variable. The models highlight differential priorities that generally conform to the ideas of captive and choice riders. Compared to low-income workers, wealthier workers were more likely to prioritize proximity, schedule, and speed, and less likely to cite cost as an important factor.
A similar split was evident along racial lines, with white, non-Hispanic workers more likely than workers of color to emphasize proximity, schedule, and speed. Black workers were more likely than white workers to emphasize reliability, and all nonwhite workers were more likely to cite safety. It is worth noting that once income is controlled for, there is no evidence of racial differences in the likelihood of prioritizing affordability.
The idea of transit as a means for avoiding travel stress was most popular in the highly congested Atlanta metropolitan area. Workers from households with cars were more likely to express interest in avoiding travel stress than workers from zero-vehicle households. Interestingly, lowincome workers were comparatively more likely than other groups to select "avoid travel stress," though it was still one of the least commonly chosen options for these respondents.
23 Odds ratios (ORs) are the factors by which the odds of choosing each response differ for people with the associated characteristic (e.g., living in a medium MPO area), compared to the base or reference group (for the continuous variable age, the OR is the factor by which the odds differ with a 1-year increase in age). An OR of 1 means that the characteristic has no impact on the odds of choosing that response. An OR greater than 1 means that people with that characteristic are more likely to choose the associated response than those in the base group, and conversely for an OR less than 1.
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Table 36. Logistic regression: Transit service quality priorities of Georgia workers.

1. Close to Work & 2. Fits Schedule Home

3. Faster than Driving

Covariates

OR P-Value OR P-Value OR P-Value

MPO Tier (base: Tier 1 Atlanta)

2 - Medium MPOs

0.922 0.375

1.662 <0.001 *** 0.674 <0.001 ***

3 - Small MPOs

0.703 0.002 *** 1.169 0.169

0.538 <0.001 ***

4 - Non-MPO

0.600 0.001 ***

Transit use, past 30 days (base: not used)

15 days

1.190 0.457

0.847 0.873

0.295 0.560

0.584 0.002 *** 1.208 0.424

6+ days

0.930 0.600

0.884 0.371

1.004 0.977

Annual Household Income (base: <$15,000)

$15,000 to $24,999

1.569 0.058 * 1.231 0.388

1.649 0.064 *

$25,000 to $34,999

1.503 0.061 * 1.265 0.279

1.819 0.015 **

$35,000 to $49,999

1.561 0.032 ** 0.982 0.930

1.496 0.090 *

$50,000 to $74,999

1.616 0.016 ** 1.241 0.284

1.434 0.114

$75,000 to $99,999 $100,000+

1.689 0.011 ** 1.527 0.038 ** 2.075 0.002 *** 2.051 <0.001 *** 1.732 0.005 *** 2.809 <0.001 ***

Household vehicle ownership (base: zero vehicles)

Vehicle-deficit

1.144 0.650

1.164 0.633

1.102 0.795

Nondeficit

1.019 0.948

1.178 0.602

1.459 0.305

Race (base: white non-Hispanic)

Black or Black multiracial 0.744 0.002 *** 0.764 0.006 *** 0.763 0.007 ***

Other race

0.765 0.042 ** 0.657 0.002 *** 0.685 0.006 ***

Other

Female

0.984 0.846

0.981 0.812

0.890 0.162

Age in years

0.998 0.547

0.997 0.293

0.995 0.104

Has mobility impairment 0.855 0.621

0.713 0.270

0.923 0.826

Constant

0.977 0.943

0.638 0.198

0.414 0.026 **

Note: Because weighted logistic regression was used, pseudo-R 2 statistics were not provided.

Odds ratio.

4. Reasonable 5. Consistently on 6. Avoids Travel

Cost

Time

Stress

OR P-Value OR P-Value OR P-Value

1.039 1.304 1.515

0.674 0.020 ** 0.009 ***

1.056 1.291 1.198

0.560 0.030 ** 0.254

0.596 <0.001 *** 0.600 <0.001 *** 0.657 0.018 **

0.576 0.020 ** 0.968 0.814

0.972 0.909 1.173 0.247

1.094 0.702 1.666 <0.001 ***

0.839 1.035 0.935 0.993 0.786 0.650

0.449 0.871 0.741 0.973 0.231 0.023 **

1.048 0.796 0.915 0.856 0.829 0.845

0.843 0.300 0.677 0.444 0.376 0.398

0.637 0.852 0.857 0.870 0.587 0.552

0.081 * 0.503 0.498 0.515 0.019 ** 0.005 ***

0.956 0.880 1.175 0.587

1.042 0.894 0.820 0.523

2.662 0.009 *** 3.127 0.003 ***

1.147 0.151 1.075 0.584

1.283 0.012 ** 1.023 0.869

1.007 0.947 1.247 0.124

0.962 0.996 1.014 1.060

0.634 0.234 0.966 0.860

0.958 1.000 0.923 0.671

0.610 0.870 0.809 0.244

0.879 1.012 1.122 0.139

0.150 <0.001 ***
0.724 <0.001 ***

7. Safety

OR P-Value

1.055 1.394 1.103

0.618 0.011 ** 0.578

0.732 0.248 0.724 0.058 *

0.842 0.666 0.785 0.756 0.725 0.622

0.488 0.087 * 0.286 0.199 0.156 0.028 **

1.383 0.358 1.393 0.352

1.572 <0.001 *** 2.032 <0.001 ***

1.650 0.992 1.395 0.295

<0.001 *** 0.025 0.327 0.002 ***

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Gender was not strongly affiliated with preferences with one notable exception: the odds of women citing safety concerns were 65 percent greater than those of men. This finding echoes many other studies on transit security and gender, and it is worth noting that concerns about safety can depress transit use among women (Keane 1998, Loukaitou-Sideris 2014, Clark et al. 2016).
Once other differences are controlled for, recent transit use has relatively few effects on expressed preferences.24 Occasional users are less concerned about cost than both frequent users and nonusers. Frequent users are more likely to mention avoiding travel stress. Both types of user may be less concerned about safety than non-users, but the effect is insignificant for occasional users and only borderline significant for frequent users.
SUMMARY OF FINDINGS ON GEOGRAPHICAL DIFFERENCES
The first section of this chapter (see Geographic Divisions for Analysis in Methodological Notes) has described the division of Georgia's counties into MPO tiers used throughout the report.25 The second section (Overview) provided a basic overview of mobility indicators by MPO size and, where possible, individual MPOs. The third section (Trip Patterns by Location of Travel) analyzed trip location (as opposed to residence of the traveler, which is the classification used for the rest of this report), and the seventh section (Transit Preferences and Use) discussed
24 In addition to including transit use as a covariate, a series of models fully interacted by transit ridership was estimated. These models were not an improvement over the more parsimonious models presented here. Models fully interacted by Atlanta vs. other MPO tiers were also tried and rejected.
25 Tier 1 comprises counties that partially or wholly fall within the Atlanta MPO. Tier 2 consists of counties that partially or wholly fall within a medium MPO region, and tier 3 consists of counties in small MPO regions. Tier 4 consists of counties that do not fall within any MPO. See table 1 for classifications of individual counties.
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differences in transit availability, use, and preferences among Georgians from different MPOs and MPO tiers.
This section summarizes key findings about geographical differences from subsequent chapters.
Work Travel (Chapter 2Chapter 3) At a state level, a plurality of jobs (46 percent) is in the professional/managerial/technical sector, followed by sales and service (27 percent), blue-collar (i.e., manufacturing, construction, maintenance, and farming, 18 percent) and clerical (9 percent) (see chapter 2, Worker Characteristics). There is, however, significant geographic variation. Blue-collar jobs account for 29 percent of total jobs in non-MPO counties (tier 4), which is more than twice the share in Atlanta (tier 1). Tiers 2 and 3 fall somewhere in the middle. The inverse is true of professional jobs, which are most common in tier 1 and least common in tier 4. These industry differences have implications for commuter schedules, since blue-collar workers tend to have more atypical schedules (e.g., working nights, weekends, holidays, or unpredictable hours). Perhaps relatedly, the annual total work journeys per worker is higher in non-MPO counties than elsewhere in the state.
Private occupancy vehicles, whether singly-occupied or carrying passengers, are the dominant commute mode (see chapter 2, Commute Mode by Person). In Atlanta, where transit offerings are comparatively robust, 91 percent of workers usually commute by POV, compared to 94 percent of workers in medium MPO counties and 97 percent of workers in small-MPO and non-MPO counties.
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Commute durations are longest in the Atlanta MPO area (see chapter 2, Commute Duration and Burden ). The region also has the largest variability in commute duration by time of day; the average PM peak commute is 42.2 minutes, which is 13.6 minutes longer than the average overnight commute. Non-MPO counties have the second-longest commute times, but the least variability by time of day. Small MPO counties have the shortest commute durations on average.
On a typical workday, 12 percent of Georgia commuters spend 2 hours or more traveling to and from work (see chapter 2, Commute Duration and Burden). These heavy commute burdens are not equally distributed; 2-hour commute burdens are more than twice as common in the Atlanta region as elsewhere in the state (16.6 percent in Atlanta versus 6.27.1 percent elsewhere).
Complex commutes (those involving at least one stop) are equally common across MPO tiers (see chapter 2, Overview of Commuters). Additional stops typically add distance to commutes. However, idiosyncratic local geography--such as Atlanta's complex highways or the rivers bisecting downtown Macon, Columbus, and Albany--resulted in some commutes being made geographically shorter by adding stops that encouraged the traveler to follow the geographically shortest route rather than the fastest one (see chapter 2, Demographic Differences).
At the time of data collection, 46 percent of Georgia workers had a flexible work schedule, location, or both (see chapter 3, Overview). Both kinds of flexibility are more commonly available to workers in Atlanta, where 54 percent have one or both kinds of work flexibility, compared to 37 percent of workers elsewhere in the state. Flexible work locations (including teleworking and home-based work) are most common in Atlanta, where commute durations are greater than in the rest of the state, and in non-MPO counties, where the distance between home and work is, on average, higher than elsewhere in Georgia.
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New Technology (Chapter 4) Adoption of alternative-fuel vehicles, ridehailing services such as Uber and Lyft, and online shopping are all more pronounced in Atlanta compared to the rest of the state (see chapter 4, sections on Alternative-fuel Vehicles, Shared Mobility, and Online Shopping). However, while ridehailing usage is lowest in non-MPO counties, users from these counties make more ridehailing trips than users from other types of counties (see chapter 4, Ridehailing). Ridehailing accounts for an estimated 87 percent of all vehicle-for-hire trips in Georgia, with the remainder conducted by traditional taxi and limo services (see chapter 4, Ridehailing and Vehicle-for-Hire Trips). However, in small MPO regions and non-MPO counties, ridehailing accounts for 95 percent of all vehicle-for-hire trips.
Equity (Chapter 5) Per capita tripmaking is the highest in Atlanta MPO counties and the lowest in non-MPO counties (see chapter 5, Key Equitable Mobility Indicators). Similarly, immobility is lowest in Atlanta and highest in non-MPO counties. However, despite the higher average mobility in Atlanta, the region is still home to a number of mobility-disadvantaged populations, especially low-income and transit-dependent people. Neighborhoods matching the urban category, which in Georgia are found only in Atlanta, appear to confer some mobility benefits, including supporting mobility among people with mobility impairments (see chapter 5, Risk Factors for Immobility among Adults with Mobility Impairments)
After controlling for other factors, Atlanta, Columbus (medium), and Brunswick (small) have the longest transit trips, followed by Savannah and Gainesville (see chapter 5, Key Equitable Mobility Indicators). Non-MPO counties have the shortest transit trips. In all types of counties,
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captive transit users face significantly longer travel times than choice users (see chapter 5, Vehicle Access).
Nonmotorized Travel and Travel for its Own Sake (Chapter 6Chapter 7) While there are some regional differences in nonmotorized travel (NMT), the larger differences are by neighborhood type, which can vary substantially within an MPO (see chapter 6, Overview). Neighborhood type is therefore a more useful way of examining differences in walking and biking. This report examines walking and biking trips as well as nonmotorized legs to access and egress public transit. NMT is most common in the densest urban neighborhood type (which, in Georgia, is only found in Atlanta). On average, residents of urban neighborhoods make more than three times as many NMT trips and transit access/egress legs than residents of second-city and suburban neighborhoods, and more than seven times as many trips/legs as residents of small towns and rural areas (see chapter 6, Travel Day Walking and Biking by Georgia Adults). The average urban resident spends more than 20 minutes per day walking and/or biking, compared to less than 8 minutes in all other neighborhood types. The purpose of NMT also varies by neighborhood type (see chapter 6, Travel Day Walking and Biking by Georgia Adults). The majority of NMT in all neighborhood types is for instrumental purposes. However, leisure travel accounts for more than a third of NMT in small-town and rural neighborhoods versus just 17 percent in urban neighborhoods. Loop trips, which are predominantly NMT and represent a form of travel for its own sake, are most common in Atlanta and non-MPO counties and lower in small and medium MPO regions (see chapter 7, Loop Trips in the 2017 Georgia NHTS Subsample).
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Finally, among children ages 517, leisure and socialization account for more than half of nonmotorized trips in rural and small-town neighborhoods (51.9 percent and 51.5 percent respectively), as well as in non-MPO counties more generally (51.5 percent) (see chapter 6, Children's Nonmotorized Travel for All Purposes). Elsewhere in the state, these trips for "fun" purposes are outnumbered by instrumental trips. In the aggregate, 8 percent of children in medium MPO counties typically walk or bike to/from school, followed by Atlanta MPO counties (see chapter 6, Children's School Travel). In nonMPO counties, just 3.5 percent walk or bike to/from school. However, the differences are stronger by neighborhood type; 22 percent of children in urban neighborhoods walk or bike to/from school, more than double the percentage in second-city neighborhoods. Just 0.6 percent of children in rural neighborhoods walk or bike to/from school.
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CHAPTER 2. COMMUTE AND WORK PATTERNS
CHAPTER 2 SUMMARY
This chapter analyzes work-related travel, particularly commuting.
Overview includes technical and vocabulary notes and provides an overview of workforce and commute characteristics. In addition to mode choice and carpooling behavior, the section describes the prevalence of complex work journeys (i.e., trips to or from work with at least one intermediate stop). Commutes to sites other than a respondent's "official" work location are also discussed. Together, complex commutes and commutes involving a nontraditional work location account for one third of work journeys in Georgia, highlighting the importance of effectively measuring these journeys.
Defining the Commute discusses several methods of dealing with complex work journeys when measuring work travel. The section focuses primarily on the methods used to define commute distances and durations for the purposes of analysis in this chapter. Readers primarily interested in the results of the analysis may choose to quickly advance to the fifth section (Commute Duration and Burden), which summarizes definitions to be used in the third section, Work Travel Distance, and the fourth section, Work Travel Distance by Mode.
Work Travel Distance examines commute distance, person miles traveled, and vehicle miles traveled. Georgia residents travel a total of 26.4 billion miles each year in their journeys to and from work, and 23.6 billion of those miles are as a driver of a private vehicle. Two thirds of commute PMT and VMT occur during weekday peak hours. There
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is considerable regional variation. Commute distances are longest in the most and least populous areas of the state (i.e., the Atlanta MPO and non-MPO counties). Work Travel Distance by Mode focuses on work travel duration. Workers' median "usual" commute duration is 25 minutes. However, some workers must travel much longer. On a typical day, 12.3 percent of active commuters will spend a total of 2 hours or more traveling to and from work. Commute durations are greatest in the Atlanta MPO. The Atlanta MPO also experiences the greatest variability by time of day.
Commute Duration and Burden discusses the amount of time spent on individual commutes and the total amount of time spent on commuting per day (commute burden). Commute durations differ sharply by mode. The average auto commute lasts 31 minutes, versus 17 minutes for nonmotorized commutes. The average public transit commute lasts 70 minutes. However, captive riders' commutes are even slower than those of choice transit riders (i.e., commuters from vehicle-sufficient households). The average transit commute for a captive rider is 20.4 minutes longer than the average choice transit commute, despite the fact that the average distance of captive riders' commutes is 3.1 miles shorter than a choice rider's transit commute (14.2 miles vs. 17.3 miles). The data also suggest the presence of "captive" walkers and cyclists: 8.3 percent of nonmotorized commutes by workers without full access to vehicles last an hour or longer, versus just 0.7 percent of choice nonmotorized commutes. These findings suggest that Georgia's current commuting environment constitutes a two-tiered system divided not just by mode, but by the ability to choose between modes.
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OVERVIEW
Definitions and Technical Notes In general, a commute is travel for the purpose of getting to or from work. However, since some commuters make stops in between their home and workplace, more specific vocabulary is needed. A commuter is a person who travels from a home location to a different location for the purposes of paid employment. This chapter uses general data about the "usual" habits of commuters, and also travel diary data from a single day of travel. It refers to respondents who reported traveling to a workplace on their travel day as active commuters. The phrase "work journey" refers to all the trips and intermediate stops a commuter makes between home and work.26 A work journey is unidirectional (i.e., either from home to work, or from work to home). As a result, most active commuters will have two or more work journeys in a single day. "Circuit" refers to the full sequence of trips from home to work and back.
The home and work locations are anchors of the work journey, and any destinations between the two are considered stops. A simple work journey proceeds directly between home and work. A complex work journey contains at least one internal stop. For example, a commuter might stop to pick up a child from school, buy coffee, or shop for groceries on the way to or from work. A work journey might also include longer stops, such as a professional who is pursuing an advanced degree by taking classes after work.27
26 The phrase "work journey" is used rather than "tour" to avoid confusion with the alternate definition used by the NHTS in the tour files it provides to researchers.
27 In contrast to this classification, the NHTS classifies any stop of 30 minutes or more as an anchor in its own right. The reasons for departing from the NHTS classification are discussed further in chapter 2, Defining the Commute.
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A commute is the portion of the work journey that should be considered work-related travel. For a direct (simple) work journey, the work journey and the commute are identical. For a complex work journey, it is necessary to determine which portions of the journey should be attributed to the commute, and which should be considered as related to the purposes of the intermediate stops. The next section, Defining the Commute, will compare alternate methods of apportioning person miles traveled, vehicle miles traveled, and travel time between work and nonwork purposes. A technique the research team refers to as the counterfactual method is well-suited for a realistic portrayal of complex commutes. In this technique, the portion of a work journey assigned to the commute is based on what the commute would have looked like had the commuter gone directly from home to work with no intermediate stops.
The NHTS contains two types of data about work-related travel. The first consists of questions asked as part of the general questionnaire, such as "usual" commute mode, industry of employment, and flexibility of work schedule. Questions it also contains about telecommuting will be analyzed in chapter 3.
The second type of data is work trips made by respondents on their travel days. These travel diary data are used to provide more accurate mode shares, distances, and many other measures. Travel diary data are the basis for most analyses in this chapter. It should be noted that travel times are self-reported by survey respondents, but travel distances were calculated by the NHTS as a shortest path distance, rather than based on the route chosen by the traveler. Additionally, travel days ran from 4:00 a.m. through 3:59 a.m., with the result that night shift workers might actually begin or end their day at work. More details about how this report identifies work journeys and commutes within the trip diaries are available in chapter 2, Defining the Commute.
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Worker Characteristics Table 37 shows levels of workforce participation.28 Statewide, 61.5 percent of adults ages 18 and older are classified as workers by NHTS.29 Of these, 80.4 percent work full-time. Labor force participation is higher among men than women. Female workers are more likely to work parttime, and are also more likely to hold multiple jobs. Regional variations are also present. Workforce participation is highest in the Atlanta MPO region, and lowest in non-MPO counties.
Workforce participation is highest for members of Generation X (who were 3752 years old at the time of the survey). However, many Georgians (18.1 percent) continue to work past retirement age; 21.4 percent of younger seniors (ages 6579) are still working, as are 3.6 percent of Georgians ages 80 and older.
The dominant industry varies by region, gender, race, age, and income (table 38). Professional, managerial, and technical positions are the most common, both among workers overall and among all subpopulations except the lowest-income workers and those without a college degree. Sales and service is the largest sector for low-income and noncollege-educated workers, and the second largest for other groups. For men, the second-most common category is blue-collar jobs such as manufacturing, construction, maintenance, and farming. Blue-collar jobs are most common in non-MPO counties (tier 4), followed by small MPOs (tier 3). They are the least common in Atlanta MPO counties (tier 1).
28 For details on sample sizes for this and subsequent tables, see the appendix. As a reminder, unless otherwise stated, all statistics presented are weighted.
29 NHTS defines a worker as someone who worked for pay or profit, or was temporarily absent from paid employment, in the week before completing the travel survey ("last week"). All references to "workers" in this section refer to NHTS-defined workers.
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Table 37. Workforce participation.

Adults

Workers Only

All adults Gender Male Female MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO Age Cohort Millennial and Gen Z (1836) Generation X (3752) Pre-retirement age Baby Boomer (5364) Retirement age (65+) Annual Household Income <$35,000 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999

Percent Workers*
61.5%

Full-time 80.4%

More Than Part-time One Job

19.6%

9.1%

69.0%

85.4%

14.6%

8.4%

54.6%

74.4%

25.6%

9.8%

65.6%

81.5%

18.5%

8.8%

60.8%

76.4%

23.6%

8.9%

61.0%

79.3%

20.7%

9.4%

51.0%

80.7%

19.3%

9.9%

68.6%

74.5%

25.5%

8.9%

79.0%

88.8%

11.2%

9.7%

60.8%

83.4%

16.6%

9.1%

18.1%

50.2%

49.8%

5.3%

46.9% 63.2% 66.3% 69.1%

68.7% 77.6% 84.9% 87.3%

31.3% 22.4% 15.1% 12.7%

11.3% 12.8%
9.8% 7.1%

$100,000+

75.0%

85.7%

14.3%

Race

White non-Hispanic only

62.5%

80.7%

19.3%

Black and Black multiracial

59.5%

79.7%

20.3%

Other

61.9%

80.3%

19.7%

* Percentage of all cases in row that work. See the appendix for unweighted sample sizes.

As defined by NHTS, a worker is someone who worked for pay or profit or was temporarily absent from paid

employment in the week before completing the travel survey. Both full-time and part-time workers may have more than one job.

6.0%
8.2% 11.7%
6.6%

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Table 38. Job categories of Georgia workers.

Occupation* Manufacturing,

Clerical or

Construction, Professional,

Sales or Administrative Maintenance, or Managerial, or

Service

Support

Farming

Technical

All workers ages 18+

26.9%

9.3%

17.7%

46.0%

Gender

Male

24.9%

2.8%

27.7%

44.6%

Female

29.2%

16.9%

6.1%

47.6%

MPO Tier

1. Atlanta MPO

26.8%

9.6%

14.1%

49.4%

2. Medium-size MPOs

27.9%

9.7%

17.1%

45.0%

3. Small MPOs

27.5%

9.3%

21.6%

41.5%

4. Non-MPO

25.8%

7.9%

28.7%

37.4%

Age Cohort

Millennial and Gen Z (1836)

34.8%

8.0%

19.1%

38.1%

Generation X (3752)

20.5%

9.5%

16.8%

53.1%

Pre-retirement age Baby Boomer (5364) 22.7%

9.7%

18.0%

49.3%

Retirement age (65+)

30.3%

16.0%

12.9%

40.7%

Annual Household Income

<$35,000

38.8%

10.6%

27.3%

23.1%

$35,000 to $49,999

30.2%

10.8%

23.8%

34.7%

$50,000 to $74,999

24.5%

12.3%

19.2%

43.9%

$75,000 to $99,999

24.8%

9.1%

12.7%

53.4%

$100,000+

17.9%

5.9%

8.0%

68.2%

Race

White non-Hispanic only

24.9%

8.5%

15.3%

51.3%

Black and Black multiracial

30.6%

11.5%

20.5%

37.2%

Other

26.7%

7.9%

21.7%

43.7%

Education Level

High school or less

38.3%

6.7%

38.4%

16.4%

Associates degree or some college

32.1%

14.2%

19.8%

33.7%

Bachelor's degree or higher

16.9%

7.4%

4.9%

70.8%

* All workers were limited to a single self-reported occupation. Of workers, 0.1 percent reported their occupation as "other."

Note: NHTS defines a worker as someone who worked for pay or profit or was temporarily absent from paid employment in

the week before completing the travel survey ("last week").

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Workers in professional, technical, and managerial professions tend to be the wealthiest, with a median household income of $75,000$99,999.30 Clerical and administrative workers have a median household income of $50,000$74,999. Workers in the remaining two categories (sales/service and blue-collar) are the least well off, with a median household income of $35,000$49,999.
Much of the variation in industry across gender, region, and race is related to differences in educational attainment. As shown in figure 8 and table 38, 71 percent of workers in the highest income households hold at least a bachelor's degree, and 68 percent of workers in these households work in the professional sector. In contrast, only 20 percent of workers in the lowestincome households hold a bachelor's degree, and only 23 percent work in the professional sector.
As shown in table 39, differences in occupation category by gender, region, and race are much smaller among college-educated workers. For example, the proportion of college-educated men who work in blue-collar positions is 5 percentage points higher than the proportion of collegeeducated women who do so. For noncollege graduates, this difference is 32 percentage points. The occupational categories of college graduates are broadly similar across all MPO tiers, though blue-collar professions are slightly less common in Atlanta, and sales and service jobs are more common.
30 No individual-level salary data are available, so these figures represent the combined income of all household members. Additionally, income is provided as a categorical variable, so the median is also presented as a category.
90

Percent of Workers With a Bachelor's Degree or Higher

80%

70%

60% 50% 40%

48% 42%

30%

20%

10%

0%

53% 42% 31% 26%

71%
52% 39% 32% 20%

50% 43%
36%

Men Women MPO Tier 1 MPO Tier 2 MPO Tier 3 MPO Tier 4 <$35,000 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+ White non-Hispanic Black and black multiracial
Other

Figure 8. Bar graph. Educational attainment of Georgia workers by gender, MPO tier, household income, and race.
However, a college degree notably fails to erase a racial difference in employment category: the proportion of Black31 college-educated workers in professional, managerial, and technical positions is 10 percentage points lower than the proportion of white non-Hispanic college educated workers, and 11 percentage points lower than college-educated workers of another race. This difference is larger than the difference between white and Black workers without a college degree.

31 This report uses "Black" as an umbrella term to include African Americans and Black immigrants.
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Table 39. Job categories of Georgia workers by educational attainment.

College Graduate Workers (Bachelor's Degree or Higher) Manufacturing,

Clerical or Construction, Professional,

Sales or Administrative Maintenance, Managerial, or

Service

Support

or Farming

Technical

All college-educated

workers

16.9%

7.4%

4.9%

70.8%

Gender

Male

17.4%

3.6%

7.4%

71.6%

Female

16.4%

11.3%

2.3%

70.0%

MPO Tier

1. Atlanta MPO

18.2%

7.3%

3.8%

70.7%

2. Medium MPOs

14.4%

7.6%

7.6%

70.3%

3. Small MPOs

11.7%

8.0%

6.0%

74.3%

4. Non-MPO

15.1%

7.4%

7.7%

69.8%

Race

White non-Hispanic only

16.9%

5.8%

4.0%

73.3%

Black and Black multiracial

18.4%

11.7%

7.1%

62.7%

Other

13.8%

7.1%

5.0%

74.1%

Workers Without 4-Year College Degree

Manufacturing,

Clerical or Construction, Professional,

Sales or Administrative Maintenance, Managerial, or

Service

Support

or Farming

Technical

All workers without 4-year

college degree

34.8%

10.9%

28.0%

26.1%

Gender

Male

30.2%

2.2%

42.3%

25.0%

Female

40.7%

22.0%

9.6%

27.5%

MPO Tier

1. Atlanta MPO

36.3%

12.3%

25.5%

25.7%

2. Medium MPOs

37.9%

11.2%

24.1%

26.3%

3. Small MPOs

34.6%

9.9%

28.7%

26.8%

4. Non-MPO

29.5%

8.0%

35.8%

26.4%

Race

White non-Hispanic only

32.8%

11.2%

26.5%

29.5%

Black and Black multiracial

37.3%

11.3%

28.0%

22.9%

Other

35.9%

8.6%

33.9%

21.5%

Note: NHTS defines a worker as someone who worked for pay or profit or was temporarily absent from paid

employment in the week before completing the travel survey ("last week").

92

Overview of Commuters Table 40 shows the percentage of workers who reported at least one work journey on their travel day.32 This report defines these workers as active commuters. On an average weekday, 70 percent of NHTS-defined workers and 44 percent of the total adult population reported making one or more work journeys. The weekday proportion of active commuters for full-time workers was 77 percent, compared to 54 percent of part-time workers. Approximately one in five workers surveyed on a weekend or holiday commuted on that day. Weekend commuting was more common among service and blue-collar workers than clerical and professional workers, and among groups that are disproportionately employed in the service and blue-collar sectors (e.g., Black and low-income workers). It was also more common among part-time workers. Relatedly, weekend commutes are more common in non-MPO counties, which have a relatively high concentration of blue-collar jobs, than in other parts of the state.
32 The travel day ran from 4:00 a.m. through 3:59 a.m. the next day. A small number of people who were not classified as workers also reported work journeys; this group made up 1.3 percent of active commuters. Some of these are likely due to temporary employment, and others may represent a change in worker status for the participant between completing the main questionnaire and the travel diary. These journeys are not included in the percentage of workers reporting work journeys, but they will be included in per-capita and per-worker trip generation rates (for example, table 41 and table 42).
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Table 40. Active commuter rates.

All Adults

Workers Only*

Weekends

Weekends

Weekdays & Holidays

Weekdays & Holidays

All Days Only

Only All Days Only

Only

All adults

34.4%

43.8%

13.0%

55.2%

70.0%

21.0%

Gender

Male

40.3%

50.4%

16.2%

57.8%

72.1%

23.1%

Female

28.9%

37.5%

10.3%

52.2%

67.6%

18.8%

MPO Tier

1. Atlanta MPO

36.3%

46.3%

11.6%

54.8%

69.0%

18.3%

2. Medium-size MPOs

32.3%

43.3%

13.7%

52.3%

70.7%

21.6%

3. Small MPOs

33.4%

42.8%

16.7%

53.9%

71.8%

24.7%

4. Non-MPO

31.3%

37.8%

13.7%

60.1%

72.2%

27.0%

Age Cohort

Millennial and Gen Z (1836)

40.4%

48.3%

20.4%

58.4%

69.5%

29.7%

Generation X (3752)

43.0%

58.5%

11.4%

53.9%

72.0%

14.7%

Pre-retirement age Baby Boomer (5364)

33.4%

43.4%

11.5%

54.1%

70.2%

18.8%

Retirement age (65+)

8.6%

10.6%

3.8%

44.7%

59.3%

17.7%

Annual Household Income

<$35,000

28.5%

33.8%

15.8%

59.3%

69.5%

33.6%

$35,000 to $49,999

36.9%

46.7%

15.1%

58.1%

75.4%

22.6%

$50,000 to $74,999

38.4%

46.9%

20.1%

57.8%

71.0%

29.9%

$75,000 to $99,999

41.9%

53.9%

8.3%

59.3%

74.8%

12.4%

$100,000+

36.5%

50.8%

6.6%

48.3%

66.8%

8.9%

Race

White non-Hispanic only

33.3%

44.1%

10.4%

52.7%

69.8%

16.6%

Black and Black multiracial

35.5%

42.1%

18.3%

58.6%

69.3%

30.1%

Other

36.2%

46.3%

13.1%

58.1%

73.0%

21.9%

Occupational Category (Workers Only)*

Sales or service

57.7%

66.7%

37.0%

Clerical or administrative support

55.6%

74.7%

11.4%

Manufacturing, construction, maintenance, or farming

64.0%

80.5%

23.6%

Professional, managerial, or technical

54.1%

71.9%

14.3%

Worker Type (Workers Only)*

Full time

60.0%

76.8%

21.0%

Part time

44.6%

53.9%

24.2%

* Excludes the 1.3% of active commuters who were not defined as workers by NHTS. NHTS defines a worker as someone who worked for pay or profit or was temporarily absent from paid employment in the week before completing the travel survey ("last week").

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Table 41 shows the complexity of commutes made by various groups of workers. Overall, 39.6 percent of active commuters made at least one complex commute (with an interim stop). Three quarters of those who made a complex commute also made at least one simple commute, e.g., a simple commute in one direction and a complex commute in the other. Trip chaining is less common on weekends: 68.4 percent of weekend commuters made only simple work journeys, as opposed to 59.4 percent of weekday commuters.33
The prevalence of complex commutes is comparable for all MPO tiers, and across income levels. There are some differences across age groups, with younger workers being more likely to make only simple work journeys. Consistent with previous findings about gendered travel patterns (McQuaid and Chen 2012, Loukaitou-Sideris 2016), female commuters are more likely to have trip chained. Black commuters are also more likely to make complex work journeys than commuters of other races.
Of active commuters, 88 percent reported exactly two work journeys (table 42). Close to 7 percent reported one work journey. Participants who reported an odd number of work journeys typically started or ended their travel day away from home; 74 percent of these started or ended at work. The next most common nonhome location to start or end the day was visiting friends/relatives. These "odd" commuters, then, are mainly people who work night shifts or extended shifts, with a minority of people who stay out late after work and the occasional air traveler who is midtrip when the travel day ends.
33 The sample size of the weekend commuters category was too small to disaggregate further, so that group is not separately cross-tabulated in the tables presented here. For unweighted sample sizes, see the appendix.
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Table 41. Work journey complexity among active commuters.

All Days

Weekdays

All active commuters Gender Male Female MPO Tier 1. Atlanta MPO 2. Medium-size MPOs 3. Small MPOs 4. Non-MPO Age Cohort Millennial and Gen Z (1836) Generation X (3752) Pre-retirement age Baby Boomer (5364) Retirement age (65+) Annual Household Income <$35,000 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+ Race White non-Hispanic only Black and Black multiracial Other

Simple Only

Both Complex Simple &
Only Complex

Simple Only

Both Complex Simple &
Only Complex

60.4%

10.1%

29.5%

59.4%

10.5%

30.1%

65.1% 54.4%

8.5% 12.1%

26.4% 33.5%

64.6% 52.8%

8.7% 12.7%

26.7% 34.5%

60.5% 61.1% 60.7% 59.5%

10.3% 9.5% 8.8%
10.6%

29.2% 29.4% 30.4% 29.9%

60.0% 58.5% 59.5% 58.1%

10.6% 10.1% 10.5% 10.3%

29.4% 31.4% 30.0% 31.6%

63.7% 59.1% 56.3% 59.7%

9.1% 10.6% 11.1% 10.5%

27.1% 30.3% 32.6% 29.8%

62.5% 58.4% 55.7% 57.0%

10.5% 10.2% 10.6% 11.8%

27.0% 31.3% 33.7% 31.2%

60.9% 58.0% 60.9% 60.9% 60.3%

10.6% 9.0%
10.8% 8.5%
10.0%

28.6% 33.0% 28.3% 30.6% 29.7%

61.0% 57.0% 57.4% 59.6% 59.9%

11.2% 8.8%
11.6% 8.7%
10.4%

27.9% 34.2% 30.9% 31.7% 29.7%

62.2% 55.5% 65.3%

9.0% 13.9%
5.4%

28.9% 30.5% 29.3%

61.3% 54.2% 63.7%

9.5% 14.5%
5.3%

29.2% 31.3% 31.1%

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Table 42. Number of work journeys per person, active commuters.

All Days

Weekdays

More Than

More Than

One WJ Two WJs Two WJs One WJ Two WJs Two WJs

All adults

6.9% 88.1%

5.0%

6.6% 88.2%

5.3%

Gender

Male

8.1% 86.3%

5.6%

7.5% 86.6%

5.9%

Female

5.4% 90.4%

4.3%

5.4% 90.1%

4.4%

MPO Tier

1. Atlanta MPO

5.8% 89.8%

4.3%

5.5% 90.1%

4.4%

2. Medium MPOs

7.3% 85.8%

6.9%

7.1% 85.2%

7.7%

3. Small MPOs

8.3% 85.4%

6.4%

8.3% 84.9%

6.8%

4. Non-MPO

9.0% 85.9%

5.1%

8.8% 86.0%

5.3%

Age Cohort

Millennial and Gen Z (1836)

5.9% 88.9%

5.3%

5.7% 88.6%

5.7%

Generation X (3752)

7.6% 88.5%

3.9%

7.4% 88.6%

4.1%

Pre-retirement age Baby Boomer (5364)

7.7% 86.2%

6.1%

6.8% 87.1%

6.0%

Retirement age (65+)

6.5% 86.0%

7.4%

6.4% 85.0%

8.5%

Annual Household Income

<$35,000

8.0% 86.2%

5.8%

8.5% 85.2%

6.2%

$35,000 to $49,999

4.6% 87.7%

7.7%

4.3% 86.9%

8.8%

$50,000 to $74,999

8.8% 86.7%

4.4%

7.6% 88.0%

4.5%

$75,000 to $99,999

5.4% 89.0%

5.6%

4.8% 89.5%

5.7%

$100,000+

6.1% 90.6%

3.3%

5.8% 91.0%

3.2%

Race

White non-Hispanic only

6.5% 88.7%

4.8%

6.0% 88.9%

5.1%

Black and Black multiracial

8.7% 86.9%

4.4%

8.8% 87.1%

4.1%

Other

4.2% 88.4%

7.4%

3.8% 88.0%

8.3%

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Commute Mode by Person As shown in table 43, personal occupancy vehicles (whether singly-occupied or carrying passengers) are the self-reported usual commute mode for 93.2 percent of workers statewide.34 There is considerable regional variation; "only" 91.1 percent of Atlanta MPO residents commute by car, while in non-MPO counties, the use of private autos is almost universal. Unsurprisingly, transit usage is highest in Atlanta, where there are more robust transit offerings. Transit usage is also higher among women than men, and among low-income and Black workers.
A comparison of these figures with the modes actually used for work journeys on the travel day (table 44) suggests that Georgia residents' commute patterns are, unsurprisingly, slightly more complicated than reporting a single "usual" commute mode suggests. For instance, the percent of commuters reporting making a nonmotorized trip as part of a work journey is 1.6 times higher than the percent reporting nonmotorized travel as their "usual" work mode.35 Interestingly, more men than women reported nonmotorized means as their usual mode, but when trips actually made on the travel day are examined, this apparent gender gap disappears.
34 Specifically, participants were asked to report how they "usually" got to their main job last week. If multiple modes were used, they were instructed to select the one that they used for a longer distance. This question was not asked of people who reported that they usually telecommute, though it was asked of workers who telecommute some days. Most periodic telecommuters, like most workers in general, selected private auto as their usual commute mode. This question will be revisited in chapter 3.
35 Although the "usual" modes were provided by all commuters while the travel day modes were reported only by active commuters, there was no appreciable difference between the "usual" commute mode of active commuters and inactive commuters.
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Table 43. Usual commute mode as reported by NHTS-defined workers.

All workers

POV*

Nonmotorized (Walking,
Biking)

Public Transit or Other Bus
or Train

Other Ground or
Water

93.2%

2.3%

3.8%

0.4%

Gender

Male

93.8%

2.7%

2.6%

0.5%

Female

92.5%

1.8%

5.3%

0.3%

MPO Tier

1. Atlanta MPO

91.1%

2.2%

5.9%

0.4%

2. Medium MPOs

94.2%

3.4%

1.8%

0.5%

3. Small MPOs

96.8%

1.5%

1.4%

0.3%

4. Non-MPO

97.0%

2.0%

0.4%

0.3%

Age Cohort

Millennial and Gen Z (1836)

91.9%

2.8%

4.3%

0.5%

Generation X (3752)

94.2%

2.1%

3.2%

0.3%

Pre-retirement age Baby Boomer (5364)

93.6%

1.5%

4.1%

0.3%

Retirement age (65+)

95.3%

2.2%

2.5%

Annual Household Income

<$35,000

88.6%

4.1%

7.1%

0.3%

$35,000 to $49,999

94.2%

2.2%

3.1%

0.5%

$50,000 to $74,999

96.4%

1.4%

1.5%

0.5%

$75,000 to $99,999

95.3%

1.4%

2.9%

0.0%

$100,000+

94.3%

1.8%

2.9%

0.3%

Race

White non-Hispanic only

95.3%

2.4%

1.7%

0.2%

Black and Black multiracial

90.1%

1.5%

7.8%

0.4%

Other

91.8%

3.4%

3.4%

1.0%

* Including privately owned, rental, and company vehicles.

Includes all workers, whether or not they reported a work journey on their travel day.

Air 0.3%
0.4% 0.2%
0.4% 0.2% 0.1% 0.3%
0.3% 0.2% 0.5%
0.3% 0.5% 0.7%
0.4% 0.1% 0.5%

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Table 44. Travel day commute mode(s) for active commuters.

Non-

Public

motorized Transit or Other

(Walking, Other Bus Ground or

POV*

Biking)

or Train

Water

Air

All active commuters

95.0%

3.7%

3.8%

1.4%

0.2%

Gender

Male

95.1%

3.6%

2.9%

1.5%

0.2%

Female

94.9%

3.8%

5.0%

1.2%

0.4%

MPO Tier

1. Atlanta MPO

93.6%

4.4%

6.1%

1.5%

0.4%

2. Medium MPOs

96.4%

4.2%

1.5%

0.9%

3. Small MPOs

98.3%

2.3%

0.6%

0.6%

0.0%

4. Non-MPO

96.5%

1.8%

0.4%

1.8%

0.2%

Age Cohort

Millennial and Gen Z (1836)

94.0%

4.5%

4.8%

1.9%

0.1%

Generation X (3752)

96.1%

3.1%

2.8%

1.1%

0.5%

Pre-retirement age Baby Boomer (5364)

95.0%

3.1%

3.9%

1.3%

0.1%

Retirement age (65+)

95.6%

3.6%

3.1%

Annual Household Income

<$35,000

90.8%

6.7%

6.2%

2.1%

0.7%

$35,000 to $49,999

96.6%

2.6%

4.0%

1.6%

0.9%

$50,000 to $74,999

97.6%

1.5%

1.5%

1.1%

0.6%

$75,000 to $99,999

96.4%

2.4%

3.2%

0.6%

0.2%

$100,000+

96.4%

3.2%

3.5%

1.0%

0.4%

Race

White non-Hispanic only

96.6%

3.0%

1.8%

0.8%

0.1%

Black and Black multiracial

92.6%

3.7%

7.4%

2.5%

0.3%

Other

94.8%

6.1%

3.4%

1.2%

0.6%

* Including privately owned, rental, and company vehicles. Excludes taxis and ridehailing, which are included under Other Ground

or Water.

Note: Of participants, 3.6 percent reported using multiple modes, and are included in totals for all relevant modes.

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Table 45 shows rates of driving alone (single-occupancy vehicle [SOV] trips) among active commuters. Of active commuters, 64 percent drove alone for all the trips that made up the work journeys on their travel day. Only 15 percent drove alone for some trips, and the remainder either were in a car with multiple people or used a different mode. Twenty-two percent of active commuters did not spend any portion of their work journeys driving alone. Driving alone is more common among men than women and is most common in small MPOs. Younger workers drive alone less than older ones, as do lower-income and nonwhite workers. Overview of Work Journeys In chapter 1, the sections on Trip Patterns by Location of Travel and Household and Personal Mobility provided statistics summarized by commuter. This section provides summary statistics about the individual work journeys. Georgia residents make nearly 2 billion work journeys each year, or an average of 415 per adult worker.36 Figure 9 shows the temporal distribution of these work journeys based on the time of arrival at or departure from the work anchor. Seventy percent of all work journeys take place between 6:009:59 a.m. or 3:006:59 p.m.; this report defines these two time windows as peak periods.
36 Per-worker statistics are calculated based on total work journeys from all adults divided by total NHTSdefined workers. The numerator includes the 1.2 percent of work journeys that are made by "nonworkers," likely people who are working short-term or sporadic jobs (for example, some farm work).
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Table 45. Use of single-occupancy vehicles, high-occupancy vehicles (HOVs), and alternate modes among active commuters.

All SOV

Some SOV Trips No SOV Trips

All active commuters Gender Male Female MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO Age Cohort Millennial and Gen Z (1836) Generation X (3752) Pre-retirement age Baby Boomer (5364) Retirement age (65+) Annual Household Income <$35,000 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+ Race White non-Hispanic only Black and Black multiracial Other * Transit, walking, etc.

All WJ Segments by
SOV

SOV + HOV or HOV or HOV + SOV + Alt. Mode* Alt. Mode

All Trips by Alt. Mode

63.6%

14.6%

16.6%

5.2%

65.7% 60.9%

12.8% 16.9%

16.4%

5.0%

16.7%

5.5%

61.8% 63.8% 69.4% 65.9%

15.5% 14.9% 14.2% 11.7%

15.9%

6.7%

17.7%

3.6%

14.7%

1.7%

18.8%

3.7%

61.9% 62.6% 68.2% 66.9%

13.0% 18.5% 11.0% 14.1%

19.1%

6.0%

14.5%

4.4%

15.6%

5.1%

14.7%

4.4%

53.2% 63.5% 71.7% 67.4% 66.9%

14.0% 13.1% 11.8% 14.2% 18.8%

23.7%

9.2%

20.0%

3.4%

13.5%

2.9%

14.5%

3.9%

10.3%

4.0%

68.3% 58.0% 58.5%

14.0% 14.5% 17.3%

14.1%

3.6%

19.8%

7.7%

18.5%

5.7%

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12.5% 10.1%

12.0% 10.1%

6.7%
2.7% 0.6%0.2%0.3%0.1%0.9%

6.1%

7.0%

4.1% 3.1%2.4%2.7%3.2%

6.1%
3.2% 1.6%1.4%1.7%1.4%

Dark purple bars indicate peak periods. Figure 9. Histogram. Work journeys by time of arrival to
or departure from work (weighted).
As table 46 shows, 92.7 percent of work journeys are by POV, and in 68.5 percent of cases, the commuter drove alone for the entire work journey. Table 47, table 48, and table 49 show the same information broken down by MPO tier. Interestingly, the annual number of work journeys per worker is substantially higher in non-MPO counties than in any other region (i.e., 451 work journeys per worker versus 404410 in tiers 13). One likely contributing factor to this difference is that multiple-job holding is highest in non-MPO counties (table 37). Additionally, adults who are not workers produce an average of 9.9 work journeys per nonworker in tier 4 counties, versus 7.0 work journeys per nonworker in tiers 1 and 8.6 in tiers 2 and 3 (not tabulated).

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Table 46. Total annual work journeys in Georgia.

Total (Millions)

All Days Percent of Total WJs

Per Worker*

Weekdays Only Total Percent of Per (Millions) Total WJs Worker*

Total work journeys

1,967.3

415.4

1,746.4

368.7

Simple work journeys

1,490.3

75.8%

314.7

1,311.9

75.1%

277.0

Complex work journeys

477.0

24.2%

100.7

434.4

24.9%

91.7

Mode

POV

1,824.5

92.7%

385.2

1,618.4

92.7%

341.7

Multimodal with POV

16.6

0.8%

3.5

16.5

0.9%

3.5

Other

125.9

6.4%

26.6

111.2

6.4%

23.5

Nonmotorized

45.3

2.3%

9.6

42.2

2.4%

8.9

Public transit or other bus/train

50.7

2.6%

10.7

43.4

2.5%

9.2

Other ground or water

18.4

0.9%

3.9

15.0

0.9%

3.2

Multimodal without POV or air

9.1

0.5%

1.9

8.1

0.5%

1.7

Air or air multimodal

2.4

0.1%

0.5

2.4

0.1%

0.5

Driver Status (POV and Multimodal POV WJs)

Driver for entire WJ

1,687.8

85.8%

356.4

1,502.1

86.0%

317.2

Driver for part of WJ

22.0

1.1%

4.6

21.1

1.2%

4.5

Passenger

133.8

6.8%

28.3

114.1

6.5%

24.1

Vehicle Occupancy Status (POV and Multimodal POV WJs)

Drive alone entire WJ

1,347.6

68.5%

Family sharing: drive with

household passenger for 1+ legs

165.7

8.4%

Carpool driver: drive with nonhousehold passenger for 1+ legs Time of Day

212.8

10.8%

284.5 35.0 44.9

1,188.7 158.8 189.5

68.1%

251.0

9.1%

33.5

10.9%

40.0

AM peak (6 am9:59 am)

693.0

35.2%

146.3

632.1

36.2%

133.5

Midday (10 am2:59 pm)

310.3

15.8%

65.5

264.8

15.2%

55.9

PM peak (3 pm6:59 pm)

690.4

35.1%

145.8

623.3

35.7%

131.6

Overnight (7 pm6:59 am)

273.6

13.9%

57.8

226.2

* Based on total population of workers ages 18+.

Excludes multimodal trips.

Based on time of arrival at or departure from work anchor.

13.0%

47.8

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Table 47. Work journeys by MPO tier (all days).

Annual Total, Millions

Per Worker*

Tier 1 Tier 2 Tier 3 Tier 4 Tier 1 Tier 2 Tier 3 Tier 4

Total work journeys

1,119.8 299.3 194.5 353.7 409.4 403.5 410.0 450.7

Simple work journeys

845.1 229.8 148.7 266.6 309.0 309.7 313.6 339.8

Complex work journeys

274.6 69.6 45.7 87.1 100.4 93.8 96.4 111.0

Mode

POV

1,010.9 284.9 189.7 339.1 369.6 384.1 399.9 432.1

Multimodal with POV

12.4 2.9 0.9 0.4 4.6 3.9 1.9 0.5

Other

96.5 11.3 3.9 14.3 35.3 15.2 8.2 18.2

Nonmotorized

28.9 7.2 2.1 7.1 10.6 9.7 4.4 9.1

Public transit or other bus/train

46.4 2.2 0.8 1.3 17.0 2.9 1.7 1.6

Other ground or water

10.5 1.8 0.5 5.6 3.8 2.4 1.1 7.1

Multimodal without POV or air

8.5 0.1 0.4

3.1 0.2 0.9

Air or air multimodal

2.0

0.3 0.7

0.4

Driver Status (POV and Multimodal POV WJs)

Driver for entire WJ

945.5 256.3 174.9 311.1 345.7 345.5 368.7 396.4

Driver for part of WJ

12.8 5.8 1.7 1.6 4.7 7.9 3.5 2.0

Passenger

67.0 25.6 14.1 27.0 24.5 34.6 29.7 34.5

Vehicle Occupancy Status (POV and Multimodal POV WJs)

Drive alone entire WJ

747.3 203.8 145.4 251.0

Family sharing: drive with household

passenger for 1+ legs

89.8 24.8 16.2 34.8

Carpool driver: drive with non-

household passenger for 1+ legs

127.7 36.6 16.6 32.0

Time of Day

273.2 32.8 46.7

274.7 33.5 49.3

306.6 34.2 34.9

319.8 44.4 40.7

AM peak (6 am9:59 am)

406.8 101.4 65.6 119.1 148.7 136.7 138.4 151.8

Midday (10 am2:59 pm)

170.7 52.6 31.0 56.0 62.4 70.9 65.5 71.4

PM peak (3 pm6:59 pm)

394.2 102.8 67.1 126.3 144.1 138.5 141.5 161.0

Overnight (7 pm6:59 am)

148.2 42.5 30.7 52.2 54.2 57.3 64.7 66.6

* Based on total population of workers ages 18+.

Excludes multimodal trips. Based on time of arrival at or departure from work anchor.

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Table 48. Work journeys by MPO tier (weekdays only).

Annual Total, Millions

Per Worker*

Tier 1 Tier 2 Tier 3 Tier 4 Tier 1 Tier 2 Tier 3 Tier 4

Total work journeys

1,019.5 254.8 159.1 313.0 372.8 343.5 335.4 398.8

Simple work journeys

765.1 191.9 119.4 235.6 279.7 258.7 251.6 300.2

Complex work journeys

254.4 62.9 39.7 77.4 93.0 84.8 83.7 98.7

Mode

POV

919.9 243.8 154.5 300.3 336.3 328.7 325.7 382.6

Multimodal with POV

12.4 2.8 0.9 0.4 4.6 3.8 1.9 0.5

Other

87.2 7.9 3.7 12.4 31.9 10.6 7.8 15.8

Nonmotorized

28.9 4.3 1.9 7.1 10.6 5.8 4.0 9.1

Public transit or other bus/train

39.1 2.2 0.8 1.3 14.3 2.9 1.7 1.6

Other ground or water

9.6 1.3 0.5 3.6 3.5 1.7 1.1 4.6

Multimodal without POV or air

7.6 0.1 0.4

2.8 0.2 0.9

Air or air multimodal

2.0

0.0 0.3 0.7

0.4

Driver Status (POV and Multimodal POV WJs)

Driver for entire WJ

866.0 221.6 140.4 274.1 316.6 298.7 296.0 349.3

Driver for part of WJ

12.8 5.2 1.5 1.6 4.7 7.0 3.2 2.0

Passenger

55.5 19.9 13.5 25.2 20.3 26.9 28.4 32.1

Vehicle Occupancy Status (POV and Multimodal POV WJs)

Drive alone entire WJ

679.7 175.3 116.2 217.6

Family sharing: drive with household

passenger for 1+ legs

86.6 22.9 14.4 34.8

Carpool driver: drive with non-

household passenger for 1+ legs

117.8 31.1 12.2 28.4

Time of Day

248.5 31.7 43.1

236.2 30.8 42.0

244.9 30.4 25.7

277.2 44.4 36.2

AM peak (6 am9:59 am)

377.4 88.9 56.5 109.3 138.0 119.8 119.2 139.3

Midday (10 am2:59 pm)

149.7 42.9 24.4 47.7 54.7 57.9 51.5 60.8

PM peak (3 pm6:59 pm)

364.9 88.5 56.7 113.2 133.4 119.3 119.5 144.2

Overnight (7 pm6:59 am)

127.5 34.5 21.4 42.8 46.6 46.5 45.2 54.5

* Based on total population of workers ages 18+.

Excludes multimodal trips.

Based on time of arrival at or departure from work anchor.

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Table 49. Work journey mode, vehicle occupancy status, and time of day by MPO tier (percentages).
Work travel shares the roads (and sidewalks and rails) with nonwork travel. Figure 10 and figure 11 show the total number of trips and vehicle trips in progress at each hour of the day. A
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trip from 9:3010:30 a.m., for example, would be in progress during both the 9:009:59 a.m. and the 10:0010:59 a.m. periods. The figures show that commute trips dominate peak-hour travel while noncommute trips dominate mid-day trips.
Figure 10. Stacked bar graph. Weekday trips in progress by time of day (all ages, weighted).
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Figure 11. Stacked bar graph. Weekday vehicle trips in progress by time of day (weighted).
DEFINING THE COMMUTE What is a commute? When the U.S. Department of Transportation (USDOT) began to conduct large national surveys with the 1969 Nationwide Personal Transportation Survey, the answer to this question may have seemed self-evident. More than 50 years later, American's journeys to and from work have become more complicated. An influx of women to the workforce and the growing dominance of the dual-worker family has increased the numbers of workers on the road; many working parents (especially mothers) incorporate dropping children off at school into their daily commute (Gimenez-Nadal and Molina 2016; McGuckin, Zmud, and Nakamoto 2005).
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More people work multiple jobs, or balance employment and higher education (McFarland et al. 2019). Less-seismic shifts, such as the so-called "Starbucks effect," have added a morning coffee stop to many commutes (McGuckin, Zmud, and Nakamoto 2005).
These complex journeys to work, sometimes referred to as chained or tour commutes, have become more common over the past few decades (ibid.). Commute complexity has been analyzed in terms of mode choice, congestion, sustainability, and demographic differences such as gender (Concas and Winters 2007; McGuckin, Zmud, and Nakamoto 2005; Paleti, Bhat, and Pendyala 2013; Zhu et al. 2018).
As commuters' journeys to and from work become more complex, it is important to make sure that the tools used to measure work travel can provide accurate, meaningful data about work journeys that do not fit the traditional mold of a single, uninterrupted trip between the home and the workplace.
However, there is a lack of consensus on how to measure complex commutes. Researchers focusing on complexity often consider the full distance traveled between home and work (Paleti, Bhat, and Pendyala 2013; Zhu et al. 2018). The default measurement of work travel provided by the NHTS, in contrast, defines work travel based on the last trip in the chain (McGuckin and Fucci 2018). Researchers would benefit from having a more systematic measure of what portion of the travel between home and work should be considered work-related.
This section identifies some critical definitional and measurement issues associated with commute travel, especially for complex work journeys. It then compares different techniques for
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resolving these issues, and identifies best practices that will be incorporated into the analysis for the upcoming sections on Work Travel Distance and Work Travel Distance by Mode. First, we consider the entire work journey, including all intermediate stops. We discuss the importance of defining commute anchors by both purpose and location, rather than just purpose (which is the default for NHTS). Having identified whole work journeys, we then compare two alternate measures for determining what portion of each journey should be counted as commute distance (measured in PMT): (1) using the last leg of the journey, the default method when analyzing the NHTS trip file; and (2) modeling a counterfactual simple commute to estimate the distance that would have been traveled had no stops been made. Georgians make 2 billion journeys to work each year. As shown in figure 12, two thirds of these follow the pattern of a traditional commute: straight from the respondent's home location to the respondent's work location (or vice versa) with no stops in between.37 The remaining work journeys include at least one intermediate stop, involve a work or home site that differs from the respondent's home or work address, or both.
37 This figure does not include trips with the stated purpose of working from home.
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Figure 12. Pie chart. Proportions of traditional and nontraditional commutes in Georgia.
Given the complexity of Georgians' actual work-related travel, the first step in measuring commuting habits is defining what a commute is. If an office worker stops to shop for groceries and pick up a child on the way home from work, what portion of those trips should be considered part of the work commute? If a manager at a construction company travels from her home to a construction site where her employees are working and then proceeds to her company's central office, where did her commute end? If a teleworker goes to a coffee shop or a co-working space, is his trip a commute at all?
These philosophical issues are at the heart of two practical questions that must be answered in order to measure work travel. First, what destinations and purposes should count as "home" and "work" anchors for a commute? Second, when a work journey between a home and work anchor includes intermediate stops, what portion of this travel should be considered as part of the commute? We will argue that the NHTS's traditional answers to both these questions need to be adjusted to more accurately reflect the modern commute. We will first examine alternate methods of identifying the home and work anchors that begin or end a work journey. We will
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then compare methods for carving out the work-related portion of complex work journeys in order to define a commute.
Purpose-based and Place-based Anchors Work journey anchors can be identified by type of activity at the location (trip purpose), by the type of location itself, or a combination of the two. A purpose-based approach to anchors has several advantages, including that it:
Identifies commuting trips to conduct paid employment at locations that differ from the commuter's official work address. Since the NHTS limits each respondent to a single "work" location, this is especially important for people with more than one job or job site.
Differentiates between a trip to work and a weekend trip to use the gym at work. Correctly identifies commutes by people who spent the night somewhere other than their
address of record (e.g., someone working on extended assignment in another city, or those who spent the night with a romantic partner with whom they do not cohabitate fulltime).
In short, considering purpose is important because people do not always work or sleep at their official work or home addresses, and some trips to the work address may be for nonwork purposes.
NHTS's method of identifying work trips is based entirely on purpose and does not consider location. The tours are also location-blind; a homework tour is defined by a purpose of "regular home activities" at one end and a purpose of "work," not including working from home, at the
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other. This location-blind approach, which originated in an era of lower computer processing power, is an efficient way of capturing simple commutes. The approach will also capture most complex work journeys because they will all end, eventually, with a "work" or "home" purpose.38
However, by NHTS's definition, a commute tour is considered to end at home only when the purpose of the final trip is "regular home activities" or "work from home (paid)." When any of several other activities that might take place at the home location (e.g., dropping off or picking up a family member, changing type of transportation, exercise) are conducted there, the location is not recognized as a "home" anchor, and trips following a stop at home for such purposes will be included as part of the tour. Consider the following series of four trips:
Home Work Transport Someone Transport Someone Home
This sequence of trip purposes can describe multiple scenarios (figure 13):
A father went to work and picked up his children from school and daycare on the way home. A father went to work, drove straight home to pick up his daughter and took her to karate class before returning home himself.
In scenario I, the commuter makes one simple work journey and one complex work journey. In scenario II, he makes two simple work journeys, followed by additional trips that are unrelated to
38 NHTS tours also consider nonwork, nonhome stops to be anchors if they are longer than 30 minutes. However, these can simply be joined back together (i.e., a HomeOther + OtherWork tour constitutes a Home Work work journey).
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work travel. However, a location-blind method cannot distinguish between scenarios I and II. The father's first stop at home in case II would not register as a "home" trip because the purpose was not "regular home activities"; it was to pick up his daughter and transport her somewhere else. Those nonwork trips would erroneously be considered part of his commute.
Figure 13. Diagram. Trip sequence with multiple potential commute patterns. A location-blind approach also makes it difficult to distinguish a trip to the gym on the way to work (a complex work journey) from a walk around the block followed by a drive to work (a simple work journey), since both initial trips depart from home and have a purpose of "fitness." Similarly, a walk to happy hour with coworkers at the end of the work day, followed by a return to the office to change mode of transportation (i.e., pick up the car), would register the two walk trips as part of a complex commute, whether or not the person made further stops on the way home. Conversely, stopping on the way home from work to do housework for an elderly relative
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would register as a "home" anchor because chores are a regular home activity. The internal stop at a relative's house would incorrectly be identified as the end of the work journey.
To avoid these errors, the most effective definition of work journey anchors should consider both purpose and place. Unfortunately, the public use NHTS dataset does not contain any variables that would allow researchers to incorporate location into work journey identification. We are able to correct for this problem by using variables from the confidential version of the data made available to GDOT (and thence to us) by virtue of its commissioning the add-on sample. Analysts using the public-use version would not be able to incorporate location into their definition of anchors; however, the proposed method does not require identifiable data such as specific GIS coordinates. Thus, the anonymized location categories (i.e., "home," "work," and "other") and ID numbers used could arguably be released publicly without imperiling confidentiality; doing so would make the public-use dataset more powerful for researchers who are not able to access the confidential data.
Measuring Complex Commutes: The Last-leg and Counterfactual Methods Once full work journeys are identified, it is necessary to identify which portions of those observed work journeys are considered commutes. If a commuter stops for gas at a filling station that does not require deviation from the shortest route between home and work, it may be best to consider the entire work journey to be the commute. If, in contrast, a commuter goes to night classes at a university in a nearby city after work, less of the distance traveled from workplace to home is actually related to the commute. Methodologically, a uniform rule is needed that can accommodate different scenarios such as these.
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Figure 14 shows an example of an observed work journey and two methods of extracting the "real" commute from the larger work journey. In the NHTS data, the most obvious way to apportion work travel is to analyze commutes at the trip level. This report refers to this as the "last-leg" method, because work trips in the trip file are the last leg of the work journey.39
Figure 14. Diagram. Alternate methods for identifying commutes within complex work journeys.
In this example, the commuter has stopped for coffee on the way to her office. The coffee shop is very close to her workplace. As a result, the last-leg method (dotted line) identifies a commute that is much shorter than the distance between home and work. This is a key shortcoming of the last-leg method.
39 NHTS classifies trips based on purpose at destination, so even when just using the "last-leg" method, commute trips in the "home" direction need to be identified based on the trips that preceded them.
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An alternate method is to identify the commute based on a counterfactual work journey: how long would the trip have been if the commuter had proceeded directly from home to work (dashed line)? Using the counterfactual distance as the commute distance gives a more accurate picture of what portion of the work journey is work-related, and how much distance was added by the additional stops.
The ideal method of computing the counterfactual distance would be to use the same process the NHTS uses to calculate the distance of reported trips: calculate the shortest-path distance using the Google application programming interface (API). However, this solution is impractical for several reasons. To begin with, it requires exact coordinates of all travel locations, which are unavailable to users of the public dataset. Second, the Google API only provides estimates for current or future conditions; no functionality exists to calculate what conditions were like when the data were collected in 2017.
Many commuters make a simple work journey in one direction and a complex work journey in the other. In these cases, the observed simple commute can be used as a counterfactual for a complex commute between the same two anchors. However, for commuters who made no simple work journeys, no obvious counterfactual is available.40 As will be described, this study imputes counterfactuals for these commutes by first modeling commute distance for cases where an
40 NHTS contains a "distance to work" variable, but even when considering only simple commutes between the home location and the normal work location, the "distance to work" variable differed from the observed commute distance by at least 0.2 mile in 34.9 percent of cases (unweighted, N=6,881). In 1 percent of these cases, the difference was 4.3 miles or greater, and the largest observed discrepancy was 111.6 miles. It is likely that the issue is due in part to the fact that distance to work was calculated based on an address provided during an earlier stage of the survey than the travel diary, sometimes by a different person than provided the trip information. In addition, some respondents may have listed the "home office" or human resources (HR) address rather than their actual work site. For these reasons, the distance to work variable is of limited utility as a measure of counterfactual commutes.
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observed counterfactual is available, and then using that model to predict the distance for cases where it is not available.
Methods for Definition and Measurement of Commutes The identification of commutes, and measurement of their key characteristics, was completed through the following steps:
1. Identify valid anchors. 2. Use anchors to group travel day trips into journeys. 3. Validate work journeys by classifying all journeys as either "work" or "other". 4. Calculate distances and times at the work journey level, and identify modes and stop
characteristics. 5. Identify potential counterfactual commutes for complex commutes. 6. For complex work journeys with an observed counterfactual, use weighted ordinary least
squares (OLS) regression to model the "complexity increment," or the incremental contribution of internal stops to the total work journey PMT. 7. For complex work journeys with no observed counterfactual available, use the model of complexity increments to impute counterfactual distances for unmatched complex work journeys. 8. Use observed and predicted complexity increments to calculate a work journey's commute PMT.
Identifying Anchors The travel day origin site and all destinations were evaluated for their potential to be anchors to a work journey based on their purpose and location. The purpose of a location was defined as the
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primary activity conducted there, either as the origin of the first trip of the day, or as a destination otherwise.41
Location was analyzed using two variables from the confidential dataset. Location type classifies all places as home, work, school, or other (regardless of purposes at those locations). For this analysis, school and other were combined into a single category, "other." Location number is a unique identifier for each location visited by a household member on the travel day. Location number was used to resolve ambiguities and questions (such as home activity at a nonhome location), and to screen out journey-level loops. Neither of these variables contain any geographic identifiers that would pose a risk to participant confidentiality if released publicly.
Table 50 shows the criteria used in this study to define anchors. In contrast to the current NHTS approach, the home location was always considered to be a home anchor, regardless of trip purpose. This includes a number of trips where respondents, perhaps mistakenly, recorded their purpose at the home location to be "work for pay" rather than "work from home (paid)."42 The NHTS considers the former purpose (but not the latter) to signify the occurrence of a commute tour, so if using only the purpose indicator, this would cause the home to be mislabeled as a "work" destination. This error is especially important to correct because it tends to create
41 From a data management standpoint, it is important to remember that NHTS's file is organized by trips rather than locations. Each trip involves two locations (an origin and a destination). The destination of one trip is the origin of the next. Because the last destination at the end of the day does not form the start of a new trip, the number of locations is one greater than the number of trips. In the data, each location (except the start and end points for the day) will be listed twice in the trip-file: first as the destination of one trip, and then as the origin of the subsequent trip. Using destination characteristics will yield the needed data about all locations except the location where the traveler started the day.
42 It is possible that some respondents were reporting working from a separate office at their home address, such as a therapist or accountant with an office having its own separate entrance. A smaller number of respondents reported "working from home" at their work location; these were recoded as simply "work."
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imaginary commutes if a person leaves home with an origin purpose of "home activities," and then returns home to an erroneously tagged "work" destination.

Except for home, this report considers any destination with a purpose of work to be a work anchor, regardless of location. We include "other" location types in this rule because each participant was limited to providing a single work address. This does not match the reality of multiple jobs or job sites; 16 percent of work activity occurred at locations categorized as "other."

Table 50. Initial anchor classifications based on location and primary activity at location.
PRIMARY ACTIVITY Regular Home
Work for Pay Other Purpose* Activities

Home

Home

Home

Home

LOCATION TYPE

Work Provisional Work

Work

Provisional Work

Other Provisional Home

Work

Not an Anchor

* Including work from home (paid). In only 25 cases out of nearly 60,000, participants reported working from home at a location other than home or work.

The research team identified two types of provisional anchors (i.e., destinations that needed further examination to determine whether or not they were anchors). Provisional home anchors consisted of home activity at a nonhome location; these locations could reflect either a situation such as spending the night at a friend's home or hotel (making the location a legitimate, albeit temporary, home anchor), or one such as helping with housework at another person's home (for

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which the location would not be a home anchor). In a second pass through the data, provisional home anchors were considered anchors if either of the following two criteria were true: (1) the traveler began or ended the day at that location, or (2) the traveler never visited the "home" location on the travel day. Trips made to a validated provisional home destination were included whether or not that trip specifically was the first or last trip of the day. Provisional home anchors that did not meet either criterion were reclassified as non-anchors. Provisional work anchors were identified when nonwork purposes occurred at the work location, addressing situations as shown in figure 15. In the example in the figure, the traveler went out for dinner after work, then returned to the office to pick up her car. Trip 3 ends at a provisional work anchor. If the provisional anchor is not identified, the work journey home would appear to include trips 2, 3, and 4, whereas just considering trip 4 to be a simple work journey might be more appropriate. Since the validity of a provisional work anchor depends on the sequence of trips before and after it, these provisional anchors were evaluated during the validation of work journeys.
Figure 15. Diagram. Sequence of trips including a provisional work anchor. 122

Defining Work Journeys The itineraries of respondents with at least one valid work anchor and one valid home anchor on the travel day were evaluated for the presence of work journeys. The process began by dividing all of the day's trips into journeys, with a new journey beginning each time an anchor was encountered. Figure 16 shows several hypothetical sequences of trips and how they would be parsed as journeys and work journeys.
Figure 16. Diagram. Example work journey classifications of trip sequences. As scenarios I and II show, HW and WH journeys were considered work journeys regardless of how many non-anchor destinations were visited over the course of the journey or
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the duration of those non-anchor stops. HH and WW journeys were discarded.43 Some respondents had more than two work journeys (scenario III). Scenario IV illustrates how provisional work anchors (involving nonwork activity at a work location) were evaluated. The first provisional anchor is not considered part of a work journey because the preceding and following anchors are both home; the traveler was at the work location, but only there for nonwork activities. The second provisional anchor is considered a work anchor because it follows a definitive work anchor (and thus is a return to the workplace after a "side trip") and precedes a home anchor.
Many commuters made fitness walks at the beginning of the travel day like the one illustrated in scenario V. In the NHTS's location-blind tour file, these loop trips were inappropriately classified as part of the HW journey.
As table 51 shows, 55 percent of complex work journeys in Georgia involved a single, short stop. The most common purposes were shopping/errands, transporting someone, and dining. Social, recreational, and work-related stops had much longer internal dwell times. As we will see, these longer dwell times were associated with larger complexity increments than stops with shorter dwell times, with the exception of stops to transport someone else.
43 WW journeys might have the same beginning and ending destination, such as if the participant went out for a quick afternoon coffee. A WW journey might also be between two different locations at which the participant worked for pay. Both types of WW journeys were treated the same when classifying them, and neither are considered to constitute part of a commute. This classification has one potential limitation with respect to multiple job-holders: if the two ends of a WW journey were jobs with two different employers, it might be more accurate to consider that journey part of the participant's commute. Unfortunately, the NHTS does not contain enough detail to differentiate a journey between two locations associated with a single job from a journey between a first and second job.
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Table 51. Duration and purposes of stops in work journeys.

Stop Categories

Percent of all WJs Percent of Complex Mean Internal Dwell

(N=10,490)

WJs (N=2,618)

Time (Minutes)*

Itinerary (Number and Duration of Stops)

No stops (simple)

75.8%

-

0.0

Single short stop (<30 min)

13.3%

54.7%

9.3

Single long stop (30+ min)

5.0%

20.5%

97.7

Multiple short

2.5%

10.5%

18.9

Short + long or multiple long

3.5%

14.4%

132.5

Total Purpose of Stop(s)

100.0%

100.0%

46.1

Shopping/errands

10.7%

44.3%

22.0

Transport someone

6.9%

28.3%

9.2

Dining

5.0%

20.6%

26.4

Social/recreational/fitness

2.6%

10.8%

85.9

Work-related

0.7%

3.0%

81.4

Other

3.3%

13.7%

85.9

Multiple purposes

3.9%

16.3%

* For itineraries, total dwell time of all internal stops is shown. For the purpose of this report, dwell time shown is the average for a

single stop of that type. If a journey contained two 10-minute stops for shopping, the stops would be counted separately rather

than as a single, 20-minute stop.

Work journeys with stops for multiple purposes are listed under "Multiple purposes" and also under each relevant individual

purpose.

Identifying Counterfactuals and Complexity Increments Once work journeys were identified, journey-level statistics were calculated based on the trips included in each journey. These include work journeylevel mode(s); total PMT and VMT; the number, duration, and purpose of stops; and total travel time and internal dwell time (duration of all stops).
For complex work journeys, the data were examined for the presence of a trip that could serve as an observed counterfactual, which is defined as a simple work journey made by the participant

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between the same origindestination pair44 at some other point on the travel day. As shown in table 52, matches were identified for 60.5 percent of complex work journeys. Same-direction matches were used where available, but in most cases, the matched simple work journey was in the opposite direction as the complex work journey (e.g., a commuter's evening journey from work that includes a stop is matched with the simple morning journey to work). These matched complex work journeys were used to build a model to predict a counterfactual for the 37.5 percent of unmatched complex work journeys where no observed counterfactual was available.

Table 52. Counterfactual match status of complex work journeys in sample.

Category

Number of Cases
(Unweighted)

Percent

Total complex work journeys

2,618

Matched with opposite-direction simple commute

1,484

56.7%

Matched with same-direction simple commute

101

3.9%

No observed counterfactual

983

37.5%

Excluded from analysis*

50

1.9%

* Reasons for exclusion: supercommute >100 mi (N=35), invalid distance provided (N = 10), commute includes air

travel (N = 5).

The weighted median distance of the complex work journeys was 16.2 miles and the 95th percentile was 52.6 miles. The small number of work journeys longer than 100 miles was found to exert a disproportionate influence in the model; we therefore excluded them to provide more accurate estimates for shorter work journeys.

44 Location numbers were used in matching to avoid errors when matching trips of respondents with multiple home or work locations.
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Conceptually, we define a complexity increment as the incremental distance contributed by the internal stops in a complex work journey (compared to the mileage of a simple commute with the same origin and destination). Mathematically, the complexity increment for a complex work journey that has been matched with an observed counterfactual is the observed total PMT for the complex work journey minus the observed PMT for its simple counterfactual. For the set of matched cases, the weighted median complexity increment was 1.9 miles. The mean was 4.8 miles, with a maximum of 79.7 miles.
For some work journeys, the added stops did not add miles; in 3.8 percent of cases, the complexity increment was zero or near-zero.45 In 7.8 percent of cases, the complexity increment was below zero. This is likely due to the fact that the recorded distances calculated by the Google API optimize for shortest travel time rather than distance, and thus recorded distances (in particular, those for the simple commutes being used for the counterfactual match) tend to favor highways over surface streets. If an added stop causes the commuter to use local roads instead of a more circuitous highway, the stop may actually reduce the mileage of the work journey compared to its simple commute counterfactual, such as the example in figure 17.46
45 The distances for the two directions can vary due to one-way roads, traffic conditions at the time the Google API was queried, etc. To decide what constitutes "near-zero", the researchers compared opposite-direction matched simple commutes from participants with no complex work journeys. This report defines near-zero as a complexity increment with an absolute value of less than 0.2 mile.
46 The median negative complexity increment was -0.85 miles, the 95th percentile was -4.3 miles, and the longest was -8.9 miles. Negative complexity increments were most commonly found in Atlanta, which has a complex highway network, and in smaller cities with rivers running through the downtown (e.g., Macon, Columbus, and Albany).
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Figure 17. Maps. Example journey with a negative complexity increment.
Using weighted OLS regression on the complex work journeys for which an observed counterfactual was available, the research team modeled the complexity increment as a function of the distance of the observed complex work journey, stops made, and other sociodemographic variables. The fitted model was then used to predict complexity increments for the unmatched complex work journeys (i.e., those with no observed counterfactual).
Complexity increments are a measure of change; the results of the model tell how many miles stops of various types and lengths would be expected to add to (or sometimes subtract from47) a
47 For unmatched complex work journeys, the predicted complexity increment was near-zero but negative in 1.4 percent of cases, and nonnegligibly negative in 9.0 percent of cases (unweighted).
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commute. These data can be used to examine issues like how total commute PMT might change if complex commutes became more common (i.e., replacing some simple commutes with complex commutes). The model also allows for prediction of the counterfactual (simple) work journey distance for unmatched work journeys, defined as complex work journey PMT minus the complexity increment. This reveals how long these commutes would have been without internal stops. The next step is to calculate the commute portion of total observed PMT for complex work journeys. Initially we computed this as the observed (where available) or predicted counterfactual distance. However, the commute PMT represents the actual mileage traveled by the worker that should be attributed to his or her commute. Therefore, the commute PMT is capped at 100 percent of observed work journey PMT. This cap accounts for the minority of cases with a negative complexity increment, making sure that more miles are not allocated to a worker's commute than that worker actually traveled. The next section presents the results of these estimations and compares them with the results obtained from looking at the entire work journey and using the last-leg method.
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Results Model of Complexity Increments Table 53 shows the complexity increment distance (in miles) of matched commutes as a function of trip distance, stops, and demographic and environmental information.48 The full model explains more than half the variation in complexity increments. Most of this explanatory power comes from characteristics of the work journey itself: a model based only on distance and stops has an R2 of 0.486; the addition of demographic variables results in a final R2 of 0.544. Longer observed work journeys are associated with larger complexity increments; these effects differ between rural and nonrural areas. A stop of any length adds to the complexity increment, but stops of a longer duration are associated with larger increases. Stops to transport someone, which tend to be relatively brief, exert an outsized effect on the complexity increment given their duration. Demographic variables such as gender, age, income, and vehicle ownership are also significant. Mode was insignificant, perhaps due to the high private auto mode share.
48 Mode is included in the model, but was not found to be significant either in the single category of "alternate mode" shown here or in more disaggregated forms. This may be because 97 percent of complex work journeys were entirely or partially by personal vehicle.
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Table 53. Weighted OLS model of work journey complexity increment distance in miles.

Full Model

Trip Characteristics Only Model

Covariate

Coef

P-Value

Coef

P-Value

WJ PMT WJ PMT2 Interaction: rural x WJ PMT Interaction: rural x WJ PMT2

-0.0673 0.00674 0.175 -0.00615

0.372 <0.001 ***
0.016 ** 0.005 ***

0.0198 0.00432

0.855 0.060 *

WJ internal dwelltime, minutes

0.0174

0.013 **

0.0207

0.015 **

Number of stops by duration

Short (<30 minutes)

1.349

<0.001 *** 1.238

<0.001 ***

Medium (3059 minutes)

2.460

<0.001 *** 2.358

<0.001 ***

Long (60+ minutes)

3.451

<0.001 *** 3.516

0.001 ***

Purpose: transport someone (yes/no) Alternate mode used for part or all of trip

2.889 -0.422

<0.001 *** 0.795

2.923

<0.001 ***

Household size & vehicle sufficiency (Reference: 1-driver, 1-vehicle households)

Vehicle-sufficient with 2+ drivers

-2.262

<0.001 ***

Vehicle-deficit or zero-vehicle

-1.557

0.046 **

Female

-0.993

0.023 **

Age Age2

-0.271 0.00290

0.009 *** 0.010 **

Household income (reference: <$35,000)

$35,000$49,999

-0.885

0.119

$50,000$74.999

0.938

0.101

$75,000$99,999

1.238

0.148

$100,000+

2.190

0.004 ***

Income missing (dummy variable)

1.176

0.200

Constant Model R2

5.582 0.544

0.015 **

-2.042 0.486

0.036

Rural is defined by urbanicity of participant's home address rather than census designation.

Defined as any mode besides personal occupancy vehicle.

Vehicle-sufficient is defined as having a number of vehicles equal to or greater than the number of household members

ages 16+. Vehicle-deficit is defined as having at least one vehicle, but fewer vehicles than household members ages 16+.

Table 54 shows predicted complexity increments based on the most common itineraries and average work journey distance. The average predicted complexity increment is 5 miles; the median is 3.5 miles. Trips with a single short stop, the most common type of trip, have a

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predicted complexity increment of 3.2 miles. A single long stop, on average, is associated with a complexity increment of 7.4 miles.

Table 54. Predicted complexity increments.

Mean Predicted Complexity
Increment (miles)

Percent of Complex Work
Journeys (N=2,568)

Average total predicted complexity increment*

5.01

Complexity Increments for Example Itineraries

Single short stop

3.22

54.7%

Single medium stop

4.86

8.3%

Single long stop

7.37

12.2%

Two short stops

4.73

8.7%

Three short stops

6.23

1.4%

Short stop + medium stop

6.36

3.4%

Short stop + long stop

8.87

3.2%

Medium stop + long stop

10.50

1.0%

* The figure here is the average for all complex WJs under 100 miles in the NHTS GA sample, including WJs with negative

complexity increments. For consistency, in this table, predicted values are used for both matched and unmatched WJs. This is

different from table 55, where predicted values are used for unmatched WJs and observed values are used for matched WJs.

Complex Commute Distance Having discussed how internal stops change commute distance, we now estimate the share of total observed work journey PMT that pertains to the commute (i.e., commute PMT), and compare the estimates with those obtained by the last-leg method, and with total observed work journey PMT (table 55). The average complex work journey is 19.4 miles. Among complex commutes, the average distance assigned to the commute purpose, based on the counterfactual approach (i.e., subtracting the predicted complexity increment from the complex work journey length, where observed counterfactuals are not available), is 14.3 miles. When using the last-leg method, the average distance assigned to the commute purpose would be just 8.8 miles. Across all complex work journeys, about three quarters of the total journey PMT is allocated to the
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commute under the counterfactual approach, compared to only about two fifths under the last-leg approach. Of course, since 76 percent of work journeys are simple, the differences between the two methods diminish when taking both simple and complex work journeys into consideration. In that case, the commute purpose accounts for more than 90 percent of the distance using the counterfactual method, or more than 80 percent using the last-leg method. Even so, the differences between the two methods remain nontrivial.

Table 55. Comparison between full work journey, counterfactual, and last-leg commute measurements.

WJ Length Category
All complex WJs

Percent of Complex
WJs

Mean WJ PMT: Full
WJ
19.4

Mean Commute PMT
(Counterfactual Method)*
14.3 (73.9%)

Mean Commute PMT
(Last-Leg Method)
8.8 (45.1%)

<5 mi

10.2%

3.4

2.3 (69.4%)

1.7 (49.7%)

59.99 mi

18.9%

7.5

5.6 (75.1%)

3.4 (45.3%)

1024.99 mi

44.3%

16.7

12.8 (76.8%)

7.9 (47.1%)

2549.99 mi

22.6%

33.9

25.9 (76.2%)

14.8 (43.7%)

5074.99 mi

3.2%

58.0

35.8 (61.7%)

26.8 (46.2%)

7599.99 mi

0.9%

84.3

43.4 (51.5%)

29.0 (34.4%)

All WJs (simple and complex)

14.8

13.6 (91.8%)

12.3 (82.7%)

* Defined as the counterfactual (simple) WJ PMT, capped at 100% of observed (complex) WJ PMT. Observed

counterfactuals are used where available. When there is no observed counterfactual, the counterfactual is

calculated by subtracting the predicted complexity increment from the observed complex WJ PMT.

Defined as the PMT of the final trip in a complex WJ. In a home-to-work WJ, the last leg is the trip that ends

at a work anchor; in a work-to-home WJ, the final trip ends at a home anchor.

Percent of average row-category WJ PMT in parentheses.

At the state level, using the last-leg method would underestimate Georgia's commute PMT by 2.6 billion miles per year compared to using the counterfactual method, an amount that constitutes about 10 percent of the total commute PMT. As such, the counterfactual model is a clear improvement over the last-leg technique.

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Summary of Methods Implications for Upcoming Report Sections Which method of measuring work travel is preferable? This depends on the question to be answered. For example, when considering commute burden on individual travelers, using the full work journey is likely to be a better proxy for the full amount of travel required of each worker, particularly if the stops involved are household-serving travel such as dropping off children at school. Because women are disproportionately responsible for such trips, looking only at the commute portion of a complex work journey may understate the true toll of commutes that might otherwise appear short (Gimenez-Nadal and Molina 2016). However, this must be balanced with the fact that male commuters are more likely to make nonessential stops such as going for coffee (McGuckin, Zmud, and Nakamoto 2005), in which cases using the full work journey would arguably offer an inflated view of the commute burden.
For estimates of PMT and VMT generated by commuting, it might be preferable to isolate the portion of each work journey that should be considered work-related. For this purpose, the counterfactual method is a clear improvement over the last-leg method, which underestimates the true amount of work travel.
To provide data to answer a broad range of questions, this report uses several different measurements of commute distance and duration in its upcoming chapters. Henceforward, the definitions of the measurements are as follows:
Work journey PMT: Full distance traveled by the commuter, including all stops. Work journey VMT: Full distance driven by the commuter over the course of the work
journey, excluding miles as a passenger in a POV as well as miles traveled by walking, biking, and public transportation.
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Work journey duration: Travel time, in minutes, of the whole work journey (excluding dwell time for intermediate activities).
Commute PMT: Work-related portion of the whole work journey, calculated using the counterfactual method as described in this section.
Commute VMT: Work-related VMT of the whole work journey. Commute VMT will be scaled proportionally based on the ratio of commute PMT to whole work journey PMT. For example, if a commuter's commute PMT were 80 percent of the whole work journey PMT, the commute VMT would be considered to comprise 80 percent of the work journey VMT.
Commute duration: Duration, in minutes, of the work-related portion of the whole work journey. Like commute VMT, commute duration will be scaled proportionally to work journey duration.49
Supercommute: A work journey in excess of 100 miles. The data included 85 such commutes, or 0.8 percent of all work journeys. Six of these were by air. The groundbased supercommutes ranged between 100 and 447 miles. The longest of these are likely not daily commutes, but rather capture the small percent of the population that will be making longer work-related trips on any given day. Unless otherwise stated, to provide more representative estimates of "typical" work travel, supercommutes are excluded from the analysis. This report provides alternate estimates of total PMT and VMT (with and without the supercommutes). Note: Since commute PMT was not modeled for work
49 Since traffic speeds may vary at different stages of the commute, this method for estimating commute travel time is likely somewhat noisier than the estimations of VMT. However, it will provide a better measure than other available alternatives, such as using the last-leg travel time.
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journeys longer than 100 miles, for complex supercommutes, the commute PMT is defined as the full work journey PMT.
WORK TRAVEL DISTANCE
Table 56 compares workers' distance to work (i.e., the shortest-path distance between their home and work addresses) with the work journeys observed on the travel day. While "distance to work" has the advantage of having been asked of all workers, rather than just those who happened to travel to work on the day they filled out their diary, it is likely that some workers' reported work address does not match the location where they actually conduct their work (see Defining the Commute in this chapter for further discussion). While just 0.8 percent of observed work journeys exceeded 100 miles, 2.3 percent of workers listed a work address more than 100 miles from their home. The supercommutes implied by the "distance to work" variable are, on average, 142 miles longer than the observed travel day supercommutes. Even after excluding supercommutes, the mean distance to work still exceeds the mean commute PMT. For these reasons, the research team suggests caution when analyzing the reported distance to work, and focuses primarily on the observed travel day work journeys.
A few workers reported air as their usual commute mode (0.3 percent), or as their travel day mode (0.1 percent). Travel diary air commutes were nearly twice as long as the average distance to work of commuters who described air as their usual mode of travel, suggesting that the air work journeys reported in the travel diary data largely reflect unusual work travel (i.e., business trips). However, the unweighted sample sizes for both kinds of air commute are very small, and extreme caution should be used when interpreting these findings. Because it is an extreme outlier
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in terms of distance, air travel will be excluded from further analysis. Ground supercommutes will also be excluded unless otherwise stated.

Table 56. Distance to work, commute PMT, and work journey PMT.

Workers* Observed WJs

Distance to Commute

Work

PMT

Work Journey
PMT

Median

11.3

10.3

11.3

95th percentile

46.2

38.5

42.4

Proportion of supercommutes (>100 miles)

2.3%

0.8%

0.8%

Mean Commute Distances

All modes and distances

25.2

16.2

17.4

Regular commutes (<100 miles)

14.4

13.6

14.8

Supercommutes (>100 miles)

481.1

339.1

339.1

Terrestrial supercommutes Air supercommutes

599.8 660.0

191.1 1146.4

191.1 1146.4

Mean excluding air

23.5

14.8

16.0

* This question was asked of all workers; only 6,978 out of 8,363 (unweighted) responded. Weighted N=3.98

million workers (out of a total population of 4.78 million workers).

The sample included 10,463 work journeys made by 5,101 unique commuters (unweighted). Weighted N=1.96 billion WJs by 2.65 million active commuters (annually). Note: These figures should not be used to calculate perworker rates. Rather, per-worker rates should be calculated using a denominator of all workers, including those who did not work on their travel day. As shown in table 46, Georgians make an average of 415 work journeys per worker annually. Since commute PMT was not calculated for complex work journeys greater than 100 miles, WJ PMT is used for 36 complex supercommutes.

Personal occupancy vehicle or other form of ground transportation. No commutes by boat were reported. Mode for the "distance to work" column is determined by the usual commute mode.

There are an estimated 2.4 million work journeys by air each year and 12,500 regular air commuters (weighted). However, due to extremely low unweighted sample sizes (N=11 for distance to work and N=6 for observed WJs), these estimates should be treated with caution.

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Region Table 57 shows average work journey and commute distances by MPO tier. Average commute distances are longest in the most and least populous areas of the state, averaging 15.5 miles for residents of non-MPO counties and 14.1 miles for residents of the Atlanta MPO. Commutes are noticeably shorter in small and medium MPO counties (10.9 and 11.2 miles, respectively). In all parts of the state, the average full work journey is 1.01.3 miles longer than the average commute.

Table 57. Average work journey and commute distance by MPO tier.

Percent Commute Commute

of WJs

PMT

VMT

WJ PMT

WJ VMT

Supercommutes Excluded* (weighted N=1.94 billion WJs per year)

Statewide

100.0%

13.6

12.1

14.8

13.2

MPO Tier (Worker Residence)

1. Atlanta MPO

57.1%

14.1

12.3

15.4

13.4

2. Medium MPOs

15.3%

11.2

10.0

12.4

11.1

3. Small MPOs

9.9%

10.9

10.1

11.9

11.1

4. Non-MPO

17.7%

15.5

14.7

16.7

15.8

Terrestrial Supercommutes Included (weighted N=1.96 billion WJs per year)

Statewide

100.0%

14.8

13.1

16.0

14.2

MPO Tier (Worker Residence)

1. Atlanta MPO

57.0%

15.2

13.2

16.5

14.3

2. Medium MPOs

15.3%

12.1

10.9

13.3

12.0

3. Small MPOs

9.9%

12.2

11.3

13.2

12.3

4. Non-MPO

17.8%

17.1

15.9

18.2

17.0

* A supercommute is a one-way work journey longer than 100 miles.

Commutes by air excluded. No supercommutes by water were reported.

The lengthier commutes in non-MPO areas reflect the lower employment prospects in more rural parts of the state. Whether by necessity or lifestyle choice, 22.9 percent of commutes by residents of non-MPO counties were to a job site in an MPO, requiring the commuter to travel to a different community. In comparison, only 16.2 percent of commutes by residents of small and

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medium MPOs involved travel to a different MPO or non-MPO county. While Atlanta MPO residents were the least likely to travel outside of their home MPO (2.3 percent of commutes), the PMT of commutes that stay entirely within the Atlanta MPO is higher than commute PMT for commutes within medium and small MPOs (13.4 miles versus 7.9 miles). As a result, average commute distances are longest in the Atlanta MPO.
Table 58 shows the percent of statewide work travel originating from residents of each MPO tier; table 59 shows the total miles on which table 58 is based. In keeping with the longer commutes, residents of non-MPO counties account for 16.7 percent of workers but 20.3 percent of commute PMT. Atlanta also accounts for a slightly disproportionate share of commute mileage, while small- and medium-MPO counties account for a smaller portion of commute mileage than they do of workers.
A comparison of PMT and VMT shows that Atlanta MPO residents, on average, spend a smaller portion of their commutes behind a steering wheel and a larger portion either as a passenger or using alternate modes of transportation. Residents of small-MPO and non-MPO counties spend a higher share of their commute travel driving. Residents of medium-MPO counties have identical shares of commute PMT and VMT, indicating that the average percent of commute mileage as a driver matches the state average.
Table 60 shows annual commute miles per worker. As with the average distance for an individual commute, per-worker mileage is higher in the Atlanta MPO and non-MPO counties than in small and medium MPOs. In non-MPO counties, the higher per-worker total is also influenced by the increased number of work trips per worker, increased levels of multiple job-
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holding, and a higher number of work trips by people who may be irregularly employed but are not classified as workers (see Overview of Work Journeys in this chapter).

Table 58. Percent of statewide work-related miles traveled by MPO tier.

Commute PMT

Commute VMT

WJ PMT

Percent of WJ VMT Workers

Supercommutes Excluded* (weighted N=1.94 billion WJs per year)

Statewide miles (millions)

26,417

23,609

28,777

MPO Tier

1. Atlanta MPO

59.2%

57.7%

59.2%

2. Medium MPOs

12.6%

12.6%

12.8%

3. Small MPOs

7.9%

8.2%

8.0%

4. Non-MPO

20.3%

21.4%

20.0%

Terrestrial Supercommutes Included (weighted N=1.96 billion WJs per year)

Statewide

28,908

25,717

31,268

MPO Tier

1. Atlanta MPO

58.8%

57.3%

54.9%

2. Medium MPOs

12.5%

12.7%

11.8%

3. Small MPOs

8.2%

8.5%

7.4%

4. Non-MPO

20.5%

21.5%

17.6%

* A supercommute is a one-way work journey longer than 100 miles.

Commutes by air excluded. No supercommutes by water were reported.

25,684
57.8% 12.8% 8.3% 21.2%
27,792
57.4% 12.9% 8.6% 21.2%

100%
57.8% 15.5% 10.1% 16.7%

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Table 59. Annual work-related PMT and VMT by MPO tier (millions of miles).

Commute PMT

Commute VMT

WJ PMT

Supercommutes Excluded* (weighted N=1.94 billion WJs per year)

Statewide

26,417

23,609

MPO Tier (Worker Residence)

1. Atlanta MPO

15,636

13,633

2. Medium MPOs

3,323

2,979

3. Small MPOs

2,097

1,942

4. Non-MPO

5,361

5,055

Terrestrial Supercommutes Included (weighted N=1.96 billion WJs per year)

Statewide

28,908

25,717

MPO Tier (Worker Residence)

1. Atlanta MPO

16,987

14,736

2. Medium MPOs

3,621

3,271

3. Small MPOs

2,370

2,193

4. Non-MPO

5,930

5,517

* A supercommute is a one-way work journey longer than 100 miles.

Commutes by air excluded. No supercommutes by water were reported.

28,777
17,045 3,679 2,295 5,758
31,268
17,180 3,696 2,311 5,514

WJ VMT
25,684
14,839 3,285 2,127 5,433
27,792
15,942 3,576 2,378 5,896

Table 60. Annual work-related PMT and VMT per worker by MPO tier (miles).

Commute PMT

Commute VMT

WJ PMT

Supercommutes Excluded* (weighted N=1.94 billion WJs per year)

Statewide

5,578

4,985

MPO Tier (Worker Residence)

1. Atlanta MPO

5,717

4,984

2. Medium MPOs

4,479

4,016

3. Small MPOs

4,421

4,094

4. Non-MPO

6,831

6,441

Terrestrial Supercommutes Included (weighted N=1.96 billion WJs per year)

Statewide

6,104

5,430

MPO Tier (Worker Residence)

1. Atlanta MPO

6,211

5,388

2. Medium MPOs

4,882

4,409

3. Small MPOs

4,996

4,623

4. Non-MPO

7,556

7,031

* A supercommute is a one-way work journey longer than 100 miles.

Commutes by air excluded. No supercommutes by water were reported.

6,076
6,232 4,959 4,840 7,337
6,602
6,281 4,983 4,872 7,027

WJ VMT
5,423
5,425 4,428 4,484 6,923
5,868
5,829 4,821 5,013 7,513

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Peak, Off-peak, and Weekend Travel As shown in table 61, 88.7 percent of work journeys and 88.2 percent of commute PMT and VMT occur on weekdays, and 66.6 percent of PMT occurs specifically during the weekday peak hours. The average commute PMT of off-peak weekday commutes is lower than the average weekday peak commute or weekend commute. This is especially true of mid-day commutes (10.9 miles, as compared to 14.2 miles for average weekday peak and average weekend).
NHTS does not factor in time of day when calculating trip distances, so the observed differences cannot be attributed to congestion.50 They more likely relate to characteristics of the work or the workers. White-collar workers (clerical/administrative and professional/managerial/technical sectors) make 72.1 percent of their commutes during weekday peak hours, versus 63.4 percent of commutes by blue-collar workers and just 47.4 percent of commutes by service-sector workers (not tabulated). In addition to requiring more off-peak commuting than white-collar jobs, service jobs tend to be more geographically dispersed. Further, service-sector salaries are often lower, and low-income job seekers sometimes have a smaller job search radius due to transportation challenges (Blumenberg and Pierce 2014, Shen and Sanchez 2005).51
Parenthood may be an additional contributor to shorter midday commutes. Heterosexual couples have been found to make career and housing decisions that allow one parent (usually the mother) to work close to home in order to be able to pick up children after school (Jun and Kwon 2015, McQuaid and Chen 2012, Craig and van Tienoven 2019). Relatedly, an off-peak commute is
50 Congestion may, in fact, create differences in the distance of peak versus off-peak commutes, but NHTS does not provide data appropriate for examining the issue.
51 See also table 63.
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more likely to reflect part-time work, and part-time workers' commutes tend to be shorter than those of full-time workers. Table 62 shows peak and nonpeak work travel miles by MPO tier. The proportion of commute miles that occurs during weekday peak hours is highest in the Atlanta MPO, which also has the highest proportion of white-collar workers (see table 38). Small MPOs, with the lowest proportion of weekday peak miles, have the second-lowest proportion of white-collar workers.52
52 Non-MPO areas have lower white-collar employment. However, the "blue-collar" category, which is especially common in non-MPO counties, contains a wide range of job types. It may be that agricultural work is more likely to have a daytime schedule than other forms of blue-collar work (e.g., maintenance, manufacturing). NHTS only includes the job category rather than the more specific occupation.
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Table 61. Work travel distance by time of day.

Percent of Statewide Totals

Percent Commute Commute

of WJs

PMT

VMT

All work journeys (millions of miles)

26,417

23,609

Weekday

88.7%

88.2%

88.2%

AM peak (6 am9:59 am)

32.3%

34.2%

33.9%

Midday (10 am2:59 pm)

13.2%

10.6%

10.8%

PM peak (3 pm6:59 pm)

31.7%

32.4%

32.5%

Overnight (7 pm6:59 am)

11.6%

11.0%

11.0%

Weekend or Holiday

11.3%

11.8%

11.8%

AM peak (6 am9:59 am)

3.1%

3.1%

3.0%

Midday (10 am2:59 pm)

2.3%

2.4%

2.4%

PM peak (3 pm6:59 pm)

3.4%

3.7%

3.9%

Overnight (7 pm6:59 am) All work journeys

2.4%

2.6%

2.5%

13.6

12.1

Avg. Commute and Work Journey Distance Com. PMT Com. VMT

Weekday

88.7%

13.5

12.1

AM peak (6 am9:59 am)

32.3%

14.4

12.8

Midday (10 am2:59 pm)

13.2%

10.9

9.9

PM peak (3 pm6:59 pm)

31.7%

13.9

12.5

Overnight (7 pm6:59 am)

11.6%

12.9

11.6

Weekend or Holiday

11.3%

14.2

12.7

AM peak (6 am9:59 am)

3.1%

13.3

11.7

Midday (10 am2:59 pm)

2.3%

14.0

12.8

PM peak (3 pm6:59 pm)

3.4%

14.9

13.8

Overnight (7 pm6:59 am)

2.4%

14.6

12.5

Annual Miles Per Worker

Com. PMT Com. VMT

All work journeys

5,528

4,941

Weekday

88.7%

4,922

4,397

AM peak (6 am9:59 am)

32.3%

1,909

1,692

Midday (10 am2:59 pm)

13.2%

593

536

PM peak (3 pm6:59 pm)

31.7%

1,807

1,619

Overnight (7 pm6:59 am)

11.6%

613

551

Weekend or holiday

11.3%

656

588

AM peak (6 am9:59 am)

3.1%

171

151

Midday (10 am2:59 pm)

2.3%

132

120

PM peak (3 pm6:59 pm)

3.4%

208

192

Overnight (7 pm6:59 am)

2.4%

146

125

Excludes commutes greater than 100 miles. Weighted N=1.96 billion per year. Based on time of arrival at or departure from work anchor.

WJ PMT
28,777 88.5% 33.1% 11.6% 33.4% 10.5% 11.5% 2.9% 2.3% 3.9% 2.4% 14.8
WJ PMT 14.8
15.2 13.0 15.6 13.4 15.2 13.6 15.1 16.9 14.9 WJ PMT
6,022 5,375 2,008
702 2,027
637 701 175 142 235 149

WJ VMT
25,684 88.7% 32.9% 11.7% 33.6% 10.5% 11.3% 2.9% 2.4% 3.7% 2.4% 13.2
WJ VMT 13.2
13.5 11.7 14.0 12.0 13.3 12.0 13.7 14.5 12.7 WJ VMT
5,375 4,810 1,782
634 1,821
572 614 155 129 202 128

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Table 62. Work travel distance by time of day and MPO tier.

Commute VMT (millions)*

Work Journey VMT (millions)*

Statewide

23,609

25,684

Weekdays AM peak (6 am9:59 am)

20,824 (88.2%) 8,013 (33.9%)

22,778 (88.7%) 8,439 (32.9%)

Midday (10 am2:59 pm)

2,538 (10.8%)

3,004 (11.7%)

PM peak (3 pm6:59 pm)

7,666 (32.5%)

8,626 (33.6%)

Overnight (7 pm6:59 am)

2,607 (11.0%)

2,709 (10.5%)

Weekend or holiday

2,785 (11.8%)

2,906 (11.3%)

Tier 1. Atlanta MPO

13,633

14,839

Weekdays

12,387 (90.9%)

13,539 (91.2%)

AM peak (6 am9:59 am)

4,774 (35.0%)

5,018 (33.8%)

Midday (10 am2:59 pm)

1,636 (12.0%)

1,920 (12.9%)

PM peak (3 pm6:59 pm)

4,446 (32.6%)

5,008 (33.7%)

Overnight (7 pm6:59 am)

1,531 (11.2%)

1,592 (10.7%)

Weekend or holiday

1,245 (9.1%)

1,301 (8.8%)

Tier 2. Medium MPOs

2,979

3,285

Weekdays

2,577 (86.5%)

2,861 (87.1%)

AM peak (6 am9:59 am)

986 (33.1%)

1,059 (32.2%)

Midday (10 am2:59 pm)

280 (9.4%)

360 (11.0%)

PM peak (3 pm6:59 pm)

957 (32.1%)

1,071 (32.6%)

Overnight (7 pm6:59 am)

355 (11.9%)

370 (11.3%)

Weekend or holiday

402 (13.5%)

424 (12.9%)

Tier 3. Small MPOs

1,942

2,127

Weekdays

1,539 (79.3%)

1,699 (79.9%)

AM peak (6 am9:59 am)

583 (30.0%)

627 (29.5%)

Midday (10 am2:59 pm)

167 (8.6%)

203 (9.6%)

PM peak (3 pm6:59 pm)

583 (30.0%)

652 (30.7%)

Overnight (7 pm6:59 am)

206 (10.6%)

216 (10.2%)

Weekend or holiday

403 (20.7%)

428 (20.1%)

Tier 4. Non-MPO

5,055

5,433

Weekdays

4,321 (85.5%)

4,680 (86.1%)

AM peak (6 am9:59 am)

1,670 (33.0%)

1,735 (31.9%)

Midday (10 am2:59 pm)

456 (9.0%)

521 (9.6%)

PM peak (3 pm6:59 pm)

1,679 (33.2%)

1,894 (34.9%)

Overnight (7 pm6:59 am)

515 (10.2%)

531 (9.8%)

Weekend or holiday

734 (14.5%)

753 (13.9%)

Classifications are based on commuter's home address. Excludes supercommutes (>100 miles). * Percent of regional total in parentheses. Based on time of arrival at or departure from work anchor.

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Demographic Differences Table 63 shows demographic differences in average commute and work journey distance. As noted in the previous section in this chapter on Peak, Off-peak, and Weekend Travel, lowincome workers have the shortest commutes, in terms of distance. Upper-middle income households (earning $50,000$75,000 annually) have the longest commutes. Agewise, Generation X workers have the longest commutes, and workers who are above retirement age have the shortest. This is likely related to the fact that the majority of employed seniors work part-time, and part-time workers' commutes are shorter than those of full-time workers. Men's commute distances are longer than those of women, but a closer look reveals that this is only actually true among workers without a college degree; the commute distances for collegeeducated men and women are comparable. As will be presented in the next section in this chapter on Work Travel Distance by Mode, the differences between men's and women's commute durations are smaller than the differences in distance. These same patterns can be observed in annual per-worker miles of work travel (table 64) and percent of miles of work travel (table 65), with some variations. For example, while collegeeducated women's day-to-day commute distances are on par with those of college-educated men's, their annual commute PMT is lower because a larger share of women work part-time.
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Table 63. Average work travel distance by demographic and employment characteristics. 147

Table 64. Average annual work travel distance per worker by demographic and employment characteristics.
148

Table 65. Percent of total work travel distance by demographic and employment characteristics.
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WORK TRAVEL DISTANCE BY MODE Table 66 shows differences in commute length by mode. Including multimodal commutes (but excluding supercommutes), 95 percent of commute PMT comes from POVs (as driver or passenger), 89 percent as a driver, and 75 percent as a driver with no passengers in the vehicle. Table 66 is a reminder that PMT may overstate the prominence of some modes and understate others. Nonmotorized travel, for instance, represents just 0.3 percent of annual commute and work journey miles, but its mode share in terms of number of journeys is more than 10 times higher (3.2 percent). At the opposite end of the spectrum, if supercommutes were included, air travel would account for 0.1 percent of all work journeys but 8.1 percent of total work journey PMT.53 Ground supercommutes would comprise 0.7 percent of work journeys and 7.3 percent of total work journey PMT (not tabulated). The average distance for an individual commute is 13.6 miles (or 14.8 miles for the full work journey). Excluding supercommutes, the mode with the longest average commute distance is transit, followed by multimodal commutes. For full work journeys, multimodal work journeys are the longest.
53 This is the reason for generally excluding supercommutes from the analysis.
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Table 66. Work travel distance by mode. 151

The average commute and work journey distances for a nonmotorized work journey are both 1.5 miles, making it the only mode for which full work journeys are not longer than commutes. However, this is not because nonmotorized commuters do not make stops. Auto users are the most likely to make stops on their commutes (24.2 percent of unimodal commutes by auto are complex). While nonmotorized complex work journeys are less common, nonmotorized commutes are slightly more likely to include a stop than transit work journeys (3.7 percent versus 3.3 percent). What this does suggest, however, is that nonmotorized commuters who make stops are likely to visit destinations that are directly along their normal route rather than making detours.
COMMUTE DURATION AND BURDEN
Having discussed the distance of work travel, we turn now to the duration. Both distance and duration are measurements of a commute's length, but because speeds vary by mode and region, distances do not, by themselves, indicate how much of a worker's day is devoted to traveling to and from the job.
This report presents three measures of commute duration.
1. Self-reported "usual" commute duration, asked of all workers. 2. Travel day commute duration: The duration of commutes observed on the travel day. For
complex commutes (those containing one or more stops), the commute duration is calculated using the methods described previously in Defining the Commute. 3. Travel day work journey duration: The full travel duration of all trips involved in the work journey (as opposed to the commute-only portion presented for measure 2).
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Time spent at stops is not included in commute or work journey duration. Supercommutes (longer than 100 miles) are excluded unless otherwise stated.
As shown in table 67, reported "usual" commute times were slightly shorter than observed travel day commute times. The average "usual" commute was 30.9 minutes, the average travel day commute was 32.5 minutes, and the average travel day work journey lasted 35.9 minutes. Depending on which indicator is consulted, 1013 percent of work travel lasts at least 1 hour. This figure excludes supercommutes, which last an average of 4 hours and 11 minutes.
The bottom half of table 67 shows the difference between an individual commuter's reported usual commute duration and that commuter's travel day commute and work journey durations. In a plurality of cases, the "usual" and travel day commute times differ by less than 5 minutes. In 16.8 percent of cases, the travel day commute is shorter than the "usual" commute, and in 37.6 percent of cases, the travel day commute is longer. The fact that observed commutes are more likely to be longer than the "usual" duration suggests that respondents report their "usual" commute times under good traffic conditions.
With respect to supercommutes, 92.3 percent are longer than the corresponding "usual" times, and 82.5 percent exceed the duration of the usual commute by at least 30 minutes. This suggests that most supercommutes are unusual work travel or business trips and do not reflect that person's typical commute times. For this reason, supercommutes are excluded from further analysis in this chapter.
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Table 67. Work-related travel duration in minutes.

Workers*

Observed Work Journeys

"Usual" Commute Duration

Travel Day: Commute

Travel Day: WJ

Travel Day: Supercommute

Mean (minutes)

30.9

32.5

35.9

250.4

Percentiles (minutes) 5th 50th 95th

5

5

5

106

25

26

30

222

75

81

90

490

Percent lasting 60+ minutes

11.9%

10.3%

13.1%

100%

Compared to the "usual" commute, the travel day duration was...

Shorter by at least 5 minutes

16.8%

13.7%

6.4%

The same (04 minutes difference)

45.6%

42.9%

1.3%

Longer by at least 5 minutes

37.6%

43.4%

92.3%

* This question was asked of all workers; 6,961 out of 8,363 (unweighted) responded. Weighted N=3.97 million workers.

Based on all work journeys 0100 miles reported in travel diaries. The sample included 10,378 work journeys made by 5,067 unique commuters (unweighted). Weighted N=1.94 billion WJs by 2.61 million active commuters.

Based on all work journeys greater than 100 miles reported in travel diaries. The sample included 85 supercommutes made by 67 unique commuters (unweighted). Weighted N=15.42 million supercommutes by 36,366 active commuters.

(Travel day duration minus usual duration) -5.

-5 < (Travel day duration minus usual duration) < 5. (Travel day duration minus usual duration) 5.

Commute Times by Time of Day and MPO Tier Commute times are longest during weekday evening peak hours (table 68). The Atlanta MPO has the longest commute times and the most variability; the average weekday PM peak commute is 42.2 minutes, which is 13.6 minutes longer than the average overnight commute. Non-MPO counties have the second-longest commute times, but the least variability by time of day. Small MPO counties have the shortest commute durations on average.

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Table 68. Travel day commute and work journey duration by time of day and MPO tier.

Commute Duration

Work Journey Duration

Mean

Median Percent Mean

Median Percent

(Minutes) (Minutes) 1 Hour (Minutes) (Minutes) 1 Hour

Statewide

Weekdays

32.4

27

13.6%

35.8

30

17.1%

AM peak (6 am9:59 am)

32.1

27

13.3%

33.9

30

15.1%

Midday (10 am2:59 pm)

27.3

20

10.1%

33.1

24

15.3%

PM peak (3 pm6:59 pm)

36.7

30

18.1%

41.4

33

23.1%

Overnight (7 pm6:59 am)

27.1

24

6.4%

28.9

25

8.2%

Weekend or holiday

32.7

25

13.3%

35.9

26

16.4%

Tier 1. Atlanta MPO

Weekdays

36.4

30

17.4%

39.9

31

21.1%

AM peak (6 am9:59 am)

35.9

30

16.4%

37.7

30

18.2%

Midday (10 am2:59 pm)

29.9

24

12.5%

36.1

29

17.3%

PM peak (3 pm6:59 pm)

42.2

35

24.2%

47.2

40

29.7%

Overnight (7 pm6:59 am)

28.6

25

6.8%

30.2

25

9.6%

Weekend or holiday

38.6

28

19.9%

42.4

30

23.3%

Tier 2. Medium MPOs

Weekdays

26.8

21

6.7%

30.7

25

10.1%

AM peak (6 am9:59 am)

25.9

20

6.4%

28.0

23

9.0%

Midday (10 am2:59 pm)

23.6

20

5.5%

31.1

20

11.2%

PM peak (3 pm6:59 pm)

29.5

25

8.0%

33.5

29

11.9%

Overnight (7 pm6:59 am)

26.4

20

5.8%

29.7

20

6.6%

Weekend or holiday

28.4

20

9.0%

32.7

21

12.6%

Tier 3. Small MPOs

Weekdays

24.6

20

8.0%

27.6

20

10.9%

AM peak (6 am9:59 am)

24.0

20

7.5%

25.8

20

8.8%

Midday (10 am2:59 pm)

21.3

15

6.8%

26.3

16

12.0%

PM peak (3 pm6:59 pm)

27.6

23

10.8%

31.6

25

14.8%

Overnight (7 pm6:59 am)

21.9

20

3.3%

23.3

20

4.7%

Weekend or holiday

24.7

20

4.5%

27.3

22

7.0%

Tier 4. Non-MPO

Weekdays

27.9

20

9.7%

30.5

25

12.9%

AM peak (6 am9:59 am)

28.4

20

11.4%

29.6

20

12.9%

Midday (10 am2:59 pm)

25.2

18

8.4%

28.6

20

14.5%

PM peak (3 pm6:59 pm)

29.2

22

9.8%

33.4

28

14.4%

Overnight (7 pm6:59 am)

25.8

24

7.0%

27.3

25

7.0%

Weekend or holiday

30.0

25

9.7%

31.1

27

11.9%

Classifications are based on commuter's home address. Excludes supercommutes (>100 miles one way). Based on time of arrival at or departure from work anchor.

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Commute Times by Mode and Vehicle Ownership As shown in table 69, nonmotorized commutes are the shortest in terms of time, and public transit commutes are the longest. However, table 69 suggests a two-tiered commuting system divided not just by mode, but by the ability to choose between modes. Unsurprisingly, "choice" commuters from vehicle-sufficient households (i.e., those with vehicles available for every potential driver) are more likely to commute by POV. However, choice commuters enjoy commutes of a shorter duration even when they choose an alternative mode.
The average transit commute for a captive rider54 is 20.4 minutes longer than the average choice transit commute (table 69), despite the fact that the average distance of captive riders' commutes is 3.1 miles shorter than a choice rider's transit commute (14.2 miles versus 17.3 miles) (not tabulated). In other words, on average, commuters who use transit out of necessity receive worse service than commuters who take transit by choice.
There are many potential explanations for this inequality. Choice riders can opt out of the transit system if the available routes do not align with their needs, while captive riders may have to use transit regardless of travel times. Service jobs, a prominent source of low-income employment, may be more geographically dispersed than white-collar jobs, rather than being concentrated in higher-density employment nodes that can be more efficiently served by transit. It is possible
54 A "captive" rider is a commuter from a zero-vehicle or vehicle-deficit household (i.e., a household which owns at least one car, but has fewer cars than potential drivers). The transit mode share for commutes made by residents of low-income vehicle-deficit households (<$35,000 per year) is more than double the rate of transit use by vehicle-sufficient households at the same income level (5.0 percent versus 2.0 percent). This suggests that when residents of vehicle-deficit households use transit, they are likely to be doing so because they were not able to drive or get a ride from a household member.
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that choice riders may be more likely to complain to transit operators, increasing the likelihood that their issues will be addressed.

Table 69. Work travel duration by mode and vehicle ownership.

Commute Duration

Mean

Median

(Minutes) (Minutes)

Work Journey Duration

Percent

Mean

Median Percent

1 Hour (Minutes) (Minutes) 1 Hour

All Work Journeys 0100 Miles

Household Vehicle Ownership* Zero-vehicle Vehicle-deficit Vehicle-sufficient

39.2 32.8 32.1

25 24.4% 42.5 25 13.3% 35.2 27 13.3% 35.7

30 27.8% 27 15.3% 30 17.2%

Mode POV Nonmotorized Public transit or other bus/train Other ground or water Multiple modes

31.6 17.0 69.5 17.5 56.5

26 12.3% 34.8 15 5.1% 17.7 63 58.0% 70.8 15 0.9% 17.6 52 42.3% 81.0

30 15.7% 15 5.7% 63 58.0% 15 0.9% 79 62.4%

Vehicle-sufficient Household Work Travel ("Choice" Commuters)

POV

31.9

27 12.8% 35.3

Nonmotorized

12.5

5 0.7% 13.6

Public transit or other bus/train

58.2

60 50.7% 58.2

Multiple modes

43.1

38 27.8% 77.2

30 16.5% 5 2.1%
60 50.7% 61 57.3%

Vehicle-deficit and Zero-vehicle Household Work Travel

POV

30.5

25 10.5% 33.0

26 12.8%

Nonmotorized

20.2

15 8.3% 20.7

15 8.3%

Public transit or other bus/train

78.6

72 63.8% 80.9

72 63.8%

Multiple modes

71.6

79 58.8% 85.3

85 68.2%

* Vehicle-sufficient households have at least one vehicle for each potential driver. Vehicle-deficit households have at least one

vehicle, but fewer vehicles than potential drivers. Due to inadequate sample size, trips with the mode "other" are not subdivided by vehicle ownership.

Whatever the causes, the effect is that transit-dependent commuters are experiencing worse transit service than their choice-rider neighbors. To reduce this inequality, it is therefore important to examine potential discrepancies in vehicle frequency and route density, and to examine whether current transit routings match the needs of low-income commuters.

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Vehicle ownership also affects nonmotorized commutes; 8.3 percent of nonmotorized commutes by captive pedestrians and cyclists lasted an hour or more, versus just 0.7 percent of nonmotorized choice commutes. Inspiring more commuters to choose walking and cycling is a laudable goal, but this finding suggests that captive pedestrians and cyclists need a different kind of assistance. In addition to facilitating access to other transportation options, it is critical to ensure that the locations where these obligatory nonmotorized commuters walk and bike have adequate and safe infrastructure.
Transit service improvements and subsidies for obligatory nonmotorized commuters could help improve transportation for commuters without vehicle access. However, it should be noted that the time discrepancy between commuters in vehicle-sufficient and vehicle-insufficient households vanishes when examining commutes made by cars (as either driver or passenger). Helping all these workers acquire cars could have serious consequences for sustainability and congestion, but there is no denying that it would be a substantial quality of life improvement for many, not just in terms of shortening an existing commute but also in terms of expanding future employment opportunities (O'Regan and Quigley 1998, Shen and Sanchez 2005, Blumenberg and Pierce 2014, Blumenberg 2016, Loukaitou-Sideris 2016, Smart and Klein 2018). It may be beneficial to explore ways of promoting community carpooling or carsharing and providing microtransit service to a wider variety of routes.
Demographic Differences in Commute Duration Table 70 and table 71 show work travel duration broken down by demographic characteristics. The usual commute, travel day commute, and travel day work journey are shown. As mentioned in the section on Work Travel Distance, in terms of distance, men's commutes are, on average,
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longer than women's by 1.8 miles. One notable finding here is that this difference does not extend to work travel duration (table 70). While men's median "usual" commute time is 5 minutes longer than that of women, the difference for observed commutes on the travel day is only 2 minutes. When the full work journey is considered, men's and women's commute times differ by just 30 seconds on average (and the medians are identical). In other words, while female workers do not produce as much VMT as male workers do, from a quality of life standpoint, female workers spend just as much time commuting as their male colleagues do.
Similarly, while Black commuters' commute distance was almost identical to that of white workers, Black workers' commutes last, on average, 7.4 minutes longer than the commutes of white workers (table 71). This was the largest intergroup difference observed. It may relate to racial differences in mode choice and residential patterns (e.g., work journey durations are longer in Atlanta and when using transit). In terms of economic and occupational characteristics, the longest commutes were found among blue-collar workers.
Total Daily Commute Burden To understand the cumulative effects of work travel on Georgians' time use, in addition to examining the duration of individual one-way commutes, it is worth examining the total amount of time spent commuting per day (i.e., the commute burden). Table 72 and table 73 show the total amount of time spent traveling to and from work on a typical work day, broken down by various demographic characteristics. As a point of comparison, total daily commute distance is also shown.
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Table 70. Work travel duration by MPO tier, gender, education, and age.

"Usual" Commute

Travel Day Commute

Travel Day Work Journey

Mean Median Percent Mean Median Percent Mean Median Percent (Minutes) (Minutes) 1 Hour (Minutes) (Minutes) 1 Hour (Minutes) (Minutes) 1 Hour

Statewide*

30.9

25

11.9%

32.5

MPO Tier (Worker Residence)

1. Atlanta MPO

34.6

30

15.7%

36.6

2. Medium MPOs

23.4

20

5.0%

27.0

3. Small MPOs

23.0

20

5.9%

24.6

4. Non-MPO

27.3

20

10.9%

28.1

Gender

Male

31.4

25

13.0%

32.8

Female College-educated

29.2

20

11.3%

32.0

29.7

25

11.5%

32.0

College-educated men

29.5

25

11.5%

31.7

College-educated women

29.8

25

11.4%

32.4

Without 4-year College Degree

31.0

25

12.8%

32.7

Men without college degree

32.6

25

14.0%

33.5

Women without college degree

28.8

20

11.2%

31.6

Age Cohort

Millennial and Gen Z (1836)

28.8

20

10.7%

31.8

Generation X (3752)

32.2

25

13.4%

33.6

Pre-retirement age Baby Boomer (5364)

31.2

25

13.2%

32.6

Retirement age (65+)

27.9

20

13.2%

27.9

* Excludes commutes greater than 100 miles one way. Weighted N=1.96 billion per year.

Defined as bachelor's degree or higher.

26

10.3%

35.9

30

17.6%

40.2

20

7.0%

31.0

20

7.4%

27.5

21

9.7%

30.6

27

14.1%

36.0

25

13.0%

35.5

28

12.2%

35.6

27

12.0%

35.6

28

12.5%

35.7

25

14.6%

35.9

28

15.4%

36.4

25

13.4%

35.3

25

13.1%

35.1

30

13.8%

36.8

27

15.1%

36.3

21

9.9%

32.0

30

13.1%

30

21.3%

25

10.4%

20

10.2%

25

12.8%

30

17.4%

30

16.6%

30

16.2%

30

16.2%

30

16.1%

30

17.6%

30

18.1%

28

17.0%

28

16.2%

30

17.0%

30

18.8%

25

17.1%

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Table 71. Work travel duration by income, race, occupation, and worker type.

"Usual" Commute Mean Median
(Minutes) (Minutes)

Travel Day Commute

Travel Day Work Journey

Percent Mean Median Percent Mean Median Percent

1 Hour (Minutes) (Minutes) 1 Hour (Minutes) (Minutes) 1 Hour

Statewide*

30.9

25

11.9%

32.5

Annual Household Income

<$35,000

30.0

20

13.4%

32.3

$35,000 to $49,999

28.2

20

9.0%

30.0

$50,000 to $74,999

31.5

25

12.5%

33.5

$75,000 to $99,999

31.3

25

12.2%

33.0

$100,000+

30.5

25

11.9%

32.5

Race

White non-Hispanic only

28.0

20

10.5%

30.5

Black and Black multiracial

35.4

30

15.8%

36.6

Other

28.7

25

11.0%

30.5

Occupational Category (NHTS-designated Workers Only)

Sales or service

28.8

20

11.8%

31.0

Clerical or administrative support Blue collar

30.9

25

13.9%

33.1

34.6

25

15.4%

34.5

Professional, managerial, or technical

29.6

25

10.8%

32.7

Worker Type (NHTS-designated Workers Only)

Part-time

28.6

20

11.2%

30.4

Full-time

30.8

25

12.5%

32.9

* Excludes commutes greater than 100 miles one way. Weighted N=1.96 billion per year.

Blue collar refers to manufacturing, construction, maintenance, or farming.

26

10.3%

35.9

25

14.6%

35.9

25

11.3%

32.4

28

13.6%

36.3

27

14.3%

36.1

30

13.2%

36.6

25

11.5%

33.3

30

17.6%

40.8

25

12.5%

33.6

25

12.5%

34.5

28

14.0%

36.9

30

16.8%

36.8

27

13.0%

36.1

20

12.0%

34.0

28

14.0%

36.2

30

13.1%

27

17.7%

25

13.8%

30

16.7%

30

17.8%

30

17.7%

28

14.5%

30

22.2%

28

15.0%

27

15.8%

30

16.6%

30

18.6%

30

17.2%

25

16.1%

30

17.2%

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The median daily commute duration is 55 minutes (or 60 minutes if the full work journey is included). However, 12.3 percent of commuters spend 2 hours or more traveling to and from work. These heavy commute burdens are not equally distributed; 2-hour commute burdens are more than twice as common in the Atlanta MPO as elsewhere in the state (16.6 percent in Atlanta versus 6.27.1 percent elsewhere). If the whole work journey is included, more than one in five Atlanta-MPO employees has a commute burden of 2 or more hours. As with individual commute durations, Black residents also have the longest total average commute burden (73.6 minutes, versus 62.1 and 64.5 for white non-Hispanic and other race, respectively). Retirement-age commuters generally have somewhat smaller median commute burdens than other age groups. They also have a lower proportion of 2-hour commute burdens. As a result, the age difference in average commute burden is larger than the difference in median commute burden.
CONCLUSION This chapter has considered Georgians' travel to and from job sites. As telecommuting becomes more common, many Georgia workers "travel" to work without leaving their houses. Some workers also benefit from flexible work schedules, enabling them to avoid peak-hour work travel, and/or better balance work and life demands. The next chapter will focus on patterns associated with telecommuting and work schedule flexibility.
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Table 72. Total daily commute burden by MPO tier, gender, education, and age.

Daily Commute Totals Minutes Minutes Percent Miles (Mean) (Median) 2 Hours (Mean)

Daily Work Journey Totals Minutes Minutes Percent Miles (Mean) (Median) 2 Hours (Mean)

Statewide*

66.2

55

12.3%

27.8

73.0

MPO Tier (Worker Residence)

1. Atlanta MPO

74.4

63

16.6%

28.6

81.7

2. Medium MPOs

56.5

47

7.1%

23.4

64.7

3. Small MPOs

50.6

42

6.2%

22.4

56.6

4. Non-MPO

56.5

45

6.4%

31.5

61.4

Gender

Male

66.9

56

12.9%

29.5

73.5

Female

65.3

54

11.6%

25.6

72.4

College-educated

66.1

58

11.7%

27.2

73.4

College-educated men

65.8

57

12.2%

27.2

73.9

College-educated women

66.3

60

11.0%

27.2

72.9

Without 4-year College Degree

66.2

53

12.7%

28.1

72.7

Men without college degree

67.5

55

13.3%

30.9

73.3

Women without college degree

64.4

50

12.0%

24.4

71.9

Age Cohort

Millennial and Gen Z (1836)

65.4

55

11.6%

26.5

72.1

Generation X (3752)

67.5

57

12.9%

29.8

74.0

Pre-retirement age Baby Boomer (5364)

66.9

52

13.8%

27.4

74.4

Retirement age (65+)

58.2

50

7.7%

23.7

66.8

* Includes all active commuters with no supercommutes on travel day (weighted N=2.6 million).

Defined as bachelor's degree or higher.

60

16.3%

30.2

70

21.0%

31.2

53

10.8%

25.9

45

9.3%

24.5

50

9.9%

33.8

60

16.9%

32.0

60

15.5%

28.0

65

16.1%

29.9

65

17.3%

30.4

65

14.9%

29.4

60

16.4%

30.5

60

16.6%

33.0

59

16.0%

26.9

60

15.0%

29.0

63

16.7%

32.2

60

18.0%

30.0

54

17.4%

26.4

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Table 73. Total daily commute burden by income, race, occupation, and worker type.

Daily Commute Totals

Daily Work Journey Totals

Minutes Minutes Percent Miles Minutes Minutes Percent Miles

(Mean) (Median) 2 Hours (Mean) (Mean) (Median) 2 Hours (Mean)

Statewide*

66.2

55

12.3%

27.8

73.0

60

16.3%

30.2

Annual Household Income

<$35,000

65.9

52

12.4%

23.5

73.3

60

16.8%

25.6

$35,000 to $49,999

63.5

53

10.4%

29.6

68.5

58

12.9%

31.5

$50,000 to $74,999

67.4

55

12.9%

31.4

73.1

60

15.9%

33.8

$75,000 to $99,999

67.8

57

13.2%

29.4

74.2

63

16.4%

31.9

$100,000+

65.7

60

12.2%

28.2

74.0

65

17.8%

31.4

Race

White non-Hispanic only

62.1

52

10.0%

28.1

68.0

60

13.4%

30.4

Black and Black multiracial

73.6

60

16.4%

28.1

82.2

65

21.4%

31.0

Other

64.5

52

11.5%

25.5

71.0

60

15.3%

27.7

Occupational Category (NHTS-designated Workers Only)

Sales or service

63.2

52

10.6%

26.3

70.4

59

15.0%

28.7

Clerical or administrative support Blue collar

66.7

53

14.2%

25.8

74.4

60

17.4%

28.6

69.4

55

14.3%

31.0

74.1

60

16.8%

32.9

Professional, managerial, or technical

67.1

59

12.5%

28.1

74.2

65

16.8%

30.8

Worker Type (NHTS-designated Workers Only)

Part-time

61.7

49

9.4%

22.8

69.5

52

15.1%

25.3

Full-time

67.3

57

13.0%

28.9

73.8

60

16.6%

31.4

* Includes all active commuters with no supercommutes on travel day (weighted N=2.6 million).

Blue collar refers to manufacturing, construction, maintenance, or farming.

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CHAPTER 3. TELEWORKING AND WORKER SCHEDULE FLEXIBILITY
CHAPTER 3 SUMMARY
This chapter analyzes workplace forces that afford workers more choice in how, when, or if they travel to work. In particular, its focus is on teleworking and schedule flexibility.
Overview includes technical and vocabulary notes and provides an overview of schedule flexibility and teleworking as an overall proportion of worker activity in Georgia. It also provides summary statistics about the number of telecommuters each day by MPO tier.
Work Flexibility for Whom? examines access to flexible work conditions. It considers demographic differences between Georgians who have the ability to flex their schedule or work location and those whose job is less flexible. Some of the differences in work flexibility are related to job function or prestige; workers in high-income households and with college degrees are more likely to have a flexible worksite or schedule. However, using regression analysis, we document that workers' likelihood of being allowed to telework or set their own schedule is also influenced by where they live and demographic factors that are not related to the nature of the work in which they engage.
The Effects of Flexible Scheduling examines the effects of schedule flexibility on arrival times to and departure times from work. In general, workers with flexible schedules tend to arrive at and depart from work later than workers with inflexible schedules. While schedule flexibility influences departure and arrival times, it was not found to have a significant effect on commute duration.
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Frequency of Teleworking examines the incidence of teleworking by looking at: (1) the monthly frequency of teleworking by telecommute-eligible workers, and (2) commuting and telecommuting on the travel day. In particular, we focus on differences by region, gender and caregiver status, mobility impairment, and distance between the home and work location.
Work flexibility, particularly teleworking, has taken on new significance with the onset of the coronavirus disease 2019 (COVID-19) pandemic in 2020. The data analyzed in this report obviously reflect pre-COVID-19 conditions. With respect to teleworking prevalence (i.e., adoption and frequency; Frequency of Teleworking), it remains to be seen what the postCOVID-19 "new normals" will be; the 2017 NHTS data will provide an important benchmark against which to gauge future levels of teleworking. With respect to telework eligibility (see sections on Descriptive Statistics on Access to Flexible Schedule and Work Location and Logistic Regression Analysis of Eligibility for Flexible Work Location), changes may be more uneven. The pandemic has made it clear that many kinds of essential work cannot be accomplished remotely. On the other hand, the pandemic may also expand the number and types of job functions that are considered possible to accomplish remotely, and in many instances transform teleworking from a perk to a public health measure seen to be in the interests of employers as well as employees.
OVERVIEW
Types of Work Flexibility Employers provide Georgia workers with two kinds of flexibility: scheduling flexibility, or the ability to adjust start and end times, and location flexibility, or the ability to work from home or
166

a "third place" such as a coffee shop or coworking space (Oldenburg 1989).55 As shown in table 74, 40.9 percent of Georgia workers have work schedule flexibility. Twenty-seven percent have flexible work locations (either telecommuting periodically56 or working from home on a regular basis). There is substantial overlap between workers with schedule flexibility and those with location flexibility; 22.3 percent of workers have both schedule and location flexibility, while 18.7 percent have schedule flexibility only. Most workers who have work location flexibility also have schedule flexibility; just 4.9 percent of workers have location flexibility without schedule flexibility.

Table 74. Eligibility for job flexibility among Georgia workers.

Flexibility Type

Schedule

Schedule Location

Only

Only*

and Location*

All workers

18.7%

4.9%

22.3%

MPO Tier 1. Atlanta MPO

19.2%

5.7%

27.2%

2. Medium MPOs

18.2%

4.0%

15.1%

3. Small MPOs

18.5%

3.1%

15.1%

4. Non-MPO counties

17.4%

3.9%

15.9%

* Includes home-based workers and telecommute-eligible workers.

Totals

Schedule

Location*

No

With or Without With or Without

Flexibility

Location

Schedule

54.2%

40.9%

27.1%

47.9% 62.7% 63.2% 62.8%

46.4% 33.3% 33.6% 33.3%

32.9% 19.1% 18.2% 19.8%

A slight majority of workers in the Atlanta MPO area have flexibility with regard to schedule, location, or both. Elsewhere in the state, 63 percent of workers have neither schedule nor location flexibility.

55 Flexible scheduling, or flextime, is assessed by asking participants, "At your primary job, do you have the ability to set or change your own start time?" If the response is yes, then for simplicity of exposition, workers are considered to have a flexible schedule (or "schedule flexibility"), whether or not they choose to take advantage of the option to vary their work schedule. Location flexibility is assessed with a series of questions; see Worker Telework Eligibility Categories.
56 Table 74 and the accompanying text include the 3 percent of workers, shown in figure 18, who are eligible to telecommute but have not done so in the past 30 days.
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Definitions and Technical Notes Teleworking refers to working and interacting remotely with an employer, colleagues, and clients using the internet and/or telephone. Typically, a teleworker works from home. However, some may work out of nearby coffee shops, coworking spaces, or similar facilities--a type of "third place," to use the term popularized by Oldenburg (1989) and often applied in teleworking contexts (e.g., Venolia et al. 2014).
Teleworking and telecommuting are often used interchangeably. In this report, however, telecommuting refers to a specific case of teleworking, in which workers whose primary workplace is outside the home substitute teleworking for their usual conventional commute. Teleworking is an umbrella term that comprises telecommuting, but also employment where home is the "usual" work site (i.e., home-based work).
Worker Telework Eligibility Categories For the purposes of this report, with respect to teleworking, there are three types of workers:
Home-based workers, or workers who usually or always work from home. Home-based workers include people who work for remote employers (such as online tutoring companies) and self-employed people who operate a business out of their home office.
Telecommute-eligible workers, or workers whose primary workplace is outside the home, but "have the option of working from home or an alternate location."57 In other words, with telecommuting, teleworking is substituting for a conventional commute. Although being an eligible telecommuter does not require taking advantage of this option, most
57 This is the wording used in the NHTS questionnaire; see p. 44 of the Retrieval Questionnaire found at https://nhts.ornl.gov/assets/2016/NHTS_Retrieval_Instrument_20180228.pdf.
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(78 percent) telecommute-eligible workers report telecommuting at least once in the past 30 days (figure 18). Telecommute-ineligible workers, or nonhome-based workers who do not have the option of telecommuting. Home-based workers and telecommute-eligible workers are considered to have a flexible work location. These categories are measured by the NHTS with a series of questions. Workers are first asked if they "usually" work from home. If so, they are considered home-based workers. Nonhome-based workers are asked a follow-up question about whether they "have the option of working from home or an alternate location instead of going into your/their primary workplace." Workers who answer "yes" to this second question are considered telecommute-eligible; those who answered "no" are considered telecommute-ineligible.58 Telecommute-eligible workers are asked about the number of days they worked from home over the past 30 days; this information is not asked of home-based or telecommute-ineligible workers. However, the travel day telework categories used in this report (see Travel Day Work and Telework Categories) do cover teleworking by all workers, regardless of their official telework status.
58 This question wording may result in the self-misclassification as "telecommute-eligible" of some selfemployed workers whose jobs routinely take them to "alternate locations" (e.g., plumbers, electricians, etc.), because they do not "usually" work from home (which would have classified them as a "home-based worker").
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Figure 18. Pie chart. Telework category of Georgia workers and telecommuting frequency, past 30 days.
Travel Day Work and Telework Categories In addition to ascertaining the telework eligibility status of individuals, it is also of interest to analyze whether they actually teleworked on the day the travel diary was completed. We define an active worker as someone who reported working at a home or nonhome location on the travel diary day. Because respondents are not asked directly about travel day teleworking, that classification was made based on respondents' stated trip purposes (or, if no trips were made, stated reason for not making any trips).
Specifically, respondents have engaged in a conventional commute if they meet either of the following criteria:
Reported working for pay, engaging in work-related business, or working "from home" at their work address.
Reported working for pay or engaging in work-related business at any location besides their home and work addresses.
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This definition diverges from the definition used in chapter 2 on commuting, by including trips for the purposes of off-site work-related meetings. The reason for this methodological difference is that the most relevant question for the present analysis is whether or not the worker leaves the house (versus accomplishing the same work from home), whereas for previous analysis the focus was on travel to and from the workplace. For the purposes of analyzing teleworking, the relevant distinction is whether or not work causes a worker to leave the home.
Participants have teleworked on the travel day if they meet any of the following criteria:
Reported no trips on the travel day with the stated reason that they worked from home. Described primary activity at any location visited on the travel day as "work from home
(paid)."59 Reported working for pay or engaging in a work-related meeting at their home address.60
Travel day teleworking is subdivided into exclusive telework (i.e., the participant did not work or conduct work business outside of the home on the travel day), and mixed telework, where the participant reported both a conventional commute and teleworking.61 Across the 7-day week, on average, 5.2 percent of Georgia adults, or 13.1 percent of active workers, report teleworking on a given day (figure 19).
59 Twenty-two participants reported working specifically "from home" at a location other than home or work; since these may reflect telecommuting from a friend or relative's house or a secondary address, this report defers to the participant's judgment and classifies them as travel-day telecommuters. The exception to this rule is that if respondents reported working from "home" at their work location, it was recoded as working outside the home.
60 Unfortunately, the data do not provide a way to distinguish between off-site work meetings and telework at a "third place" (such as a caf), so this definition of telework does not include third-place teleworking.
61 These two categories are sometimes described as "full day" and "partial day" telecommuting; this report generally avoids that terminology because its classification does not depend on the number of hours worked.
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Figure 19. Pie chart. Travel day working and teleworking among Georgia adults.
It should be noted that participants can report teleworking on the travel day even if their job does not usually allow telecommuting. This may reflect a second job, or an exception granted by an employer in the case of an illness, weather emergency, or other circumstance.62 While these data were collected before the outbreak of COVID-19, recent events have illustrated that some workers who are not normally considered telework-eligible can sometimes be given the option of working from home in extraordinary circumstances.
The travel diary data unfortunately does not allow for distinguishing between nonhome teleworking sites (such as coworking spaces) and other work locations (such as a second job, alternate office address, or visit to a client). To be conservative, therefore, in this analysis travel day telecommuting refers only to working from home, or to reporting a nonhome activity as such; any other work-related nonhome activity is not assumed to be teleworking. In that respect, this analysis will underestimate the amount of teleworking occurring.
62 In addition, a small percentage of respondents not designated as workers by the NHTS reported teleworking on the travel day. They are included in figure 19 for completeness. See Overview of Travel Day Telecommuting for further discussion.
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In addition, the travel day telecommuting metric likely (even further) underestimates the true incidence of telecommuting because it is based on the reported primary activity at a location, and each location is limited to a single listed activity. If individuals are working from home for an extended period of time, it is likely that they conduct a mix of work and regular home activities, and which purpose participants choose to list may be somewhat idiosyncratic. Even with this caveat, however, measuring travel day telecommuting provides valuable information to this analysis.
Overview of Travel Day Telecommuting As shown in table 75, on a typical weekday, nearly 498,000 Georgia adults will telecommute, representing 6.5 percent of the total adult population. This is equivalent to 13 percent of people who worked on the travel day ("active workers") (not tabulated). There are clear regional differences, with telecommuting being most common in Atlanta.
As with the analysis of commuting travel in chapter 2, both commuting and telecommuting are observed by Georgians who are not officially categorized as workers.63 These "nonworkers" are included in table 75 for completeness. They represent a comparatively small group (just 151 respondents). Because the other survey items analyzed for this chapter were only asked of NHTS-defined workers, for consistency, throughout the remainder of the chapter, the researchers restrict the sample to NHTS-defined workers. Specifically, telecommuting by "nonworkers" is excluded from all analysis in this chapter aside from table 75 and figure 19.
63 The NHTS defines workers based on their workforce participation in the week leading up to the survey; economic activity on the travel day by "nonworkers" might reflect a change in employment status, irregular work, or an error.
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Table 75. Travel day working and teleworking by MPO tier.

Active Workers

Teleworked on Travel Day

Weekdays

Total Population

(Worked for Pay on Travel Day)

(Exclusively or Partially)

All adults* ages 18+

7,704,068

3,854,775 (50.0%) 497,627 (6.5%)

MPO Tier

1. Atlanta MPO

4,167,843

2,290,234 (55.0%) 354,766 (8.5%)

2. Medium MPOs

1,219,439

577,655 (47.4%)

48,334 (4.0%)

3. Small MPOs

777,264

351,886 (45.3%)

25,024 (3.2%)

4. Non-MPO

1,539,522

630,659 (41.0%)

64,090 (4.2%)

All workers* ages 18+

4,736,051

3,761,714 (79.4%) 471,779 (10.0%)

MPO Tier

1. Atlanta MPO

2,735,122

2,222,401 (81.3%) 335,322 (12.3%)

2. Medium MPOs

741,844

567,367 (76.5%)

44,741 (6.0%)

3. Small MPOs

474,303

358,100 (75.5%)

23,874 (5.0%)

4. Non-MPO

784,782

608,566 (77.5%)

60,964 (7.8%)

Weekends and Holidays All adults ages 18+

7,704,068

1,201,200 (15.6%) 169,268 (2.2%)

All workers ages 18+

4,736,051

1,155,736 (24.4%) 139,343 (2.9%)

All Days

All adults ages 18+

7,704,068

3,044,842 (39.5%) 397,404 (5.2%)

MPO Tier

1. Atlanta MPO

4,167,843

1,811,751 (43.5%) 285,910 (6.9%)

2. Medium MPOs

1,219,439

430,139 (35.3%)

37,264 (3.1%)

3. Small MPOs

777,264

278,552 (35.8%)

20,105 (2.6%)

4. Non-MPO

1,539,522

524,399 (34.1%)

54,126 (3.5%)

All workers ages 18+

4,736,051

2,972,715 (62.8%) 371,129 (7.8%)

MPO Tier

1. Atlanta MPO

2,735,122

1,772,912 (64.8%) 268,167 (9.8%)

2. Medium MPOs

741,844

418,858 (56.5%)

34,295 (4.6%)

3. Small MPOs

474,303

273,055 (57.6%)

18,368 (3.9%)

4. Non-MPO

784,782

507,891 (64.7%)

50,299 (6.4%)

* NHTS defines a worker as someone who worked for pay or profit or was temporarily absent from paid

employment in the week before completing their travel survey ("last week"). Of adults who were not classified

as workers, 2.4 percent reported working on the travel day, including 0.9 percent who reported teleworking.

These "nonworker" workers are included in totals for all adults, but not totals for workers.

Note: Percents shown are percent of row subpopulation (adults or workers).

However, it is worth noting that more than one third of these irregular workers--36 percent-- reported teleworking. This is a much higher rate than was observed among regular workers. This suggests that teleworking may be more common among people whose employment situations are

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not easily captured by the standard worker/nonworker binary measure. The issue of teleworking among irregular workers may therefore merit further study.

As table 75 shows, on a typical weekday, approximately 80 percent of workers will be actively working for pay. On weekends, this figure is 24 percent, and there is some variation across MPO tiers. Table 76 shows teleworking among these active workers.

Table 76. Travel day teleworking and conventional commuting among active workers.

Weekdays All workers* ages 18+

Exclusive Telework (Worked from Home
Only)

Mixed Telework (Worked from Home & Outside of Home)

Conventional Commute
(Worked Outside of Home Only)

All Teleworking (Exclusive + Mixed)

9.0%

3.6%

87.5%

12.5%

MPO Tier

1. Atlanta MPO

11.5%

3.6%

84.9%

15.1%

2. Medium MPOs

5.1%

2.8%

92.1%

7.9%

3. Small MPOs

3.0%

3.6%

93.3%

6.7%

4. Non-MPO Weekends and Holidays All workers ages 18+ All Days

5.8% 7.9%

4.2% 4.1%

90.0% 87.9%

10.0% 12.1%

All workers ages 18+

8.8%

3.7%

87.5%

12.5%

MPO Tier

1. Atlanta MPO

11.3%

3.8%

84.9%

15.1%

2. Medium MPOs

5.1%

3.1%

91.8%

8.2%

3. Small MPOs

3.6%

3.1%

93.3%

6.7%

4. Non-MPO

6.2%

3.7%

90.1%

9.9%

Note: Percentages shown are row percentages based on the population of active workers (workers who reported working for pay or

working from home for pay on the travel day).

* NHTS defines a worker as someone who worked for pay or profit or was temporarily absent from paid employment in the week

before completing their travel survey ("last week").

As table 76 shows, at least 12.5 percent of active workers on a typical day will be working from home for some or all of their work day. Teleworking, particularly exclusive teleworking, is much more common in the Atlanta region than it is in the rest of the state. The area with the secondhighest rate of teleworking is non-MPO counties, where nearby work opportunities may be less

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available. Rates of teleworking are similar on weekdays and weekends (12.5 percent and 12.1 percent, respectively).
Due to sample size concerns, weekend telecommuting is not disaggregated by MPO tier.
WORK FLEXIBILITY FOR WHOM? The opportunity to work from home or have a flexible schedule is not evenly distributed among Georgia workers. This section examines differences by region, employment characteristics, and demographic characteristics in who has the ability to choose their own work location or schedule.
Workers are considered to have a flexible schedule if they have some leeway over what time they start and stop their work day. For this study, the category of flexible location includes: (1) workers who usually work outside the home but have the ability to telecommute (even if they have not done so within the past 30 days), and (2) home-based workers. Workers who fit neither category do not have location flexibility.
Descriptive Statistics on Access to Flexible Schedule and Work Location As shown in table 77, 45.9 percent of workers have access to some form of flexibility, whether that be schedule (18.7 percent), location (4.9 percent), or both (22.3 percent). Work flexibility is more common in the Atlanta MPO region, where a slight majority of workers have some form of flexibility, than in the rest of the state. Workers with higher education levels and incomes are more likely to have flexible schedules and locations. Work flexibility is also more common among professional, managerial, and technical jobs than other occupation categories.
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Table 77. Employment flexibility by MPO, job characteristics, educational attainment, and income. 177

However, access to employment flexibility is also correlated with a number of demographic characteristics that are not directly related to job type or function (table 78). White workers are more likely to have flexible jobs than workers of other races. Workers with a mobility impairment are slightly more likely than nondisabled workers to have a flexible schedule, and much more likely to have a flexible location (38.5 percent versus 26.9 percent). It is unclear whether this is an accommodation offered by employers to workers with disabilities, or if mobility-impaired workers unable to find flexible employment simply exit the labor market. Men are more likely to have a flexible schedule and location than women. This gap is more pronounced among caregivers for children under the age of 16 than it is among noncaregivers (table 79), despite the fact that the burden of childcare falls disproportionately on women (McQuaid and Chen 2012, Loukaitou-Sideris 2016). As the next section (Logistic Regression Analysis of Eligibility for Flexible Work Schedule) will show, many of these demographic differences in employment flexibility persist after controlling for other factors.
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Table 78. Employment flexibility by sex, caregiver status, age, race, and mobility impairment.

Flexibility Type

Totals

Flexible Schedule Flexible Location*

Schedule Only

Location* Schedule &

No

Only

Location Flexibility

With or Without Location

With or Without Schedule

All workers

18.7%

4.9%

22.3%

54.2%

40.9%

27.1%

Sex Male
Female Caregiver Status

19.9% 17.2%

4.4% 5.4%

23.7% 20.6%

52.0% 56.8%

43.6% 37.8%

28.1% 26.0%

Noncaregiver Caregiver, youngest child ages 015

18.4% 19.1%

5.3% 4.1%

21.2% 24.0%

55.1% 52.7%

39.6% 43.1%

26.5% 28.2%

Age Cohort Millennial and Gen Z (1836) Generation X (3752) Pre-retirement age Baby Boomer (5364) Retirement age (65+)

15.5% 19.7% 20.9% 25.5%

4.9% 4.6% 5.9% 1.9%

16.6% 25.4% 24.5% 33.4%

63.0% 50.2% 48.7% 39.1%

32.2% 45.1% 45.3% 58.9%

21.5% 30.0% 30.4% 35.4%

Race White non-Hispanic only Black and Black multiracial Other

20.9% 14.4% 18.8%

3.5% 7.5% 4.6%

26.0% 17.0% 18.8%

49.6% 61.1% 57.8%

46.9% 31.3% 37.6%

29.4% 24.5% 23.4%

Mobility Impairment Absent Present

18.8% 7.8%

4.9% 2.5%

22.0% 36.0%

54.2% 53.6%

40.9% 43.8%

26.9% 38.5%

* Includes home-based workers and telecommute-eligible workers.

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household with a child 515 years old.

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Table 79. Employment flexibility by caregiver status.

Flexibility Type

Totals

Flexible Schedule Flexible Location*

Schedule Only

Location* Schedule &

No

Only

Location Flexibility

With or Without Location

With or Without Schedule

Male noncaregiver

18.9%

5.0%

22.4%

53.8%

41.3%

27.3%

Female noncaregiver Male caregiver

17.8% 21.6%

5.6% 3.4%

19.9% 26.0%

56.7% 49.1%

37.7% 47.6%

25.5% 29.4%

Female caregiver

16.2%

5.1%

21.7%

57.1%

37.9%

26.8%

By Age of Youngest Child Youngest, ages 04

18.7%

4.1%

23.5%

53.7%

42.2%

27.6%

Youngest, ages 515

19.5%

4.1%

24.5%

51.9%

44.0%

28.7%

Household Type Male co-caregiver

21.6%

3.4%

26.1%

48.8%

47.7%

29.6%

Female co-caregiver

16.3%

4.7%

22.9%

56.1%

39.2%

27.6%

Male single caregiver

21.6%

0.0%

20.0%

58.4%

41.6%

20.0%

Female single caregiver

15.7%

6.5%

17.1%

60.7%

32.8%

23.6%

Household Type and Age of Youngest Child

Male co-caregiver, youngest ages 04

21.4%

3.4%

24.8%

50.5%

46.1%

28.2%

Female co-caregiver, youngest ages 04

14.2%

5.5%

23.3%

57.1%

37.4%

28.8%

Female single caregiver, youngest ages 04

22.1%

2.7%

12.0%

63.2%

34.1%

14.6%

Male co-caregiver, youngest ages 515

21.8%

3.5%

27.5%

47.2%

49.3%

31.0%

Female co-caregiver, youngest ages 515

18.3%

4.0%

22.5%

55.3%

40.7%

26.4%

Male single caregiver, youngest ages 515

23.1%

0.0%

16.0%

60.9%

39.1%

16.0%

Female single caregiver, youngest ages 515 12.5%

8.4%

19.7%

59.4%

32.1%

28.1%

* Includes home-based workers and telecommute-eligible workers.

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household with a child 515 years old.

Male single caregivers with youngest child ages 04 are omitted due to small sample size (5 individuals).

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Sixty percent of college-educated workers have a flexible schedule, location, or both (table 80), whereas only 34 percent of workers without a 4-year college degree have location or schedule flexibility (table 81).
In addition to the direct difference in flexibility between workers with and without a college degree, the relationship between some other demographic / employment characteristics and flexibility differs between those two groups. For example, for low-education workers, sales and service jobs are among the least likely to have flexibility (along with blue-collar jobs), whereas for high-education workers, sales and service jobs are among the most likely to have flexibility (along with professional, managerial, and technical jobs). Similarly, the gender gap in flexibility is much larger among college-educated workers. Among high-education workers, there is a 14 percentage-point difference between genders: 67 percent of college-educated male workers have some form of flexibility, versus 53 percent of similarly educated women (table 82). Approximately one third of low-education workers have some kind of employment flexibility, regardless of gender (table 83).
A number of demographic and employment characteristics are correlated with each other quite apart from flexibility considerations, and thus, in addition to examining the relationship to flexibility of one or two other variables at a time, it is desirable to be able to assess the relationship of one variable to flexibility while simultaneously controlling for as many other variables as possible. In the next two subsections, we use logistic regression to isolate the effects of different demographic variables and job characteristics on work schedule and location flexibility, respectively.
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Table 80. Employment flexibility for workers with a 4-year college degree by location and job characteristics.

Flexibility Type

Schedule Only

Location* Only

All workers with a 4-year college degree

20.6%

4.7%

MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO counties

19.8% 23.4% 26.4% 17.5%

5.3% 4.3% 3.8% 1.7%

Worker Type Full-time Part-time

21.6% 20.4%

4.6% 4.7%

Occupational Category Sales or service
Clerical or administrative support Blue collar

14.7% 23.3% 19.3%

5.2% 7.5% 1.6%

Professional, managerial, or technical

21.8%

4.5%

Annual Household Income <$35,000 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+

17.1% 25.2% 15.4% 22.8% 21.6%

6.3% 3.0% 4.6% 4.4% 4.7%

* Includes home-based workers and telecommute-eligible workers.

Blue collar refers to manufacturing, construction, maintenance, or farming.

Schedule & Location
35.0%

No Flexibility 39.7%

Totals

Flexible Schedule Flexible Location*

With or Without Location

With or Without Schedule

55.6%

39.7%

40.2% 21.5% 28.6% 23.0%

34.7% 50.7% 41.1% 57.8%

60.0% 45.0% 55.0% 40.5%

45.5% 25.8% 32.5% 24.6%

34.9% 35.0%

38.9% 39.8%

56.6% 55.4%

39.5% 39.7%

41.2% 13.3% 20.8% 36.7%

38.9% 55.9% 58.3% 36.9%

55.9% 36.6% 40.0% 58.6%

46.4% 20.8% 22.4% 41.2%

19.6% 18.2% 28.0% 31.6% 44.7%

57.0% 53.6% 52.1% 41.2% 29.0%

36.6% 43.4% 43.3% 54.4% 66.3%

25.9% 21.2% 32.6% 36.0% 49.4%

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Table 81. Employment flexibility for workers without a 4-year college degree by location and job characteristics.

Flexibility Type

Schedule

Location*

Only

Only

All workers without a 4-year college degree

17.0%

5.0%

MPO Tier 1. Atlanta MPO

18.3%

6.1%

2. Medium MPOs

14.3%

3.7%

3. Small MPOs

15.0%

2.8%

4. Non-MPO counties

17.3%

4.7%

Worker Type Full-time

14.6%

4.3%

Part-time

17.8%

5.2%

Occupational Category Sales or service

12.5%

6.4%

Clerical or administrative support Blue collar

20.4% 17.6%

3.1% 4.0%

Professional, managerial, or technical

21.2%

4.8%

Annual Household Income <$35,000

14.5%

6.1%

$35,000 to $49,999

11.5%

3.5%

$50,000 to $74,999

20.7%

4.9%

$75,000 to $99,999

23.6%

4.1%

$100,000+

20.5%

4.8%

* Includes home-based workers and telecommute-eligible workers.

Blue collar refers to manufacturing, construction, maintenance, or farming.

Schedule & Location 12.1%
12.8% 10.4%
9.1% 13.5%
12.5% 11.9%
11.1% 11.9%
7.8% 17.9%
8.3% 9.5% 11.0% 13.9% 21.3%

No Flexibility
65.9%
62.8% 71.7% 73.1% 64.5%
68.6% 65.1%
70.0% 64.6% 70.6% 56.0%
71.1% 75.4% 63.4% 58.4% 53.4%

Totals
Flexible Schedule With or Without 29.1%
31.1% 24.6% 24.0% 30.8%
27.1% 29.7%
23.6% 32.3% 25.4% 39.2%
22.8% 21.1% 31.7% 37.5% 41.8%

Flexible Location* With or Without 17.1%
18.9% 14.1% 11.9% 18.2%
16.8% 17.2%
17.5% 15.0% 11.9% 22.8%
14.4% 13.0% 15.8% 18.1% 26.1%

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Table 82. Employment flexibility among workers with a 4-year college degree by sex, caregiver status, age, race, and mobility impairment.

Flexibility Type

Totals

Schedule

Location* Schedule &

No

Flexible Schedule Flexible Location*

Only

Only

Location

Flexibility

With or Without

With or Without

All workers with a 4-year college degree

20.6%

4.7%

35.0%

39.7%

55.6%

39.7%

Sex Male

23.1%

4.0%

39.9%

33.0%

63.0%

43.9%

Female

18.0%

5.5%

29.9%

46.6%

47.9%

35.4%

Caregiver Status

Noncaregiver

20.4%

5.0%

33.6%

40.9%

54.1%

38.7%

Caregiver, youngest child ages 015

20.9%

4.2%

37.0%

37.9%

57.9%

41.2%

Age Cohort Millennial and Gen Z (1836)

17.3%

5.5%

32.5%

44.7%

49.8%

37.9%

Generation X (3752)

21.5%

3.9%

35.9%

38.6%

57.5%

39.9%

Pre-retirement age Baby Boomer (5364)

23.6%

5.5%

34.9%

36.0%

58.5%

40.4%

Retirement age (65+)

23.4%

2.8%

45.1%

28.7%

68.5%

47.9%

Race White non-Hispanic only

21.0%

3.9%

36.9%

38.1%

58.0%

40.9%

Black and Black multiracial

16.5%

6.1%

30.7%

46.6%

47.3%

36.9%

Other

26.7%

5.9%

33.6%

33.9%

60.2%

39.4%

Mobility Impairment Absent

20.8%

4.8%

34.7%

39.8%

55.4%

39.4%

Present

10.0%

1.5%

55.5%

33.0%

65.5%

57.0%

* Includes home-based workers and telecommute-eligible workers.

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household with a child of 515 years old.

184

Table 83. Employment flexibility among workers without a 4-year college degree by sex, caregiver status, age, race, and mobility impairment.

Flexibility Type

Totals

Schedule

Location* Schedule &

No

Flexible Schedule Flexible Location*

All workers without a 4-year college degree

Only 17.0%

Only 5.0%

Location 12.1%

Flexibility 65.9%

With or Without 29.1%

With or Without 17.1%

Sex Male Female Caregiver Status

17.5% 16.5%

4.7% 5.4%

12.0% 12.2%

65.8% 66.0%

29.5% 28.6%

16.7% 17.5%

Noncaregiver Caregiver, youngest child ages 015

16.8% 17.5%

5.5% 4.1%

12.0% 12.2%

65.8% 66.2%

28.8% 29.7%

17.5% 16.3%

Age Cohort Millennial and Gen Z (1836) Generation X (3752) Pre-retirement age Baby Boomer (5364) Retirement age (65+)

14.3% 17.8% 18.9% 27.1%

4.5% 5.4% 6.3% 1.3%

6.2% 14.9% 16.5% 24.3%

75.0% 62.0% 58.4% 47.3%

20.5% 32.6% 35.4% 51.4%

10.7% 20.2% 22.8% 25.6%

Race White non-Hispanic only Black and Black multiracial Other

20.9% 13.2% 12.6%

3.0% 8.3% 3.8%

15.1% 9.3% 8.0%

61.0% 69.2% 75.6%

35.9% 22.4% 20.6%

18.1% 17.6% 11.8%

Mobility Impairment Absent Present

17.2% 6.4%

5.0% 3.3%

11.8% 22.7%

65.9% 67.6%

29.1% 29.1%

16.9% 26.0%

* Includes home-based workers and telecommute-eligible workers. A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household with a child of 515 years old.

185

Logistic Regression Analysis of Eligibility for Flexible Work Schedule Some of the demographic differences discussed in the previous section (Descriptive Statistics on Access to Flexible Schedule and Work Location) are likely attributable to intergroup differences in education attainment and job type. However, using logistic regression analysis, the research team found that even after controlling for relevant characteristics of the jobs themselves, eligibility for a flexible work schedule is associated with characteristics unrelated to the employee's qualifications or work function (table 84).
Being female and nonwhite are both associated with a statistically significant reduction in the likelihood of having a flexible work schedule among both home-based and nonhome-based workers. Among nonhome-based workers, being a caregiver for a child is associated with an increased likelihood of having a flexible schedule, but this effect only applies to male caregivers once the children reach school age.
The likelihood of having schedule flexibility increases with age. Where a worker lives impacts the likelihood that she will have a flexible schedule, but only for nonhome-based workers. For those who work primarily from home, MPO tier does not have a significant effect.
An additional set of models incorporating interaction terms with education level was estimated. The models are not shown because they did not represent an improvement to the overall goodness of fit. However, a few key insights are worth noting. In line with patterns in the descriptive statistics in the previous section, the gender gap in schedule flexibility is greater among college-educated workers as compared to the gap between men and women without a four-year college degree. Conversely, race-based inequality in schedule flexibility is smaller among college-educated workers than for workers without a four-year degree.
186

Table 84. Logistic regressions: Eligibility for flexible work schedule among Georgia workers.

All Workers

Nonhome-based Home-based Workers Only Workers Only

Female Caregiver, youngest child 04 years old

-0.3593 *** 0.2418 **

-0.3420 *** 0.2446 **

-0.4727 ** 0.0464

Caregiver, youngest child 515 years old

0.3126 ***

0.3011 ***

0.4706

Female x caregiver, youngest 04 years old

-0.0380

-0.1008

0.5872

Female x caregiver, youngest 515 years old

-0.3925 ***

-0.4863 ***

0.3251

Education level (reference: high school or less)

Some college or associate degree

0.2901 ***

0.2274 ***

0.7466 ***

Bachelor's (4-year) degree

0.7758 ***

0.7343 ***

1.0166 ***

Postgraduate or professional degree

0.8919 ***

0.8300 ***

1.4035 ***

Race (reference: white non-Hispanic)

Black and Black multiracial

-0.5917 ***

-0.5136 ***

-1.1412 ***

Other

-0.1898 **

-0.1274

-0.7870 ***

MPO Tier (reference: Atlanta)

2. Medium MPOs

-0.3350 ***

-0.3611 ***

-0.0684

3. Small MPOs

-0.3247 ***

-0.3253 ***

-0.4075

4. Non-MPO

-0.3421 ***

-0.3653 ***

-0.1077

Age

0.0209 ***

0.0200 ***

0.0272 ***

Mobility impairment

0.0025

-0.0968

0.5338

Occupational Category (reference: sales and service)

Clerical or administrative support Blue collar

0.0275 -0.1324

0.0634 -0.1506

-0.2739 0.1720

Professional, managerial, or technical

0.3304 ***

0.3705 ***

0.0327

Full-time worker

0.0498

0.0942

-0.1484

Home-based worker

1.9913 ***

Constant

-1.6710 ***

-1.6515 ***

0.0482

Model Indicators

Number of cases, N

7960

6890

1070

Final log likelihood, LL()

-4692.4

-4239.9

-432.7

Market share log likelihood, LL(MS) McFadden's pseudo-R2: 1-LL()/LL(MS)

-5463.5 0.141

-4582.6



0.075

-494.2 0.125

Coefficients shown. *** denotes significance for =.01, ** for =.05, and * for =.10. A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+

in a household with a child of 515 years old.

Blue collar refers to manufacturing, construction, maintenance, or farming.

187

Logistic Regression Analysis of Eligibility for Flexible Work Location So far, this report has treated location flexibility as a binary variable. However, flexible work location is actually subdivided between home-based workers, who usually work from home, and telecommute-eligible workers, who work outside the home but have the option to telework sometimes instead of going to their usual work location. The remaining workers, telecommuteineligible workers, are nonhome-based workers without the opportunity to telecommute.
Table 85 shows the distribution of telecommute eligibility and home-based work by various demographic characteristics. We constructed a multinomial logistic regression to model the likelihood of being a telecommute-eligible or home-based worker, as compared to the base category of telecommute-ineligible worker (table 86).
As with schedule flexibility, being female reduces the likelihood of being telecommute-eligible and home-based. Being a caregiver influences telecommute eligibility and being a home-based worker differently. Being a caregiver of children ages 515 is associated with an increased likelihood of being telecommute-eligible, but only for men. However, for home-based work, female caregivers are more likely to be home-based; there is no effect for male caregivers.
People of color are less likely to be telecommute-eligible; race does not have a significant effect on home-based work.
Having a mobility impairment does not have a significant effect on the likelihood of being telecommute-eligible, but it does increase the likelihood of being a home-based worker. This suggests that many disabled workers are selecting home-based work rather than being offered accommodations by employers. Workers in Atlanta are more likely to have the option to
188

telecommute and to be home-based workers. While being a full-time worker did not have a significant effect on schedule flexibility, it is associated with an increased likelihood of being telecommute-eligible and a decreased likelihood of being a home-based worker.
189

Table 85. Teleworking status of Georgia workers (descriptive statistics).

Home-based

All workers

13.7%

MPO Tier

1. Atlanta MPO

16.0%

2. Medium MPOs

8.8%

3. Small MPOs

8.8%

4. Non-MPO

13.0%

Sex

Male

12.2%

Female

15.4%

Age Cohort

Millennial and Gen Z (1836)

10.3%

Generation X (3752)

14.0%

Pre-retirement age Baby Boomer (5364)

17.2%

Retirement age (65+)

22.6%

Race

White non-Hispanic only

13.7%

Black and Black multiracial

13.6%

Other

13.4%

Medical Condition

Absent

13.5%

Present

23.7%

Occupation Category

Sales or service

16.4%

Clerical or administrative support

9.8%

Blue collar

6.9%

Professional, managerial, or technical

15.4%

Education

HS or less

9.3%

Some college or associate degree

10.6%

Bachelor's degree or higher

18.2%

Work Schedule

Part-time

19.1%

Full-time

12.3%

Annual Household Income

<$35,000

11.6%

$35,000 to $49,999

10.7%

$50,000 to $74,999

12.9%

$75,000 to $99,999

9.5%

$100,000+

18.6%

Schedule Flexibility (Flextime)

Flexible

25.7%

Not flexible

5.2%

Blue collar refers to manufacturing, construction, maintenance, or farming.

Telecommute-eligible 13.5%
17.0% 10.3%
9.5% 6.9%
15.9% 10.7%
11.2% 16.1% 13.1% 12.9%
15.8% 10.8% 10.0%
13.5% 15.0%
9.2% 7.4% 6.3% 20.1%
5.3% 8.4% 21.5%
5.3% 15.5%
5.0% 4.9% 9.5% 17.9% 23.9%
28.7% 3.0%

Telecommuteineligible 72.8%
67.1% 80.8% 81.7% 80.2%
71.9% 74.0%
78.5% 69.9% 69.6% 64.5%
70.5% 75.5% 76.6%
73.1% 61.3%
74.4% 82.8% 86.8% 64.6%
85.4% 81.0% 60.2%
75.7% 72.2%
83.4% 84.4% 77.6% 72.6% 57.5%
45.6% 91.8%

190

Table 86. Multinomial logistic regression: Teleworking status of Georgia workers.

Base Category: Telecommute-ineligible workers

Telecommute-eligible Worker

Odds Ratio P-value

Female Caregiver, youngest child 04 years old

0.746 1.133

0.001 *** 0.345

Caregiver, youngest child 515 years old

1.379

0.008 ***

Female x caregiver, youngest 04

0.969

0.878

Female x caregiver, youngest 515

0.625

0.014 **

Education level (reference: high school or less)

Some college or associate degree

1.531

0.002 ***

Bachelor's (4-year) degree

3.394

0.000 ***

Postgraduate or professional degree

3.437

0.000 ***

Race (reference: white non-Hispanic)

Black and Black multiracial

0.690

0.000 ***

Other

0.656

0.002 ***

MPO Tier (reference: Atlanta)

2. Medium MPOs

0.553

0.000 ***

3. Small MPOs

0.469

0.000 ***

4. Non-MPO

0.492

0.000 ***

Age

1.009

0.002 ***

Mobility impairment

1.478

0.175

Occupational Category (reference: sales and service)

Clerical or administrative support Blue collar
Professional, managerial, or technical

0.731 0.630 1.181

0.053 * 0.002 *** 0.088 *

Full-time worker Constant

1.797 0.058

0.000 *** 0.000 ***

Home-based Worker

Odds Ratio P-Value

0.768

0.002 ***

0.754 0.883 2.302 1.527

0.102 0.397 0.000 *** 0.030 **

1.408 2.780 2.506

0.003 *** 0.000 *** 0.000 ***

0.876 0.810

0.136 0.120

0.554 0.503 0.686 1.027 1.681

0.000 *** 0.000 *** 0.004 *** 0.000 *** 0.023 **

0.461 0.502 0.656
0.492 0.112

0.000 *** 0.000 *** 0.000 ***
0.000 *** 0.000 ***

Model Indicators

Number of cases, N

7,972

Final log likelihood, LL()

-5617.21

Market share log likelihood, LL(MS) McFadden's pseudo-R2: 1-LL()/LL(MS)

-6140.95 0.085

Base category: Telecommute-ineligible workers (those who work outside the home without the option of teleworking).

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household

with a child of 515 years old.

Telecommute-eligible workers usually work outside of the home but have the option of telecommuting. Home-based workers usually

work from home.

Blue collar refers to manufacturing, construction, maintenance, or farming.

191

THE EFFECTS OF FLEXIBLE SCHEDULING Having examined which workers are allowed a degree of flexibility with regard to their work starting time and location in the previous section, this section discusses the effects of that flexibility on workers' behavior. Figure 20 shows weighted histograms of the time of arrival at and departure from work for workers' travel-day commutes. Workers with flexible schedules tend to both arrive at and depart from work later than those with inflexible schedules.
Figure 20. Bar graphs. Commute schedules of workers with and without flexible working times. 192

The difference is most pronounced with respect to arrival times at work. The median arrival time for a worker with a flexible schedule is 8:34 a.m., 34 minutes later than the median arrival time for workers with inflexible schedules (table 87). For departure from work, the difference is just 15 minutes (5:00 p.m. vs. 4:45 p.m.). Figure 20 also shows a small secondary peak in the departure times of workers with flexible schedules during the afternoon, though it is unknown if these early departures are followed by teleworking after the worker arrives at home.
Flexible work scheduling is also associated with less dispersion of arrival and departure times. The interquartile range of work arrival times for workers with inflexible schedules is 200 minutes (7:0010:20 a.m.) (not included in table). For workers with flexible schedules, it is just 125 minutes (7:409:45 a.m.), a time period that is more than an hour shorter. For departures, the difference is again somewhat smaller. Workers with inflexible schedules have an interquartile range of 180 minutes (3:006:00 p.m.), versus 150 minutes for workers with flexible schedules (3:306:00 p.m.).
Table 87 also shows information about arrival and departure times disaggregated by MPO tier, occupational category, educational attainment, and worker type. Flexible scheduling is associated with wider differences in arrival times in small MPO areas and non-MPO counties. However, for departure times, the differences are larger in Atlanta and medium MPOs. Contrary to the general trend, part-time workers with flexible schedules tend to arrive at and depart from work earlier than those with inflexible schedules.
193

Table 87. Median work arrival and departure times by schedule flexibility, MPO tier, education, and employment characteristics.

Median Work Arrival Time (am)

Median Work Departure Time (pm)

Inflexible Flexible

Difference Inflexible Flexible

Difference

Schedule Schedule (minutes) Schedule Schedule (minutes)

All workers

8:00

8:34

34

4:45

5:00

15

MPO Tier

1. Atlanta MPO

8:05

8:38

33

4:45

5:00

15

2. Medium MPOs

7:55

8:20

25

4:30

4:50

20

3. Small MPOs

8:00

9:00

60

4:55

4:45

-10

4. Non-MPO

7:40

8:28

48

5:00

5:00

0

Occupational Category

Sales or service

9:06

9:10

4

5:00

5:00

0

Clerical or administrative support

7:55

8:30

35

4:41

4:30

-11

Blue collar*

7:01

8:00

59

4:05

5:00

55

Professional, managerial, or technical

7:54

8:30

36

4:55

5:00

5

Educational Attainment

High school or less

8:00

8:26

26

4:30

5:00

30

Some college or associate degree

8:00

8:37

37

4:45

5:00

15

Bachelor's (4-year) degree

7:57

8:40

43

5:00

5:00

0

Postgraduate or professional degree

7:55

8:40

45

5:00

5:00

0

Worker Type

Part-time

10:03

9:35

-28

4:30

3:50

-40

Full-time

7:50

8:30

40

4:50

5:00

10

* Blue collar refers to manufacturing, construction, maintenance, or farming.

The difference in median arrival times between workers with and without flexible scheduling is largest among blue-collar workers (59 minutes) and smallest among sales and service workers (4 minutes). The difference in median departure times between workers with and without flexible schedules is also largest among blue-collar workers (55 minutes). Clerical and administrative workers with flexible schedules are the only occupation category to leave work earlier than their counterparts with nonflexible schedules (a difference of 11 minutes).
However, as visualized in figure 21 and figure 22, while flexible scheduling is not associated with substantial changes in median arrival and departure times for sales and service jobs, it is associated with a dramatic reduction in the interquartile range. For example, the interquartile
194

range of arrival times for service jobs with inflexible schedules stretches across 6 hours (7:30 a.m.1:25 p.m.), more than double the interquartile range for service jobs with flexible schedules (8:1511:00 a.m.). However, the NHTS occupation categories are broad, and one possible interpretation of this finding is that, since jobs that allow for flexible schedules tend to be higher status, the difference in schedules may more accurately reflect a difference between higher-status service jobs (e.g., call center manager) versus lower-status jobs (e.g., retail employee).
Figure 21. Histograms. Arrival times at work for service and nonservice workers by schedule flexibility. 195

Figure 22. Histograms. Departure times from work for service and nonservice workers by schedule flexibility.
Table 88 shows median work arrival and departure times by schedule flexibility, disaggregated by demographic characteristics. Schedule flexibility is associated with larger differences between median arrival times and between median departure times for women and older workers. The differences between the median arrival times of flexible and inflexible workers are similar for caregivers and childless adults. However, caregivers show a larger difference in departures at the end of the work day. Flex time is associated with a larger difference in schedule among caregivers for older children (ages 515) than among those caring for younger children.
196

Figure 23 and figure 24 visually depict the variability of schedules among caregivers for young children by gender.

Table 88. Median work arrival and departure times by schedule flexibility, demographic characteristics, and caregiver status.

Median Work Arrival Time (am)

Median Work Departure Time (pm)

Inflexible Flexible

Difference Inflexible Flexible

Difference

Schedule Schedule (minutes) Schedule Schedule (minutes)

All workers

8:00

8:34

34

4:45

5:00

15

Sex

Male

7:55

8:20

25

5:00

5:00

0

Female

8:00

8:55

55

4:35

5:00

25

Caregiver Status

Noncaregiver

8:00

8:40

40

5:00

5:00

0

Caregiver, youngest child ages 015

7:55

8:30

35

4:30

5:00

30

Age Cohort

Millennial and Gen Z (1836)

8:20

8:50

30

5:00

5:00

0

Generation X (3752)

7:45

8:30

45

4:30

5:00

30

Pre-retirement age Baby Boomer (5364) 7:50

8:30

40

4:30

5:00

30

Retirement age (65+)

8:00

9:00

60

5:00

4:15

-45

Annual Household Income

<$35,000

8:00

9:00

60

4:30

5:00

30

$35,000 to $49,999

8:00

8:30

30

4:45

4:30

-15

$50,000 to $74,999

8:00

8:30

30

4:45

4:30

-15

$75,000 to $99,999

8:00

8:45

45

5:00

5:00

0

$100,000+

7:53

8:30

37

5:00

5:00

0

Race

White non-Hispanic only

7:55

8:30

35

5:00

4:55

-5

Black and Black multiracial

8:00

8:30

30

4:30

5:00

30

Other

8:15

9:00

45

4:30

5:30

60

Mobility Impairment

Absent

8:00

8:35

35

4:45

5:00

15

Present

10:45

8:10

-155

3:30

3:10

-20

Caregiver Status by Gender

Male noncaregiver

8:00

8:25

25

5:00

5:00

0

Male caregiver

7:46

8:17

31

4:40

5:00

20

Female noncaregiver

8:03

8:55

52

5:00

5:00

0

Female caregiver

7:58

8:53

55

4:25

4:45

20

Caregiver Status by Age of Youngest Child

Youngest child 04 years

8:00

8:30

30

4:50

5:00

10

Youngest child 515 years

7:45

8:30

45

4:25

5:00

35

197

Figure 23. Histograms. Arrival times at work for caregivers by schedule flexibility and gender.
198

Figure 24. Histograms. Departure times from work for caregivers by schedule flexibility and gender.
199

Models of Work Arrival and Departure Times Linear regression was used to isolate the relationship between flexible scheduling and work arrival and departure times (table 89). All else held equal, a worker with a flexible schedule will arrive at work an average of 22 minutes later than a comparable worker with an inflexible schedule, and they will depart from work an average of 6 minutes later. Unsurprisingly, occupation category and full-time status exert a strong influence on work arrival and departure times. Once other factors are accounted for, MPO tier is not associated with significant differences in either departure or arrival time. As measured by R2, the goodness of fit is not high for either model (especially the departure time model), signifying that arrival and departure times are largely determined by factors unavailable in the data. The researchers also modeled commute duration as a function of schedule flexibility and found no practically significant effect.64
64 When the model was estimated, the flexibility coefficient was small in magnitude, statistically significant, but with a counterintuitive positive sign (indicating that, after controlling for commute distance and other factors, those with schedule flexibility had commutes that were 0.96 minutes longer on average). This result likely reflects the opposite direction of causality (if the commute is longer duration--which, after controlling for distance and mode, means that it is more congested--the respondent is more likely to seek schedule flexibility). In consideration of all these factors the research team concludes that flexibility is not a good predictor of commute duration, perhaps because of counteracting effects reflecting both directions of causality.
200

Table 89. Linear regression: Work arrival and departure times for travel day commutes.

Arrival Time at Work

Coefficient P-value

Flexible schedule

22.017

0.000 ***

Log commute distance (miles)

-18.129

0.000 ***

Work Journey Mode (reference: private auto)

Nonmotorized (pedestrian or cyclist)

-23.472

0.207

Public transit or other bus/rail

-10.483

0.607

Multimodal and other

52.761

0.028 **

Full-time worker

-81.372

0.000 ***

Occupational Category (reference: sales and service)

Clerical or administrative support Blue collar

-49.854 -49.265

0.000 *** 0.000 ***

Professional, managerial, or technical

-39.647

0.000 ***

Education level (reference: high school or less)

Some college or associate degree

7.599

0.364

Bachelor's (4-year) degree

2.334

0.787

Postgraduate or professional degree

4.074

0.653

MPO Tier (reference: Atlanta)

2. Medium MPOs

-7.804

0.197

3. Small MPOs

-2.388

0.739

4. Non-MPO

-9.447

0.362

Race (reference: white non-Hispanic)

Black and Black multiracial

21.122

0.004 ***

Other

8.301

0.346

Age Age2

-5.692 0.053

0.000 *** 0.001 ***

Female

5.017

0.361

Caregiver, youngest child 04 years old

-8.206

0.328

Caregiver, youngest child 515 years old

-2.367

0.749

Day of week (reference: Sunday)

Monday

-104.764

0.000 ***

Tuesday

-107.431

0.000 ***

W ednesday

-109.528

0.000 ***

Thursday

-115.699

0.000 ***

Friday

-105.665

0.000 ***

Saturday

-70.653

0.023 **

Constant

912.744

0.000 ***

Departure Time from Work

Coefficient P-value

5.915

0.000 ***

8.429

0.000 ***

4.270 2.922 -12.146 27.561

0.207 0.607 0.028 ** 0.000 ***

-11.491 -49.278 -18.382

0.000 *** 0.000 *** 0.000 ***

2.835 13.220 23.422

0.364 0.787 0.653

-11.103 -8.073
-16.550

0.197 0.739 0.362

-20.785 17.582
2.162
-0.030 -4.937 -34.201 -23.706

0.004 *** 0.346 0.000 ***
0.001 *** 0.361 0.328 0.749

-5.575 -18.079 -18.085
-6.525 -32.719
-4.919 947.020

0.000 *** 0.000 *** 0.000 *** 0.000 *** 0.000 *** 0.023 ** 0.000 ***

R2

0.089

Number of cases, N

5,061

Departure and arrival times are given in minutes past midnight. Coefficients are in minutes.

Blue collar refers to manufacturing, construction, maintenance, or farming.

0.024 5,091

201

FREQUENCY OF TELEWORKING How often do workers who have the option of working from home or a third place do so? This report approaches this question using two types of data. First, it examines the frequency of telecommuting among telecommute-eligible workers. Second, it looks at travel-day teleworking among Georgia workers, regardless of their stated eligibility for telework.
Telecommuting Frequency among Eligible Workers When workers have the ability to telecommute, the overwhelming majority choose to do so; 78 percent of telecommute-eligible workers reported telecommuting at least once in the past 30 days (table 90). Telecommuting is least common in small MPO areas, where nevertheless 62 percent of eligible workers telecommuted.
Workers in rural non-MPO counties are no more likely than average to telecommute. However, those who do telecommute do so for a higher number of days; the average telecommuter from a non-MPO area telecommutes 9.3 days per month, compared to the state average of 6.7 days. Preretirement age Baby Boomers and Generation X workers are the most likely to telework; the Baby Boomers do so for more days on average.
While white workers are more likely to telecommute than Black workers, among those who do telecommute, Black workers do so for more days on average.
Workers with disabilities are slightly less likely than average to take advantage of the ability to telecommute, but among those who did so, the mean days telecommuted for disabled workers (11.7) is the highest of any subgroup studied.
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For a finer-grained breakdown, table 91 presents the distribution across telecommuting frequency categories for the same variables as in table 90. There are not strong differences in the percentage of workers in different occupation categories who telecommute at least once (table 92, table 93), but among those who telecommute at least once, blue-collar workers telecommute the most days.65 Part-time workers and low-income workers are less likely to telecommute but do so for more days.
65 One possible explanation for this phenomenon is that there is a larger difference in job function between telecommute-eligible and -ineligible blue-collar jobs than in other industries. Some portion of this finding may also reflect a misclassification as telecommuters of self-employed workers such as movers, plumbers, and other professions that are based primarily on house calls.
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Table 90. Average number of days telecommuted in the past 30 days among eligible workers by MPO tier, sex, caregiver status, age, race, and medical condition.

Percent of Eligible Workers who Telecommuted

Mean Days, All Eligible Workers

Mean Days, Workers who Telecommuted

All workers

77.5%

5.2

6.7

MPO Tier 1. Atlanta MPO

79.9%

5.0

6.2

2. Medium MPOs

72.8%

5.7

7.8

3. Small MPOs

62.3%

4.3

7.0

4. Non-MPO

76.1%

7.1

9.3

Sex Male

76.7%

5.3

6.9

Female Caregiver Status

79.0%

5.1

6.4

Noncaregiver

75.0%

4.6

6.1

Caregiver, youngest child ages 015

81.1%

6.1

7.5

Age Cohort Millennial and Gen Z (1836)

73.5%

4.7

6.5

Generation X (3752)

79.7%

4.9

6.1

Pre-retirement age Baby Boomer (5364)

80.8%

6.7

8.3

Retirement age (65+)

70.6%

5.1

7.2

Race White non-Hispanic only

80.1%

5.1

6.3

Black and Black multiracial

71.0%

5.8

8.1

Other

76.6%

4.6

6.1

Medical Condition Absent

77.5%

5.1

6.6

Present

74.0%

8.6

11.7

Based on telecommute-eligible workers. Excludes home-based workers. A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household with a child of 515 years old.

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Table 91. Telecommuting 30-day frequency among eligible workers by MPO tier, sex, caregiver status, age, race, and medical condition.

Did Not Telecommute
(0 Days)

Occasional (14 Days)

Moderate (510 Days)

Frequent (11+ Days)

All workers

22.5%

37.7%

27.9%

11.9%

MPO Tier 1. Atlanta MPO

20.1%

39.6%

31.1%

9.3%

2. Medium MPOs

27.2%

33.1%

21.2%

18.5%

3. Small MPOs

37.7%

34.4%

17.3%

10.5%

4. Non-MPO

23.9%

31.0%

19.0%

26.1%

Sex Male

23.3%

38.4%

25.9%

12.4%

Female Caregiver Status

21.0%

36.5%

31.4%

11.1%

Noncaregiver

25.0%

37.8%

27.9%

9.3%

Caregiver, youngest child ages 015

18.9%

37.5%

28.0%

15.7%

Age Cohort Millennial and Gen Z (1836)

26.5%

36.7%

26.7%

10.1%

Generation X (3752)

20.3%

39.7%

30.8%

9.2%

Pre-retirement age Baby Boomer (5364)

19.2%

37.3%

22.5%

21.0%

Retirement age (65+)

29.4%

27.6%

31.4%

11.5%

Race White non-Hispanic only

19.9%

39.8%

29.3%

11.0%

Black and Black multiracial

29.0%

30.9%

25.4%

14.7%

Other

23.4%

40.7%

24.9%

11.0%

Medical Condition Absent

22.5%

38.3%

27.6%

11.6%

Present

26.0%

8.8%

49.2%

16.0%

Based on telecommute-eligible workers. Excludes home-based workers. A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a
household with a child of 515 years old.

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Table 92. Average days telecommuted in the past 30 days among eligible workers by employment characteristics.

Percent of Eligible Workers who Telecommuted

Mean Days, All Eligible Workers

All workers

77.5%

5.2

Occupation Category

Sales or service

75.6%

7.4

Clerical or administrative support

74.6%

3.9

Blue collar

72.5%

8.0

Professional, managerial, or technical

78.8%

4.4

Education

HS or less

68.9%

7.3

Some college or associate degree

72.2%

6.3

Bachelor's degree or higher

80.1%

4.6

Work Schedule

Part-time

66.8%

6.9

Full-time

78.4%

5.1

Annual Household Income

<$35,000

64.3%

6.7

$35,000 to $49,999

69.4%

5.2

$50,000 to $74,999

77.0%

5.5

$75,000 to $99,999

78.3%

4.6

$100,000+

79.6%

4.7

Schedule Flexibility (Flextime)

Not flexible

78.1%

5.2

Flexible

73.9%

4.9

Based on telecommute-eligible workers. Excludes home-based workers. Blue collar refers to manufacturing, construction, maintenance, or farming.

Mean Days, Workers who Telecommuted
6.7
9.8 5.2 11.1 5.5
10.7 8.7 5.7
10.3 6.4
10.4 7.4 7.2 5.9 5.9
6.6 6.7

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Table 93. Telecommuting 30-day frequency among eligible workers by employment characteristics.

Did Not Telecommute
(0 Days)

Occasional (14 Days)

All workers

22.5%

37.7%

Occupation Category

Sales or service

24.4%

28.2%

Clerical or administrative support Blue collar

25.4% 27.5%

41.2% 30.4%

Professional, managerial, or technical

21.2%

40.8%

Education

HS or less

31.1%

26.1%

Some college or associate degree

27.8%

29.6%

Bachelor's degree or higher

19.9%

41.5%

Work Schedule

Part-time

33.2%

19.1%

Full-time

21.6%

39.2%

Annual Household Income

<$35,000

35.7%

22.5%

$35,000 to $49,999

30.6%

23.8%

$50,000 to $74,999

23.0%

30.4%

$75,000 to $99,999

21.7%

45.4%

$100,000+

20.4%

41.5%

Schedule Flexibility (Flextime)

Not flexible

21.9%

38.3%

Flexible

26.1%

33.9%

Based on telecommute-eligible workers. Excludes home-based workers. Blue collar refers to manufacturing, construction, maintenance, or farming.

Moderate (510 Days)
27.9%
23.1% 25.6% 14.9% 31.0%
18.1% 25.5% 29.9%
22.5% 28.4%
21.8% 35.9% 31.2% 22.5% 29.4%
27.7% 29.3%

Frequent (11+ Days)
11.9%
24.4% 7.7%
27.2% 7.0%
24.8% 17.1%
8.7%
25.3% 10.8%
19.9% 9.7%
15.4% 10.5%
8.7%
12.1% 10.7%

The ability to telecommute can be of particular benefit to caregivers. As shown in table 94 and table 95, when caregivers are eligible for telecommuting, they are more likely than noncaregivers to take advantage of the option. The difference between caregivers and noncaregivers is greater for women than for men. Caregivers for young children (ages 04) are more likely to use the option to telecommute than caregivers for older children.

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Table 94. Average days telecommuted in the past 30 days among eligible workers by caregiver status.

Percent of Eligible Workers who Telecommuted

Mean Days, All Eligible Workers

Mean Days, Workers who Telecommuted

All workers Caregiver Status

77.5%

5.2

6.7

Noncaregiver

75.0%

4.6

6.1

Caregiver, youngest child ages 015 years

81.1%

6.1

7.5

Caregiver Status by Sex

Male noncaregiver

74.3%

4.5

6.0

Female noncaregiver

79.7%

6.2

7.8

Male caregiver

76.1%

4.7

6.2

Female caregiver

84.5%

5.9

6.9

By Age of Youngest Child

Youngest ages 04 years

84.2%

6.4

7.6

Youngest ages 515 years

78.4%

5.9

7.5

Household Type

Male co-caregiver

79.8%

6.3

7.9

Female co-caregiver

86.2%

5.9

6.8

Male single caregiver

69.5%

2.4

3.5

Female single caregiver

77.3%

5.8

7.5

Based on telecommute-eligible workers. Excludes home-based workers. A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household with a child of 515 years old.

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Table 95. Telecommuting 30-day frequency among eligible workers by caregiver status.

Did Not Telecommute
(0 Days)

Occasional (14 Days)

Moderate (510 Days)

Frequent (11+ Days)

All workers Caregiver Status

22.5%

37.7%

27.9%

11.9%

Noncaregiver

25.0%

37.8%

27.9%

9.3%

Caregiver, youngest child ages 015 years

18.9%

37.5%

28.0%

15.7%

Caregiver Status by Sex

Male noncaregiver

25.7%

38.2%

26.8%

9.3%

Female noncaregiver

20.3%

38.6%

24.9%

16.2%

Male caregiver

23.9%

37.3%

29.4%

9.3%

Female caregiver

15.5%

34.8%

35.3%

14.5%

By Age of Youngest Child

Youngest ages 04 years

15.8%

42.1%

26.8%

15.2%

Youngest ages 515 years

21.6%

33.4%

29.0%

16.0%

Household Type

Male co-caregiver

20.2%

38.2%

25.2%

16.5%

Female co-caregiver

13.8%

36.9%

36.0%

13.3%

Male single caregiver

30.5%

63.2%

6.3%

0.0%

Female single caregiver

22.7%

25.8%

32.3%

19.2%

Based on telecommute-eligible workers. Excludes home-based workers.

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a

household with a child of 515 years old.

Telecommuting Frequency and Distance to Work To analyze the relationship between distance to work and telecommuting frequency, the researchers began by examining key percentiles for distance to work by MPO tier (table 96). Then, after trying several alternate specifications, they divided respondents into five categories based on round-number distances nearest to these key percentiles.

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Table 96. Distance to work in miles by MPO tier.

25th Percentile

All Workers

5.1

MPO Tier

1. Atlanta MPO

5.9

2. Medium MPOs

4.3

3. Small MPOs

3.9

4. Non-MPO

4.5

Excluding Atlanta (Tiers 24)

4.3

50th Percentile
11.4
12.8 9.3 7.9
12.9 9.5

75th Percentile
21.1
22.3 15.7 13.9 25.2 18.7

95th Percentile
46.3
42.0 46.7 48.0 75.2 60.7

Unsurprisingly, workers who live the farthest away are the most likely to telecommute and do so for more days (table 97 and table 98). Workers who live more than 45 miles from their workplace telecommute an average of 10.3 days, versus the state average of 6.7 days.
Somewhat more surprisingly, the group who telecommutes the second-highest number of days is workers who live the closest to work (5 miles or less). This pattern holds for counties in MPOs of all sizes. The sole exception to this pattern is non-MPO counties, where in general workers who live more than 10 miles from work telecommute more often than workers who live 10 miles or fewer from their workplace.

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Table 97. Average days telecommuted in the past 30 days among eligible workers by distance to work and MPO tier.

Percent of Eligible Workers who Telecommuted

All Workers

77.5%

By Distance to Work 05 miles 5.110 mi 10.120 mi 20.145 mi >45 mi

72.2% 70.5% 82.2% 80.0% 86.9%

Tier 1: Atlanta MPO Only 05 miles 5.110 mi 10.120 mi 20.145 mi >45 mi

74.7% 72.2% 83.2% 84.0% 79.5%

Tier 2: Medium MPOs Only 05 miles 5.110 mi 10.120 mi 20.145 mi >45 mi

58.2% 80.7% 78.5% 77.1% 91.6%

Tier 3: Small MPOs Only 05 miles 5.110 mi 10.120 mi 20.145 mi >45 mi

72.1% 52.2% 75.6% 32.3% 85.2%

Tier 4: Non-MPO Counties 05 miles 5.110 mi 10.120 mi 20.145 mi >45 mi

84.1% 68.0% 71.9% 66.9% 98.3%

All Workers Excluding Atlanta (Tiers 24) 05 miles 5.110 mi 10.120 mi 20.145 mi >45 mi

68.1% 67.5% 76.6% 62.3% 93.2%

Mean Days, All Eligible Workers
5.2
5.8 3.9 4.4 5.6 8.9
6.0 4.0 4.3 5.3 7.5
5.0 5.2 5.3 6.1 9.7
6.0 3.1 3.1 1.9 10.0
5.7 2.6 7.3 9.3 10.8
5.4 3.8 5.1 7.2 10.1

Mean Days, Workers who Telecommuted
6.7
8.0 5.6 5.3 7.0 10.3
8.1 5.5 5.1 6.3 9.5
8.6 6.4 6.7 7.9 10.6
8.3 6.0 4.2 6.0 11.7
6.8 3.9 10.1 13.9 11.0
8.0 5.6 6.7 11.5 10.9

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Table 98. Telecommuting 30-day frequency among eligible workers by distance to work and MPO tier.

Did Not Telecommute
(0 Days)

All workers

22.5%

By Distance to Work

05 miles

27.8%

5.110 mi

29.5%

10.120 mi

17.8%

20.145 mi

20.0%

>45 mi

13.1%

Tier 1: Atlanta MPO Only

05 miles

25.3%

5.110 mi

27.8%

10.120 mi

16.8%

20.145 mi

16.0%

>45 mi

20.5%

Tier 2: Medium MPOs Only

05 miles

41.8%

5.110 mi

19.3%

10.120 mi

21.5%

20.145 mi

22.9%

>45 mi

8.4%

Tier 3: Small MPOs Only

05 miles

27.9%

5.110 mi

47.8%

10.120 mi

24.4%

20.145 mi

67.7%

>45 mi

14.8%

Tier 4: Non-MPO Counties

05 miles

15.9%

5.110 mi

32.0%

10.120 mi

28.1%

20.145 mi

33.1%

>45 mi

1.7%

All Workers Excluding Atlanta (Tiers 24)

05 miles

31.9%

5.110 mi

32.5%

10.120 mi

23.4%

20.145 mi

37.7%

>45 mi

6.8%

Occasional (14 Days)
37.7%
31.7% 32.0% 48.4% 38.1% 22.7%
29.5% 31.6% 49.5% 41.3% 23.4%
20.3% 40.0% 45.7% 40.9% 31.5%
46.0% 26.5% 51.7%
7.0% 18.3%
54.6% 30.1% 20.0% 23.2% 12.1%
35.4% 32.7% 42.6% 23.8% 22.2%

Moderate (510 Days)
27.9%
24.8% 32.4% 27.1% 28.8% 28.6%
29.8% 35.3% 28.8% 32.7% 28.3%
23.1% 28.1% 17.2% 19.2% 11.9%
8.6% 18.9% 18.5% 23.2% 42.9%
10.8% 37.9% 18.4%
4.2% 44.6%
16.3% 27.4% 17.7% 11.2% 28.9%

Frequent (11+ Days)
11.9%
15.7% 6.1% 6.7%
13.2% 35.5%
15.3% 5.4% 4.9%
10.0% 27.8%
14.8% 12.7% 15.5% 17.0% 48.2%
17.5% 6.9% 5.3% 2.1%
24.0%
18.7% 0.0%
33.5% 39.4% 41.7%
16.4% 7.4%
16.3% 27.3% 42.1%

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Travel Day Telecommuting In addition to looking at behavior over the course of a month, it is useful to examine telecommuting on the travel day itself. This analysis takes as its base all workers ages 18+, regardless of whether they report being eligible for telework. It includes telework-ineligible workers because sometimes employees who are usually ineligible for teleworking are granted exceptions due to illness, inclement weather, natural disasters, or, more recently, pandemics. On an average day, 8.8 percent of people who work will exclusively telework, and a further 3.7 percent will work both from home and outside the home (mixed telework) (table 99). In other words, 12.5 percent of workers will telework for at least part of their work day.66 Teleworking is most common in the Atlanta MPO, and second-most common in non-MPO counties. Women are more likely to exclusively telework than men, though a higher percentage of men engage in mixed telework. One in five people with mobility impairments who are working on any given day will do so exclusively from home, which is more than double the statewide average.
66 As discussed in Overview in this chapter, the true total is likely higher because this figure only includes the primary activity at each location.
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Table 99. Travel day commuting and telecommuting among workers ages 18+ by MPO tier, sex, caregiver status, age, race, and medical condition.

All workers

Exclusive Telework (Worked from Home Only)
8.8%

Mixed Telework
(Worked from Home & Outside
of Home)
3.7%

Conventional Commute
(Worked Outside of Home Only)
87.5%

Total Teleworking (Exclusive and
Mixed)
12.5%

MPO Tier

1. Atlanta MPO

11.3%

3.9%

84.9%

15.1%

2. Medium MPOs

5.1%

3.1%

91.8%

8.2%

3. Small MPOs

3.0%

3.1%

93.9%

6.1%

4. Non-MPO

6.3%

3.7%

90.0%

10.0%

Sex

Male

7.9%

4.1%

88.0%

12.0%

Female
Caregiver Status Noncaregiver Caregiver, youngest child ages 015 years

9.9%
8.3% 9.6%

3.1%
3.3% 4.4%

87.0%
88.5% 85.9%

13.0%
11.5% 14.1%

Age Cohort

Millennial and Gen Z (1836) Generation X (3752) Pre-retirement age Baby Boomer (5364)
Retirement age (65+)

7.4% 8.5% 10.2%
16.8%

3.6% 3.4% 4.1%
5.2%

89.0% 88.1% 85.6%
78.1%

11.0% 11.9% 14.4%
21.9%

Race

White non-Hispanic only

10.2%

3.7%

86.1%

13.9%

Black and Black multiracial

7.3%

3.1%

89.5%

10.5%

Other

6.5%

4.9%

88.6%

11.4%

Medical Condition

Absent

8.6%

3.6%

87.7%

12.3%

Present

20.4%

6.1%

73.6%

26.4%

Based on workers who reported working outside the home or working from home for pay on the travel day. A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household with a child of 515 years old.

Workers with a college degree and from high-income households are more likely to telework on the travel day (table 100). A strong relationship also exists with time flexibility: of workers who

214

can set their own schedule, one in four work from home for at least part of the day, versus just 4 percent of workers with inflexible schedules. Caregivers, especially female caregivers, are more likely than noncaregivers to work from home (table 101). The group most likely to telework is female co-caregivers (those living in a household with multiple caregivers). Single caregivers are less likely to work from home than noncaregivers, likely reflecting not a difference in preferences, but a difference in access to teleworking. In particular, single caregivers are often in lower education or lower income groups that have less access to teleworking. The relationship between distance to work and travel-day teleworking appears to be different when measuring monthly frequency versus daily behavior. In the monthly data, those with the smallest and largest distances to work were more likely to telecommute (table 97). When looking at travel-day telecommuting (table 102), aside from Atlanta, any elevation in telecommuting among workers who live 5 miles or less from their workplace is decidedly less pronounced.
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Table 100. Travel day commuting and telecommuting among workers ages 18+ by employment characteristics.

Exclusive Telework
(Worked from Home Only)

Mixed Telework
(Worked from Home & Outside of
Home)

Conventional Commute
(Worked Outside of Home Only)

All workers

8.8%

3.7%

87.5%

Occupation Category

Sales or service

7.0%

3.9%

89.1%

Clerical or administrative support

5.3%

1.8%

92.8%

Blue collar Professional, managerial, or technical

2.4% 13.0%

3.0% 4.2%

94.6% 82.8%

Education

HS or less

2.8%

2.7%

94.5%

Some college or associate

6.9%

2.4%

90.6%

Bachelor's or higher

13.5%

5.1%

81.4%

Work Schedule

Part-time

10.0%

4.1%

85.9%

Full-time

8.5%

3.6%

87.9%

Annual Household Income

<$35,000

5.3%

3.5%

91.2%

$35,000 to $49,999

5.2%

3.0%

91.8%

$50,000 to $74,999

7.5%

2.6%

89.9%

$75,000 to $99,999

7.1%

4.1%

88.9%

$100,000+

14.7%

4.6%

80.6%

Schedule Flexibility (Flextime)

Not flexible

2.0%

2.4%

95.7%

Flexible

19.0%

5.7%

75.4%

Based on workers who reported working outside the home or working from home for pay on the travel day.

Total Teleworking (Exclusive and
Mixed)
12.5%
10.9% 7.2% 5.4%
17.2%
5.5% 9.4% 18.6%
14.1% 12.1%
8.8% 8.2% 10.1% 11.1% 19.4%
4.3% 24.6%

216

Table 101. Travel day commuting and telecommuting among workers ages 18+ by caregiver status

All workers Caregiver Status Noncaregiver Caregiver, youngest child ages 015 Caregiver Status by Sex Male noncaregiver Female noncaregiver Male caregiver Female caregiver By Age of Youngest Child Youngest ages 04 Youngest ages 515 Household Type Male co-caregiver Female co-caregiver Male single caregiver Female single caregiver

Exclusive Telework (Worked from Home Only)
8.8%

Mixed Telework
(Worked from Home & Outside of
Home)
3.7%

Conventional Commute
(Worked Outside of Home Only)
87.5%

8.3% 9.6%

3.3% 4.4%

88.5% 85.9%

7.6% 8.5% 9.2% 11.1%
9.3% 9.9%
8.5% 12.0%
5.8% 7.9%

3.5% 5.3% 3.0% 3.3%
5.6% 3.4%
5.4% 3.8% 0.0% 1.9%

88.9% 86.2% 87.8% 85.6%
85.1% 86.6%
86.1% 84.3% 94.2% 90.2%

Total Teleworking (Exclusive and
Mixedl)
12.5%
11.5% 14.1%
11.1% 13.8% 12.2% 14.4%
14.9% 13.4%
13.9% 15.7%
5.8% 9.8%

Based on workers who reported working outside the home or working from home for pay on the travel day. A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household with a child of 515 years old.

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Table 102. Travel day commuting and telecommuting among workers ages 18+ by distance to work and MPO tier.

Exclusive

Conventional Teleworking on

Telework

Mixed Telework

Commute

Travel Day

(Worked from Home Only)

(Worked from (Worked Outside of

Home & Outside of

Home Only)

Home)

(Exclusive and Mixed)

All workers

By Distance to Work 05 miles 5.110 mi 10.120 mi 20.145 mi >45 mi

1.8% 1.6% 1.8% 2.3% 2.8%

2.4% 1.9% 1.9% 4.5% 3.9%

95.9% 96.6% 96.3% 93.2% 93.3%

4.1% 3.4% 3.7% 6.8% 6.7%

Tier 1: Atlanta MPO Only 05 miles 5.110 mi 10.120 mi 20.145 mi >45 mi

2.2% 1.7% 2.6% 3.1% 3.6%

2.4% 2.0% 1.6% 3.9% 6.8%

95.4% 96.3% 95.9% 93.0% 89.7%

4.6% 3.7% 4.1% 7.0% 10.3%

Tier 2: Medium MPOs Only 05 miles 5.110 mi 10.120 mi 20.145 mi >45 mi

0.3% 0.5% 0.3% 2.1% 8.7%

0.9% 1.9% 2.6% 4.7% 3.1%

98.8% 97.6% 97.1% 93.2% 88.1%

1.2% 2.4% 2.9% 6.8% 11.9%

Tier 3: Small MPOs Only 05 miles 5.110 mi 10.120 mi 20.145 mi >45 mi

0.7% 1.1% 0.9% 1.1% 1.9%

4.3% 3.4% 0.9% 0.8% 2.5%

95.0% 95.6% 98.2% 98.1% 95.6%

5.0% 4.4% 1.8% 1.9% 4.4%

Tier 4: Non-MPO Counties 05 miles 5.110 mi 10.120 mi 20.145 mi >45 mi

2.5% 3.0% 0.0% 0.0% 0.0%

2.1% 0.0% 3.2% 7.5% 2.1%

95.4% 97.0% 96.8% 92.5% 97.9%

4.6% 3.0% 3.2% 7.5% 2.1%

Atlanta (Tiers 2-4) 05 miles 5.110 mi 10.120 mi 20.145 mi >45 mi

1.3% 1.4% 0.3% 0.7% 2.4%

2.3% 1.7% 2.5% 5.7% 2.4%

96.4% 96.9% 97.2% 93.6% 95.2%

3.6% 3.1% 2.8% 6.4% 4.8%

Based on workers who reported working outside the home or working from home for pay on the travel day.

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CHAPTER 4. NEW TECHNOLOGIES AND SERVICES
CHAPTER 4 SUMMARY
This chapter examines three emerging trends shaping Georgians' mobility in the following sections:
Alternative-fuel Vehicles describes characteristics of the 130,000 alternative-fuel vehicles owned by Georgians. Hybrid cars are the most common AFV, followed by electric vehicles (EVs). AFVs are disproportionately owned by Atlanta households and high-income households. AFVs are driven somewhat fewer miles than other vehicles of a similar age.
Shared Mobility discusses shared mobility, focusing on carsharing, bikesharing, and ridehailing. The section compares demographic characteristics of the users of these services to those of the general public. It also estimates the total monthly trips using these modes. While carsharing and bikesharing use are still relatively uncommon, 1 in 10 Georgians has used a ridehailing app in the past 30 days. This analysis estimates that ridehailing now accounts for 87 percent of all vehicle-for-hire trips, with the remainder comprising trips in conventional taxi and limo services. Ridehailing accounts for an even higher percentage of VFH trips in small MPOs and non-MPO counties.
Online Shopping discusses online shopping. In an average month, more than half of Georgians ages 16+, and more than two thirds of Georgia households, place at least one online order for delivery. This section discusses demographic differences in who is shopping online and how frequently. Although online shopping is most common among
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adults ages 1852, two in five seniors (6579) and one in five elderly adults (80+) have placed an order in the past 30 days. Online shopping is much more common among the wealthy and white, and uncommon among low-income people and carless households that could presumably benefit from the convenience of having goods delivered.

ALTERNATIVE-FUEL VEHICLES

As shown in table 103, in 2017, 1.9 percent of Georgia's vehicle fleet was composed of vehicles using alternative-fuel sources. These alternative-fuel vehicles comprise a larger percentage (2.5 percent) of the vehicle fleet in Atlanta than elsewhere in the state.

Table 103. Number of alternative-fuel vehicles by MPO tier.

All Vehicles* (N=16,921)

Alternative-fuel Vehicles (AFVs) (N=313)

Statewide

6,982,773

130,216 (1.9%)

MPO Tier

1. Atlanta MPO

3,679,778

92,511 (2.5%)

2. Medium MPOs

1,120,485

18,619 (1.7%)

3. Small MPOs

707,626

7,001 (1.0%)

4. Non-MPO counties

1,474,885

12,085 (0.8%)

* Includes gas, diesel, and AFVs.

AFVs include hybrids, electric, plug-in hybrids, flex fuel, ethanol, and bifuel engines.

As shown in table 104, AFVs were dominated by hybrid vehicles, which accounted for 61.7 percent of the fleet. Electric vehicles were the second-most common, accounting for 29.5 percent of the fleet. Plug-in hybrids comprised only 4.9 percent of the fleet, but national sales of plug-in hybrids increased substantially in 2018 and 2019, after the close of survey data

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collection.67 Conventional-fuel vehicles are dominated by gasoline vehicles, with a small percentage of diesel engines.

Table 104. Fuel type of alternative- and conventional-fuel vehicles.

Weighted Percent

All Vehicles

All Alternative-Fuel Vehicles (AFVs)*

Hybrid

61.71%

Electric

29.48%

Plug-in hybrid

4.89%

Flex fuel or E85

3.16%

Other AFV (bifuel and unspecified) All Conventional-Fuel Vehicles (CFVs)

0.75%

Gas

97.79%

Diesel Other CFV

2.19% 0.02%

Note: Percentages shown are percent of category (AFV or CFV).

Unweighted Sample Size
16,546
313 228
64 11
8 2 16,606 16,233 367 6

Unweighted Percent
72.84% 20.45%
3.51% 2.56% 0.64%
97.75% 2.21% 0.04%

* All vehicle years (19872016). When the sample of AFVs is limited to recent vehicles (2004 or later), it contains 222 hybrids (61.8% weighted), 58 electric (29.5% weighted), 10 plug-in hybrid (5.0%), 7 flex fuel/E85 (3.2%), and 1 other (0.5%). Electric and plug-in hybrids are more common among recent vehicles, which correlates with a decrease in hybrid vehicles as a percentage of the overall fleet. All vehicle years (1900-2017). When the sample of CFVs is limited to recent vehicles (2004 or later), it contains 11,052 gas vehicles (98.1% weighted), 232 diesel vehicles (1.9%), and 1 "high tech" (.003%).

Other CFV includes three "high-tech" and one each of nitro burner, C16 racing fuel, and unspecified mixture.

Table 105 shows more details on AFVs, in comparison with all vehicles in Georgia. Because 96 percent of AFVs are from 2004 or later, this report provides statistics for all recent vehicles (from 2004 onward) to control for vintage when comparing AFVs to the fleet at large.

67 See https://afdc.energy.gov/data/10567.
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The median age of AFVs is 4 years, compared to 10 years for all vehicles in Georgia. AFVs are also slightly newer than the typical recent vehicle. More than 80 percent of AFVs are cars or wagons, versus around half of all vehicles and recent vehicles. The complete lack of certain body types among AFVs, notably vans, indicates that some body types are more readily available as AFVs. Mean annual miles driven is lower for AFVs (11,778) than the state average (11,940), and also lower than the average for recent vehicles (13,109).
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Table 105. Vehicle characteristics: AFVs, all vehicles, and recent vehicles.

Vehicle Characteristics

Alternative-fuel Vehicles (AFV)

All Vehicles

Recent Vehicles (20042017)

Vehicle Age (Years)

Mean

5.5

10.9

6.7

Median

4

10

6

Annual Miles Driven in Vehicle

Mean

11,778

11,940

13,109

Median

10,915

9,585

10,913

Odometer Mileage

Mean

57,725

105,440

80,804

Median

39,000

94,214

68,932

Vehicle Type (Column Percentage)

Car/wagon

81.7%

49.5%

52.2%

Van

0.0%

5.4%

5.5%

SUV

6.5%

23.6%

26.2%

Pickup

4.3%

17.5%

13.3%

Other truck

0.0%

0.6%

0.4%

RV

0.6%

0.7%

0.3%

Motorcycle

0.0%

2.4%

1.9%

Something else

6.9%

0.3%

0.3%

Vehicle Age Cohort* (Column Percentage)

Pre-LEV (pre-1993)

1.9%

4.6%

0.0%

LEV1 (19932003)

2.5%

26.7%

0.0%

LEV2 (20042014)

67.7%

56.3%

82.0%

New vehicles (20152017)

27.9%

12.3%

18.0%

Newly-purchased (Past 12 Months)

Not newly purchased

81.3%

84.2%

81.5%

Newly purchased

18.7%

15.8%

18.5%

* Cohorts are based on the passage of California's Low Emissions Vehicle (LEV) standards, which serve as a proxy for

tightening emissions standards.

Figure 25 shows the weighted distribution of annual miles driven. Compared to all vehicles or all recent vehicles, the AFV distribution has a thicker left tail (more vehicles driven less than a thousand miles per year) and a thinner right tail. This dynamic illustrates why AFVs have a higher median and lower mean mileage compared to all vehicles (table 105).

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Figure 25. Histograms. Annual miles driven by AFVs, all vehicles, and recent vehicles. 224

Table 106 provides greater details on the average annual miles driven by fuel type for all vehicle years and for recent vehicles (from 2004 onward). On average, AFVs are driven fewer miles than CFVs (11,778 miles versus 11,943 miles). As shown by the lower standard deviation, there is also less variation in how far AFVs are driven. The distribution of AFVs shown in figure 25 is not as strongly right-skewed as the distribution of vehicles in general; 1.1 percent of CFVs are driven more than 50,000 miles annually, versus just 0.05 percent of AFVs.

Table 106. Annual miles driven by fuel type.

All vehicles All conventional-fuel vehicles (CFV)

Mean Annual Miles Driven in Vehicle
11,940
11,943

Standard Deviation* 12,388 12,455

Gas

11,900

12,301

Diesel All alternative-fuel vehicles (AFV)

14,004 11,778

18,177 8,079

Hybrid and plug-in hybrid

12,315

7,930

Electric

9,652

7,529

All recent vehicles (2004 or later) All recent CFV

13,109 13,134

12,841 12,942

Gas

13,076

12,717

Diesel All recent AFV

16,277 12,131

21,720 8,071

Hybrid and plug-in hybrid

12,580

7,971

Electric

10,124

7,442

* Based on an estimate of simple random sampling (SRS) variance within each subpopulation.

Includes gas, diesel, and other CFV.

Includes hybrid, plug-in hybrid, electric, flex fuel/E85, and other.

Table 106 also shows heterogeneity within AFVs and CFVs. Among CFVs, diesel vehicles have a higher mean annual miles driven than gasoline vehicles. Among AFVs, hybrids and plug-in hybrids have a larger mean annual miles driven than fully electric vehicles. It is likely this

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difference is due in part to technological limitations on the range of the electric vehicles available in 2016 (the most recent model year of EVs in the sample) and before. The difference in annual miles driven between AFVs and CFVs cannot be assumed to be a product of the vehicles themselves; they may reflect differences in the lifestyles and preferences of the people who chose to purchase AFVs versus CFVs. This section turns now to the demographic characteristics of the households and main drivers of AFVs as compared to vehicle owners in general. Table 107 shows the distribution of AFVs and other vehicles among different types of households. Of AFVs, 71 percent are owned by households in Atlanta, compared to 56 percent of recent vehicles and 53 percent of all vehicles. AFVs are mostly owned by wealthier households that own, on average, 2.7 vehicles. One-driver households are less common than the average among AFVs. Table 106 showed that AFVs are driven less than CFVs on average. Table 107 shows that this is still the case when annual miles are normalized by total household vehicles, household drivers, or household members of driving age.
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Table 107. Household characteristics for AFVs, all vehicles, and recent vehicles.

Household Characteristics

Alternative-fuel Vehicles (AFV)

All Vehicles

Recent Vehicles (20042017)

MPO Tier

1. Atlanta MPO

71.0%

52.7%

55.9%

2. Medium MPOs

14.3%

16.0%

16.1%

3. Small MPOs

5.4%

10.1%

9.6%

4. Non-MPO counties

9.3%

21.1%

18.3%

Annual Household Income

<$35,000

9.4%

25.3%

20.1%

$35,000 to $49,999

6.1%

12.0%

11.6%

$50,000 to $74,999

10.2%

18.4%

18.7%

$75,000 to $99,999

17.6%

13.7%

14.3%

$100,000+

56.7%

30.6%

35.3%

Number of Household Vehicles

Mean

2.74

2.66

2.54

Median

2

2

2

Number of Household Drivers (Ages 16+)*

Mean

2.23

2.10

2.10

One driver

14.3%

22.3%

20.9%

Two drivers

57.4%

53.4%

55.6%

Three or more drivers

28.3%

Annual Miles Driven in All Household Vehicles (Mean)

24.1%

23.3%

Total

30,476

29,743

29,908

Per vehicle Per household driver

11,314 13,589

11,743 14,621

12,356 14,693

Per household member ages 16+

13,271

13,766

13,945

* 45 vehicles (unweighted) were in households that reported having zero drivers, representing 0.2 percent of all vehicles

(weighted) and 0.1 percent of recent vehicles (weighted). No AFVs were owned by zero-driver households.

The sum of all miles driven in all household vehicles, whether by a household driver or someone else. Does not include

miles driven by household drivers in rental cars or other non-household vehicles.

Households with zero drivers recoded as one driver for the purposes of this statistic.

Table 108 shows driver characteristics for AFVs, all vehicles, and recent vehicles. The main driver for an AFV is more likely to be male, white, highly educated, and a worker. These

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demographic factors may correlate with AFV ownership due to demographic differences in attitudes, disposable income, and travel needs, or a combination of these factors.

Table 108. Main driver characteristics for AFVs, all vehicles, and recent vehicles.

Driver Characteristics Sex Male Female Age Cohort Under 18 Millennial and Gen Z (1636) Gen X (3752) Pre-retirement age Boomer (5364) Seniors (6579) Elderly (80+) Race White non-Hispanic only Black, Black multiracial, and Black Hispanic Other Employment Status Nonworker Worker Educational Attainment High school or less Some college or associate degree Bachelor's degree Graduate or professional degree

Alternative-fuel Vehicles (AFV)
53.0% 47.0%
2.8% 16.9% 37.4% 27.8% 13.6%
1.5%
78.3% 10.8% 10.9%
27.4% 72.6%
8.8% 21.3% 35.9% 34.0%

All Vehicles
50.2% 49.8%
1.3% 22.9% 31.5% 25.4% 16.6%
2.5%
64.3% 24.7% 10.9%
33.8% 66.2%
25.9% 32.0% 23.2% 18.9%

Recent Vehicles (20042017)
44.4% 55.6%
1.2% 23.7% 33.4% 24.0% 15.6%
2.0%
65.0% 23.8% 11.2%
31.4% 68.6%
21.0% 30.8% 26.3% 21.9%

SHARED MOBILITY Shared mobility, as defined by the Shared Use Mobility Center, broadly encompasses "transportation services and resources that are shared among users, either concurrently or one

228

after another."68 Some forms of shared mobility, such as carpooling and public transit, are not reliant on emerging technologies. However, the proliferation of location-enabled smartphones and other new technologies have facilitated the evolution of several forms of shared mobility. These include bike- and carsharing systems, as well as the rise of ridehailing apps such as Uber and Lyft. More recently, scooter sharing has become a part of the transportation landscape, particularly in the Atlanta region. However, this recent development is not reflected in the 2017 NHTS data.
This section focuses on bikesharing, carsharing, and ridehailing, the latter of which is sometimes referred to as ridesharing.69 It provides a portrait of shared mobility use from when the data were collected in 20162017. With the increased availability of many types of shared mobility, it is likely that usage of all of these services has risen in the intervening years.
Methods NHTS assesses use of shared mobility services with the questions shown in table 109.
As table 109 shows, 10.2 percent of Georgians ages 16 and up report having used a ridehailing app at least once in the past 30 days, and 1.0 percent report using a carsharing app. Because the question about bikesharing was only asked of respondents who had bicycled within the past 7 days but asked about bikesharing use within the past 30 days, it is not possible to get a precise estimate of bikesharing use among the general population.70 However, 8 percent of recent
68 https://sharedusemobilitycenter.org/what-is-shared-mobility/. 69 The NHTS uses the word "ridesharing." The researchers prefer to use "ridehailing" in this report because it avoids ambiguity and confusion with other forms of shared rides, such as carpooling. 70 For example, someone who used a bikesharing service 14 days previously and had not biked since would not have been asked whether she had used a bikesharing service.
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cyclists (those who rode a bike within the past 7 days) reported having used a bikesharing service at some point in the past 30 days.

Table 109. Shared mobility questions in the NHTS.

Mobility Type

Question Wording

Who Was Asked?

Ridehailing

In the past 30 days, how many All respondents times have you purchased a ride ages 16+ with a smartphone rideshare app (e.g., Uber, Lyft, Sidecar)?

Affirmative Responses (Ages 16+)
Weighted Unweighted 10.2% 1,176 (7.54%)

Carsharing

In the past 30 days, how many All respondents times did you use a carsharing ages 16+ service where a car can be rented by the hour (e.g., Zipcar or Car2Go)?

1.0%

104 (0.67%)

Bikesharing

In the past 30 days, how many times did you use a bikeshare program (e.g., Bikeshare, Zagster, or CycleHop)?

All recent cyclists* ages 5+

8.0%

51 (5.70%)

* Reported at least one cycling trip in the past 7 days. Recent cyclists represent 5.8 percent of respondents (unweighted).

In addition to these questions, when respondents filled out their travel diary, one of the choices of mode for trips was "Taxi/limo (including Uber/Lyft)." Because ridehailing services are combined with more traditional vehicle-for-hire services, travel diaries do not provide a precise estimate of ridehailing usage. However, those data are included here to provide a baseline for future analysis. To provide an exploratory estimation of what proportion of these trips are using a ridehailing app, we also compare them with estimates of total trips based on the question in table 109.

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As table 109 also shows, because bikeshare and carshare services are utilized by a small fraction of the population, the sample sizes for users of these services are quite small. Survey weights are unreliable with small sample sizes; thus, the analysis of these two user groups will be based on unweighted data and may not be representative. The results should therefore be interpreted with caution.
In addition to the 51 adults who reported using bikeshare systems, 26 children between ages 5 and 15 were recorded as bikeshare users. Ten of these were under the age of 10. Bikesharing apps require a smart phone and a credit card. Most, including Relay and Zagster, two services available in Georgia, require riders to be at least 18 years of age. Some require a governmentissued ID for verification. Further, the available bicycles are sized for adult riders.
One potential explanation for this finding is that some children were riding bicycles that had been unlocked on their behalf by adults. Relay, for example, allows members to unlock up to four bicycles at a time (though they still legally require all riders to be ages 18+). Another potential explanation is that parents filling out the survey on their child's behalf interpreted the question differently than intended, for example including borrowing a bicycle from a neighbor or some other kind of bicycle-sharing program. More targeted data collection would provide better information about this finding, and about bikesharing in general.
Shared Vehicles: Bikesharing and Carsharing Table 110 shows demographic breakdowns of bikeshare users, recent cyclists who have not used a bikeshare, and carshare users. Weighted statistics for the population as a whole are included for comparison. Results should be interpreted with caution due to small sample sizes (included in the table).
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Table 110. Demographics of bikeshare and carshare users ages 16+.

Bikeshare Users Past 30 Days (Unweighted)

Other Recent Cyclists*
(Unweighted)

Carshare Users All Persons Past 30 Days Ages 16+ (Unweighted) (Weighted)

Total

51 (100%)

842 (100%)

104 (100%)

MPO Tier

1. Atlanta MPO

13 (25%)

230 (27%)

38 (37%)

54.4%

2. Medium MPOs

23 (45%)

348 (41%)

38 (37%)

15.8%

3. Small MPOs

12 (24%)

191 (23%)

19 (18%)

10.1%

4. Non-MPO counties

3 (6%)

73 (9%)

9 (9%)

19.8%

Sex

Male

27 (53%)

503 (60%)

42 (40%)

47.9%

Female

24 (47%)

339 (40%)

62 (60%)

52.1%

Age Cohort

Millennial and Gen Z (1636)

23 (45%)

237 (28%)

35 (34%)

36.7%

Gen X (3752)

10 (20%)

237 (28%)

22 (21%)

27.6%

Pre-retirement age Boomer (5364)

8 (16%)

222 (26%)

27 (26%)

19.5%

Retirement age (65+)

10 (20%)

146 (17%)

20 (19%)

16.2%

Race

White non-Hispanic only

18 (35%)

634 (75%)

41 (39%)

54.8%

Black and Black multiracial

23 (45%)

146 (17%)

51 (49%)

32.1%

Other

10 (20%)

62 (7%)

12 (12%)

13.1%

Driver Status

Nondriver

9 (18%)

69 (8%)

11 (11%)

12.6%

Driver

42 (82%)

773 (92%)

93 (89%)

87.4%

Mobility Impairment

Absent

45 (88%)

811 (96%)

88 (85%)

90.7%

Present

5 (10%)

31 (4%)

15 (14%)

9.3%

Annual Household Income

<$15,000

12 (24%)

104 (12%)

23 (22%)

14.5%

$15,000 to $34,999

15 (29%)

86 (10%)

21 (20%)

19.6%

$35,000 to $74,999

9 (18%)

207 (25%)

28 (27%)

28.4%

$75,000+

13 (25%)

421 (50%)

28 (27%)

37.4%

Vehicle Deficit Category of Household

Zero-vehicle

6 (12%)

44 (5%)

13 (13%)

5.0%

Deficit (hard or soft)

18 (35%)

133 (16%)

22 (21%)

27.0%

Nondeficit (sufficient/surplus)

27 (53%)

665 (79%)

69 (66%)

68.0%

Transit Use, Past 30 Days

No

36 (71%)

714 (85%)

66 (63%)

88.9%

Yes

15 (29%)

127 (15%)

38 (37%)

11.1%

Walking, Past 30 Days

No

4 (8%)

79 (9%)

18 (17%)

28.0%

Yes

47 (92%)

763 (91%)

86 (83%)

72.0%

Note: Because survey weights may not produce accurate estimates for small subsamples, unweighted percentages are shown

for carshare and bikeshare users, as well as other recent cyclists.

* Respondents who reported at least one cycling trip within the past 7 days but did not report using a bikeshare within the past 30 days.

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While the majority of Georgia's population is in Atlanta, the plurality of bikeshare users, as well as of other recent cyclists, live in medium MPO areas. Carshare users are divided evenly between Atlanta and medium MPO areas, with a smaller proportion of users in small MPO areas and non-MPO counties.
There is a well-documented gender gap in cycling (Emond et al. 2009). Men dominate among recent cyclists who are not bikeshare users, but bikeshare users are more evenly divided by gender. The majority of carshare users surveyed are female.
Compared to bikeshare nonusers and the general population, a larger proportion of bikeshare users are Black. Black residents also make up a larger proportion of carshare users than the general population. Low-income people make up a larger share of carshare users than of the general population. They are also disproportionately represented among bikeshare users compared to other recent cyclists.
About half of bikeshare users live in vehicle-deficit or zero-vehicle households, compared to just one third of the general population. Interestingly, the percent of carshare users from vehiclenondeficit households is approximately equal to that of the general population.
With respect to walking and transit use, compared to the general population, a higher proportion of bikeshare and carshare users have used these modes. In the case of walking, there is little difference between bikeshare users and other recent cyclists. However, bikeshare users are more likely to be transit users than other recent cyclists are.
As shown in table 111, Georgians who had used a bikeshare at least once in the past 30 days reported an average of 5.6 uses, versus 3.8 carshare uses among users. This is equivalent to
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497,000 carshare uses per month (258,000) and 165,000 bikeshare uses (77,000). The bikeshare estimate should be taken with further caution because it does not include users who rode a bike within the past 30 days but not within the past 7 days.
Table 111. Frequency of carshare and bikeshare use among users ages 16+, past 30 days.

Bikeshare Users

All Users

5.6

MPO Tier

1. Atlanta MPO

5.3

2. Medium MPOs

6.5

34. Small MPOs and non-MPO counties

4.5

Sex

Male

5.7

Female

5.5

Age Cohort

1652 (Gen X, Millennial, and Gen Z)

5.0

53+ (Baby Boomer and retirement age)

6.7

Race

White non-Hispanic only

7.1

Black and other

4.8

Annual Household Income

<$35,000

5.4

$35,000+

5.9

Vehicle Deficit Category of Household

Deficit or zero-vehicle

5.1

Nondeficit (sufficient/surplus)

6.0

Carshare Users 3.8
3.8 2.9 5.1
3.0 4.4
4.4 3.1
2.6 4.6
4.5 3.3
3.8 3.8

Even after accounting for the small sample size, there were statistical differences in frequency of bikeshare use by race, income, and vehicle-deficit category (=.01).71 Although, as shown in table 110, Blacks are more likely to have used bikesharing at all in the previous 30 days, table 111 shows that the amount of bikeshare use during that time is greater among users who are

71 Calculated by a t-test for two-group categories and chi-squared test for categories with 3+ groups.
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white non-Hispanic than among users of color. It is lower among the lowest-income users (those in households with an annual income of less than $35,000) than among moderate- and highincome users. It is higher among users in vehicle-nondeficit households than those from zerovehicle or vehicle-deficit households. Taken together, table 110 and table 111 suggest that although bikeshare adoption is associated, to some extent, with markers of necessity such as lack of transportation alternatives or financial resources, bikeshare usage is not.
Among carshare users, there were marginally-significant differences by race (=.10), with people of color reporting two more usage occasions in the past 30 days (4.6), on average, than non-Hispanic whites (2.6).
Ridehailing As shown in table 112, 10.2 percent of Georgians ages 16 and older reported using a ridehailing app within the past 30 days. This is comparable with the national figure of 9.8 percent found by Conway, Salon, and King (2018). Ridehailing adoption was most common in Atlanta and least common in non-MPO counties. However, users in non-MPO counties made substantially more trips by ridehailing in that timeframe. There were not pronounced differences in the percent of people who used ridehailing by gender or race. However, among ridehailing users, Black riders made more trips than white riders and people of other races. Millennials and members of Gen Z were more likely to use ridehailing apps and made more trips than older users.
Two groups with elevated transportation needs, nondrivers and people with mobility impairments, were less likely to use ridehailing. However, those nondrivers and people with mobility impairments who did use a ridehailing app made more trips than drivers and people without mobility impairments.
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The percentage of people who use ridehailing is highest among people living in households with an annual income of $75,000 or more per year. However, among ridehailing users, the lowestincome users made more trips per person. These results contrast with those for bikesharing (see Shared Vehicles: Bikesharing and Carsharing), in which adoption was higher for lower-income people, but trip frequency was lower. Georgians who had used transit were also more likely to report using a ridehailing app (31.6 percent versus 7.5 percent) and made more trips (5.3 versus 4.7). Georgians who had reported one or more walking trips were also more likely to report using ridehailing but reported fewer ridehailing trips than those who had not made any pedestrian trips. Table 113 presents similar information as table 112. However, instead of showing the percentage of, for example, Atlanta residents who use ridehailing, it shows the percentage of ridehailing users who are from Atlanta.
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Table 112. Ridehailing use among Georgians ages 16+.

All persons ages 16+ MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO counties Sex Male Female Age Cohort Millennial and Gen Z (1636) Gen X (3752) Pre-retirement age Boomer (5364) Retirement age (65+) Race White non-Hispanic only Black and Black multiracial (incl. Black Hispanic) Other Driver Status Nondriver Driver Mobility Impairment Absent Present Annual Household Income <$15,000 $15,000 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+ Vehicle Deficit Category of Household Zero-vehicle Deficit (hard or soft) Nondeficit (sufficient/surplus) Transit Use, Past 30 Days No Yes Walking, Past 30 Days No Yes

Percent Who Have Used Ridehailing
(past 30 days) 10.2%
14.6% 7.6% 4.3% 3.0%
10.9% 9.5%
15.3% 12.2%
4.9% 1.5%
10.3% 10.1%
9.7%
8.9% 10.4%
10.8% 4.4%
7.2% 6.6% 5.2% 7.8% 7.8% 11.0% 17.8%
15.6% 7.2%
11.0%
7.5% 31.6%
7.0% 11.4%

Mean Trips Among Ridehailing Users
4.9
4.6 4.1 2.6 13.0
4.9 5.0
5.4 4.6 3.6 2.7
3.9 7.1 3.6
6.2 4.8
4.8 7.8
11.1 6.1 4.9 3.7 3.7 5.2 3.9
8.5 4.2 4.7
4.7 5.3
7.6 4.3

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Table 113. Demographics of ridehailing users (column percentages).

MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO counties Sex Male Female Age Cohort (adults only) Millennial and Gen Z (1636) Gen X (3752) Pre-retirement age Boomer (5364) Retirement age (65+) Race White non-Hispanic only Black and Black multiracial (incl. Black Hispanic) Other Driver Status Nondriver Driver Mobility Impairment Absent Present Annual Household Income <$15,000 $15,000 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+ Vehicle Deficit Category of Household Zero-vehicle Deficit (hard or soft) Nondeficit (sufficient/surplus) Transit Use, Past 30 Days No Yes Walking, Past 30 Days No Yes

Ridehailing Users (Past 30 Days)
78.2% 11.8%
4.3% 5.8%
51.5% 48.5%
55.0% 33.2%
9.4% 2.5%
55.5% 32.0% 12.5%
11.0% 89.0%
96.0% 4.0%
10.2% 6.1% 5.2% 9.0%
12.5% 12.6% 44.4%
7.7% 19.0% 73.3%
65.6% 34.4%
19.4% 80.6%

All Georgians Ages 16+
54.4% 15.8% 10.1% 19.8%
47.9% 52.1%
36.7% 27.6% 19.5% 16.2%
54.8% 32.1% 13.1%
12.6% 87.4%
90.7% 9.3%
14.5% 9.5%
10.1% 11.9% 16.6% 11.7% 25.7%
5.0% 27.0% 68.0%
88.9% 11.1%
28.0% 72.0%

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Ridehailing and Vehicle-for-Hire Trips This section estimates total annual ridehailing trips and compares them with the estimated total of trips by vehicle-for-hire. VFH trips include ridehailing trips, as well as more traditional taxi and limo services. This is done by comparing responses to the ridehailing question analyzed in the previous section (Ridehailing) with reported trips by VFH on the travel day (table 114).

Table 114. Unweighted sample sizes for ridehailing and vehicle-for-hire trips by Georgians ages 16+

Rideshare (Past 30 Days)*

All Vehicle-for-Hire Trips (Travel Day

Users

Times Used

Users

Trips

All Georgia

1176

4741

109

205

MPO Tier

1. Atlanta MPO

689

2920

62

111

2. Medium MPOs

349

1209

25

45

3. Small MPOs

108

326

18

38

4. Non-MPO counties

35

286

4

11

* Based on the following question: "In the past 30 days, how many times have you purchased a ride

with a smartphone rideshare app (e.g., Uber, Lyft, Sidecar)?"

Based on trips on the travel day with mode recorded as "Taxi/Limo (including Uber/Lyft)."

While 10 percent of Georgians (ages 16 and older) reported using a ridehailing app within the past 30 days (table 112), VFH trips only accounted for 0.6 percent of reported trips on the travel day itself (weighted), or 165 total trips (unweighted). Because of the small sample sizes, the researchers combine MPO tiers in two different ways: (1) the Atlanta region versus tiers 24 combined, and (2) tiers 1 (Atlanta), 2, and 3+4 combined.
As shown in table 115, Georgians ages 16+ take an estimated 56 million trips by taxi, limo, and ridehailing per year (based on the travel diaries), and 49 million trips by ridehailing alone (based

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on the ridehailing question). Therefore, an estimated 87.0 percent of vehicle-for-hire trips took place by ridehailing, and the remaining 13.0 percent by traditional taxis and limos.72

Table 115. Monthly and annualized estimates of ridehailing and vehicle-for-hire trips by Georgians ages 16+.

All Georgia Region Atlanta MPO Rest of Georgia MPO Tier Atlanta MPO Medium MPOs Small MPOs & non-MPO counties

Ridehailing Trips*

All Vehicle-for-Hire (VFH) Trips

Percent Ridehailing

Ridehailing All

Per 30 Days Annual Per 30 Days Annual

VFH

4,001,029 48,679,186 4,597,320 55,934,059

87.0%

2,903,628 1,097,400

35,327,474 13,351,705

3,324,791 1,272,529

40,451,620 15,482,439

87.3% 86.2%

2,903,628 388,362 709,039

35,327,474 4,725,070 8,626,635

3,324,791 525,472 747,058

40,451,620 6,393,238 9,089,201

87.3% 73.9% 94.9%

* Based on the following question: "In the past 30 days, how many times have you purchased a ride with a smartphone

rideshare app (e.g., Uber, Lyft, Sidecar)?" Thirty-day estimates are converted to annual estimates by multiplying by

Based on trips on the travel day with mode recorded as "Taxi/Limo (including Uber/Lyft)." By default, trip weights

produce annual estimates, which have been converted to 30-day estimates by multiplying by 30/365.

Ridehailing accounts for a larger percentage of VFH trips in Atlanta (87.3 percent) compared to the rest of Georgia (86.2 percent). However, when the data are further disaggregated, ridehailing appears to make up a larger portion of VFH trips in small MPOs and non-MPO counties (94.9 percent) compared to medium MPOs (73.9 percent). While this may be an artifact of small sample sizes, one possible explanation is that traditional taxi services are not as widely available in small towns and rural communities.

72 Due to the small sample of VFH trips in the travel diary, the 95 percent confidence interval of VFH trips is between 52.2 million and 74.1 million. This would mean that the percentage of ridehailing trips might range between 66 and 93 percent, before accounting for additional uncertainty in the number of ridehailing trips.
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ONLINE SHOPPING

As online shopping becomes more prominent, it may change travel behavior in the form of fewer or shorter shopping trips, and may lead to longer-term changes by remaking the kinds and quantity of retail businesses in Georgia communities. Online shopping has become more prominent during the COVID-19 pandemic, but in 2017, it was already common. The NHTS asks respondents ages 16+ how many times in the past 30 days they "purchase[d] something online and had it delivered." As shown in table 116, 52 percent of Georgians reported placing at least one online order, and 66 percent of households contained at least one person who placed an online order.

Table 116. Online orders by MPO tier, past 30 days.

Percent Placing 1+ Orders

Persons Ages 16+

Households

Number of Orders

Total Orders

Per Household

All

51.9%

66.0%

12,332,000 16,725,000 3.4 4.6

MPO Tier

1. Atlanta MPO

57.1%

72.4%

7,297,000 9,805,000 3.7 5.0

2. Medium MPOs

51.1%

64.4%

2,047,000 2,828,000 3.4 4.8

3. Small MPOs

47.6%

62.3%

1,111,000 1,547,000 3.0 4.1

4. Non-MPO counties

40.4%

52.3%

1,878,000 2,546,000 2.6 3.5

Note: An order consists of instances when a household member "order[ed] something online and had it delivered" in the past 30 days. Because orders may be split into multiple shipments by the companies fulfilling them, the number of deliveries may exceed the number of orders.

Number of household orders is derived from responses of individual household members. In some cases, multiple household members may have listed the same order (such as a kitchen item to be used by household members generally). Therefore, the estimated number of orders is reported as a range. The high estimate assumes no overlap between household members' purchases, and the low estimate assumes total overlap.

The number of orders was asked of individual household members, and there is some possibility that multiple household members may have taken "credit" for the same purchase, for instance a

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piece of furniture that will be used by the entire family. The researchers therefore report total orders as a range. Per table 116, we estimate that Georgians placed between 12.3 and 16.7 million orders per year, or 3.44.6 per household (including those with zero orders).73 Online shopping was most common in Atlanta, where 72.4 percent of households placed one or more orders, and least common in non-MPO counties, where just 52.3 percent of households placed an online order. The average Georgia household has precisely one online shopper. However, when households with zero online purchases are excluded, the average is 1.6 online shoppers per household. Figure 26 shows the number of online shoppers in Georgia households.
73 There is not a 1:1 correlation between orders and resulting deliveries. A single order may contain multiple items, and companies that fulfill those orders may divide them into multiple shipments.
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Figure 26. Pie chart. Number of online shoppers per household, past 30 days. As shown in table 117, online shopping is nearly universal among high-income Georgians, with 94 percent of households that earn more than $100,000 a year making at least one online purchase. It is substantially less common among low-income households (32 percent of those earning less than $15,000). Among online shopping households, households earning $100,000 or more place an average of 710 online orders per month, compared to approximately 34 for all income categories below $35,000.
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Table 117. Household online shopping by MPO tier, annual household income, vehicle ownership, and race, past 30 days.

All households MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO counties Annual Household Income <$15,000 $15,000 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+ Vehicle Deficit Category of Household Zero-vehicle Deficit (hard or soft) Nondeficit (sufficient/surplus) Household Race All white non-Hispanic Nonwhite/mixed race (some or all household members are nonwhite and/or Hispanic)

Orders per Household

Households

Percent of Households

All

with 1+

with 1+ Online Order(s) Households Order(s)

66.0% 3.4 4.6

5.1 6.9

72.4% 64.4% 62.3% 52.3%

3.7 5.0 3.4 4.8 3.0 4.1 2.6 3.5

5.2 6.9 5.4 7.4 4.8 6.6 4.9 6.7

31.5% 47.0% 56.9% 66.0% 76.6% 80.6% 93.8%

1.1 1.3 1.7 2.0 2.0 2.5 2.7 3.5 3.4 4.6 4.4 5.9 6.6 9.5

3.4 4.2 3.7 4.3 3.6 4.4 4.1 5.3 4.5 6.0 5.4 7.4 7.0 10.1

25.2% 63.1% 70.6%

0.9 1.0 2.6 3.8 3.8 5.1

3.5 4.0 4.1 6.0 5.4 7.2

72.2% 4.0 5.5
58.9% 2.6 3.5

5.6 7.7
4.4 5.9

Note: An order consists of an instance when a household member "order[ed] something online and had it delivered" in the past 30 days. Number of household orders is derived from responses of individual household members. In some cases, multiple household members may have listed the same order (such as a kitchen item to be used by household members generally). Therefore, the estimated number of orders is reported as a range. The high estimate assumes no overlap between household members' purchases, and the low estimate assumes total overlap.

Similarly, white households are more likely to be online shoppers than nonwhite/mixed race households, and among online shopping households make more purchases on average. Vehiclesufficient households are more likely to be online shoppers and place more orders than households with fewer available vehicles.

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Table 118 shows online orders by household composition. Households with children are more likely to place online orders. The average number of orders is also larger for online shopping households with children compared to those without children, and largest for households with children ages five or younger. However, when this is disaggregated by number of adults in the household, it is clear that this increase in online ordering is limited to households with two or more adults. Just 41.5 percent of single-caregiver households with children ages five or younger placed an online order, versus 57.3 percent of single adults without children and 75.9 percent of households with a young child and two or more adults. This is likely related to income; singleparent households face elevated economic challenges compared to other types of households. Whether or not a household is an online shopping household is strongly correlated with the number of members ages 16 or older. More than three quarters of households with three or more people ages 16+ made at least one online purchase, compared to half of households with one person age 16+. Residents of larger households are therefore more likely to live in an online shopping household, regardless of whether they personally make a purchase. Figure 27 shows the proportion of households where none, some, or all members ages 16+ placed an online order, subdivided by household size.
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Table 118. Household online shopping by household composition.

Orders per Household

Households

Percent of Households

All

with 1+

with 1+ Online Order(s) Households Order(s)

All households

66.0%

3.4 4.6

5.1 6.9

Number of Household Members Ages 16+

1

50.0%

1.9 1.9

3.8 3.8

2

71.3%

4.0 5.5

5.6 7.7

3+

78.0%

4.1 6.5

5.2 8.3

Household Composition (Categories) Working-age adult(s), no children under 16

69.0%

3.4 4.5

4.9 6.5

Youngest child 05

72.5%

5.0 7.0

6.8 9.7

Youngest child 615 Retired, no children under 16

73.9% 52.0%

4.2 5.8 2.0 2.6

5.7 7.9 3.9 5.0

Household Composition (Detailed)

One adult, no children 2+ adults, no children

57.3% 75.9%

2.3 2.3 4.0 5.8

4.0 4.0 5.3 7.6

One adult, youngest child 05

41.5%

1.8 1.8

4.3 4.3

2+ adults, youngest child 05 One adult, youngest child 6-15

78.3% 57.1%

5.6 8.0 2.4 2.7

7.1 10.3 4.3 4.6

2+ adults, youngest child 615

79.2%

4.8 6.9

6.0 8.7

One adult, youngest child 1621 2+ adults, youngest child 1621

62.4% 85.0%

3.3 4.4 4.7 7.3

5.2 7.1 5.6 8.6

One adult, retired, no children

34.1%

1.0 1.0

2.9 2.9

2+ adults, retired, no children

61.1%

2.5 3.4

4.1 5.6

Note: An order consists of an instance when a household member "order[ed] something online and had it delivered" in

the past 30 days.

Number of household orders is derived from responses of individual household members. In some cases, multiple household members may have listed the same order (such as a kitchen item to be used by household members generally). Therefore, the estimated number of orders are reported as a range. The high estimate assumes no overlap between household members' purchases, and the low estimate assumes total overlap.

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Figure 27. Pie chart. Household online shopping by household size, past 30 days.
This report now turns from examining households to examining the individuals who live in those households (table 119). Just over half of people ages 16+ personally placed an online order, but 71 percent live in an online shopping household (whether or not they personally placed an order).74
74 This is higher than the 66 percent of households that placed at least one order because larger households, which represent a higher share of the population than of households, are more likely to contain one or more online shoppers.
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As shown in table 119, elderly Georgians are the least likely to personally place an online order (20.4 percent) and also the least likely to live in an online shopping household (42.5 percent). Teens ages 1617 have the second-lowest percentage of online shoppers (29.5 percent), perhaps because of limited economic autonomy. However, unlike elderly Georgians, teens are about as likely as adults ages 1852 to live in an online shopping household. Groups with mobility challenges (i.e., nondrivers, people with mobility impairments, and residents of zero-vehicle and vehicle-deficit households) are all less likely to personally place online orders and to live in online shopping households than their nondisadvantaged counterparts. Table 120 provides more detail about which household members are making purchases. For example, 45 percent of teens (ages 1617) live in an online shopping household without having personally placed an order, more than twice the rate for any other group.
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Table 119. Online shopping by persons ages 16+, past 30 days.

Online Shoppers

Orders/Person

Online

In Online

All

Online

Shopper

Shopping Persons Shoppers

(Personally Household

16+

Only

Placed (1+ Order was

1+ Order) Placed in HH)

All

51.9%

70.8%

2.3

4.5

MPO Tier

1. Atlanta MPO

57.1%

75.5%

2.5

4.4

2. Medium MPOs

51.1%

71.4%

2.4

4.8

3. Small MPOs

47.6%

68.2%

2.1

4.4

4. Non-MPO counties

40.4%

58.8%

1.8

4.4

Sex

Male

49.2%

72.5%

2.1

4.3

Female

54.4%

69.3%

2.5

4.6

Age Cohort

Teens (1617)

29.5%

74.5%

0.8

2.7

Millennial and Gen Z (1836)

55.1%

75.9%

2.6

4.6

Gen X (3752)

61.0%

76.4%

3.1

5.0

Pre-retirement age Boomer (5364)

50.6%

66.9%

2.0

4.0

Senior (6579)

40.3%

57.8%

1.4

3.6

Elderly (80+)

20.4%

42.5%

0.5

2.4

Race

White non-Hispanic only

59.6%

77.5%

2.9

4.9

Black and Black multiracial

39.1%

58.2%

1.4

3.5

Other

51.0%

73.8%

2.2

4.4

Driver Status

Nondriver

23.8%

50.8%

0.9

3.6

Driver

55.9%

73.7%

2.5

4.5

Mobility Impairment

Absent

53.9%

72.6%

2.4

4.5

Present

32.1%

53.5%

1.3

4.1

Annual Household Income

<$15,000

24.8%

38.3%

0.8

3.3

$15,000 to $24,999

32.7%

52.6%

1.1

3.5

$25,000 to $34,999

42.5%

59.5%

1.4

3.4

$35,000 to $49,999

50.8%

69.5%

1.8

3.6

$50,000 to $74,999

55.9%

78.2%

2.3

4.1

$75,000 to $99,999

60.3%

80.2%

2.8

4.6

$100,000+

74.4%

94.3%

4.1

5.6

Vehicle Deficit Category of Household

Zero-vehicle

23.1%

29.9%

0.8

3.3

Deficit (hard or soft)

37.9%

66.1%

1.4

3.6

Nondeficit (sufficient/surplus)

59.6%

75.7%

2.8

4.7

Note: An order consists of an instance when a household member "order[ed] something online and had it

delivered" in the past 30 days.

249

Table 120. Which household members placed online orders in the last 30 days for people ages 16+.

Self Only

Other Household Member(s)
Only

Self and Other Household Member(s)

Nobody in Household

All

17.4%

18.9%

34.5%

29.2%

MPO Tier

1. Atlanta MPO

18.7%

18.4%

38.4%

24.5%

2. Medium MPOs

16.7%

20.3%

34.4%

28.6%

3. Small MPOs

16.9%

20.6%

30.7%

31.8%

4. Non-MPO counties

14.7%

18.4%

25.7%

41.2%

Sex Male

12.6%

23.3%

36.6%

27.5%

Female

21.8%

15.0%

32.6%

30.7%

Age Cohort Teens (1617)

4.0%

45.0%

25.5%

25.5%

Millennial and Gen Z (1836)

15.7%

20.7%

39.5%

24.1%

Gen X (3752)

20.1%

15.4%

40.9%

23.6%

Pre-retirement age Boomer (5364) 18.5%

16.4%

32.1%

33.1%

Senior (6579)

19.8%

17.5%

20.5%

42.2%

Elderly (80+)

10.5%

22.2%

9.8%

57.5%

Race

White non-Hispanic only

17.2%

17.9%

42.4%

22.5%

Black and Black multiracial

19.1%

19.1%

20.0%

41.8%

Other

14.0%

22.8%

36.9%

26.2%

Driver Status Nondriver

7.3%

27.0%

16.5%

49.2%

Driver

18.8%

17.8%

37.1%

26.3%

Mobility Impairment Absent

17.6%

18.7%

36.4%

27.4%

Present

15.7%

21.5%

16.3%

46.5%

Annual Household Income <$15,000

14.4%

13.5%

10.4%

61.7%

$15,000 to $24,999

20.2%

19.8%

12.5%

47.4%

$25,000 to $34,999

21.9%

17.0%

20.6%

40.5%

$35,000 to $49,999

21.1%

18.7%

29.7%

30.5%

$50,000 to $74,999

22.2%

22.4%

33.6%

21.8%

$75,000 to $99,999

16.8%

19.9%

43.5%

19.8%

$100,000+

12.3%

19.9%

62.1%

5.7%

Vehicle Deficit Category of Household

Zero-vehicle

15.3%

6.9%

7.8%

70.1%

Deficit (hard or soft)

11.2%

28.1%

26.7%

33.9%

Nondeficit (sufficient/surplus)

20.0%

16.2%

39.5%

24.3%

Note: An order consists of an instance when a household member "order[ed] something online and had it delivered" in

the past 30 days.

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Online Shopping and Travel Behavior Aside from online shopping during stay-at-home orders, such as during the COVID-19 pandemic, the extent to which online shopping replaces in-person shopping trips is an open question. Compared to Georgians who had not placed an online order within the past 30 days, a larger percentage of online shoppers made a trip to buy goods on the travel day (35.5 percent versus 29.7 percent) (table 121).

Table 121. Percent of persons ages 16+ who made a trip to buy goods on the travel day.

Online shoppers*

All persons ages 16+

35.5%

MPO Tier

1. Atlanta MPO

35.0%

2. Medium MPOs

37.2%

3. Small MPOs

37.6%

4. Non-MPO counties

34.3%

* Ordered something online and had it delivered within the past 30 days.

Others 29.7%
27.8% 31.4% 29.7% 32.2%

This may be partially explained by the fact that online shoppers tend to have higher incomes and be more mobile; thus, they tend to make more trips, in general, and to shop more at baseline, in particular. However, the NHTS data have a number of limitations for examining the links between online shopping and travel behavior. First, the NHTS provides a snapshot of a single day of travel behavior; shopping trips might be more accurately measured over a longer time span. Second, it is important to consider shopping trips at the household level. For example, a household may make one trip to the grocery store per week, so whether an individual household member visited the grocery store is dependent on whether or not another household member did so. Third, the type of goods purchased and amount being spent should be considered. To what

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extent are online shoppers shifting their purchases of staple goods to the internet versus making specialty purchases that they might otherwise have foregone?
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CHAPTER 5. SOCIAL INCLUSION AND EQUITY
CHAPTER 5 SUMMARY
Throughout, this report has drawn attention to intergroup differences in Georgians' travel behavior and access to transportation. This chapter will focus specifically on those differences that can result in reduced opportunity or quality of life for Georgians. Many underlying sources of social exclusion and economic inequality are not solely caused by the transportation system. However, a failure to consider the equity implications of transportation investment, policy, and planning decisions can exacerbate the negative impacts of poverty and prejudice.
In this chapter, the researchers use key mobility indicators to examine transportation disadvantage based on economic and social exclusion. We devote particular attention to the needs of two groups not covered in depth elsewhere in the report: immigrants and people with mobility impairments. We also examine gendered inequalities stemming from intrahousehold decisions about how to allocate resources and tasks.
Overview introduces the topic of social inclusion and discusses key findings from other chapters in this report as they relate to equity. This section also discusses the research team's choice of indicators for measuring equity.
Key Equitable Mobility Indicators examines mobility disparities using key indicators such as travel day and chronic immobility, number of trips, and access to vehicles. We consider the travel patterns of immigrant Georgians, finding that they are less mobile and less likely to be drivers than nonimmigrants. They also own fewer vehicles. Ethnic and
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transportation differences exist between immigrant Georgians of different education levels, but these travel patterns are extant among immigrants of all educational levels. Vehicle Access examines intrahousehold vehicle allocation and travel differences between vehicle-sufficient households and households that are not vehicle-sufficient. We find that captive transit users pay a time penalty for taking transit, not just in comparison to Georgians who use private autos, but also in comparison to choice transit users. Captive nonmotorized trips are also longer than choice nonmotorized trips, although (unlike the case with transit) the difference is based on different purposes for captive and choice trips rather than quality of service.
Turning to intra-household vehicle allocation, we find that when there is a shortage of vehicles, women are less likely to be the primary driver of a vehicle. The exception to this is for female caregivers of young children, perhaps because the vehicle is needed for child-serving trips. Women are also less likely to be the recipients of newly purchased vehicles, but somewhat counterintuitively more likely to have the newest vehicle by model year. Among teen drivers, girls are more likely than boys to be given the newest household vehicle, whether because they are considered more trustworthy drivers, or because of greater concern about their safety in case of mechanical difficulties, or both.
How Much and What For: The Interrelated Effects of Gender and Age on Mobility and Trip Purpose examines the interrelated effects of gender and age on travel behavior and immobility. This section shows that older women are at an elevated risk of becoming housebound, but because younger women are slightly more mobile than younger men, this risk may not be apparent in averages by gender alone. These results also draw
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attention to the need to apply an intersectional lens to analysis of transportation needs (considering the interaction between multiple sources of social exclusion). Health and Disability examines equity concerns related to disability status and health. Mobility and physical activity are compared between Georgians with and without mobility impairments, and between different subgroups of Georgians with disabilities. The researchers identify a strong correlation between low income and poor health among both disabled and nondisabled Georgians. Furthermore, this health disparity persists even when comparing Georgians at similar levels of physical activity. We also discuss the ways in which elderly Georgians and those with mobility impairments adapt their travel behavior, and use logistic regression to examine risk factors for immobility among Georgians with mobility impairments.
OVERVIEW
Unequal mobility and transportation access can diminish Georgians' capability of participating fully in economic, social, and political life (Nussbaum 2003, World Bank 2013). Transportation policymakers and planners can mitigate inequality through social inclusion, or "the process of improving the ability, opportunity, and dignity of people, disadvantaged on the basis of their identity, to take part in society" (World Bank 2013, p. 4). Improved transportation can increase Georgians' employment prospects and educational opportunity, and reduce social isolation (Suzuki, Cervero, and Iuchi 2013; Vasconcellos 2001; World Bank 2013).
Transportation disadvantage stems from two interconnected sources: (1) social exclusion, or barriers based on membership in a stigmatized or stereotyped group, and (2) poverty. The cumulative effect of social exclusion is often poverty. For example, 24 percent of Georgians
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make less than $25,000 per year. However, as shown in figure 28, while just 14.7 percent of white men fall into this income category, 40.1 percent of Black women have a household income of less than $25,000.
The effects of mobility impairments are even more striking: more than half of people with mobility impairments live in low-income households,75 and more than 70 percent of Black people with mobility impairments. It should be noted that the NHTS asks specifically about mobility impairments (a "condition or handicap that makes it difficult to travel outside of the home").76
While mobility impairments can also impede the ability to work, the need for a mobility aid such as a wheelchair in and of itself has no bearing on someone's capacity to engage in paid employment. However, a lack of high-quality, accessible, affordable transportation can pose a barrier to employment and education, regardless of the person's capacity to work or study (Bezyak et al. 2019, National Council on Disability 2015). In addition to physical barriers, people with mobility impairments also face social stigma (Papadimitriou 2008).
75 The rate is even higher for mobility-impaired people of working age than for people of retirement age (60 percent versus 46 percent), suggesting that this is not a result of higher rates of disability among the old and infirm.
76 Other disabilities, including intellectual and psychiatric disabilities, can also impact mobility (Bezyak et al. 2019). Based on the NHTS's question wording, it is unclear whether individuals with these conditions would have responded affirmatively to this question.
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All Adults White male White female Black male Black female Latino male Latina female Other male Other female
Adults without mobility impairment White male, nondisabled
White female, nondisabled Black male, nondisabled
Black female, nondisabled Latino male, nondisabled
Latina female, nondisabled Other male, nondisabled
Other female, nondisabled
Adults with mobility impairment White male, disabled
White female, disabled Black male, disabled
Black female, disabled Latino male, disabled
Latina female, disabled Other male, disabled
Other female, disabled

24.0% 14.7%
18.3% 33.2% 40.2%
21.3% 32.1%
22.0% 16.9%
20.8% 12.9% 14.9%
28.8% 36.3%
19.9% 29.0%
20.1% 14.6%
54.1% 40.0%
45.3% 68.7% 69.9%
54.5% 59.6%
49.9% 45.6%

Figure 28. Bar graph. Percent of adults with annual household income less than $25,000, by race, gender, and mobility impairment.

Figure 28 also illustrates how the effects of multiple forms of oppression overlap. Women of color are doubly disadvantaged by their gender and race. The gender gap in low-income incidence is greatest among Latinos; 21.3 percent of men and 32.1 percent of women live in households earning less than $25,000 per year. Black Georgians are the most likely to be in lowincome households, and the gender gap is narrower than for Latinos. White Georgians have the

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smallest percentage of low-income households and also the smallest gender gap. Disability is associated with a wider gendered low-income incidence gap among whites compared to the gap among nondisabled whites, but a narrower gap among Blacks and Latinos. To account for such intersectionality, it is important to consider the joint effect of multiple sources of disadvantage.
In addition, the effects of social exclusion can persist long after officially sanctioned discrimination has been addressed. For example, while redlining and race-based exclusions from mortgage financing have been banned for 50 years, the assistance provided to white families half a century ago provided them with a "head start" on building wealth. African Americans facing housing and employment discrimination had fewer assets to pass on to their children, contributing to a racial wealth gap. Today, the median net worth of whites is 1,000 percent of that of Blacks (Jan 2017).
Mainstreaming Equity One promising template for addressing inequality is what is known as gender mainstreaming, or "the process of assessing the implications for women and men of any planned action.... so that women and men benefit equally and inequality is not perpetuated" (United Nations Economic and Social Council 1997).
Gender mainstreaming has two key features. First, gender mainstreaming recognizes that, because of pre-existing inequalities, gender-blind planning (sometimes referred to as gender "neutral" planning) tends to result in unequal outcomes. To achieve equitable outcomes, it is necessary to have a gender-aware process.
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Second, under a gender mainstreaming approach, women's needs should not be thought of as a "special issue." Rather, transportation professionals should incorporate gender considerations into standard planning and analysis practices, for example by providing gender-disaggregated data and examining policies' differential impacts on men and women. To address a longstanding "gender data gap" (Criado-Perez 2019), studies focused specifically on gender are sometimes needed. However, in addition to such focused analysis, everyday policy and planning processes should also consider whether gender might affect the equity or efficacy of a proposed action.
The idea of mainstreaming can be applied to other sources of inequality. As engineers and planners evaluate the effects of proposed projects on congestion mitigation or transit ridership, they should also examine whether the benefits of those projects will mitigate or exacerbate inequality based on gender, race, disability, and other sources of social exclusion.
This report has followed an equity mainstreaming approach by incorporating analysis of equity into discussion of general travel and commuting patterns. The researchers will provide a centralized summary of key equity findings from elsewhere in the report before proceeding with more in-depth analyses.
Key Equity Findings Race White non-Hispanic Georgians make more trips than Georgians of other races, and are less likely to be immobile on the travel day (see chapter 1, Household and Personal Mobility). Trips made by nonwhite Georgians are shorter in distance, and overall PMT per capita for nonwhite Georgians is lower than PMT per capita for white Georgians. White Georgians' trips are more likely to be by personal occupancy vehicle, and their VMT per capita is higher. There are also
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racial differences in trip purpose. Whites make more discretionary trips and fewer trips to transport someone else (see chapter 1, Trip Purpose).
Focusing on work travel, Black Georgians are more likely to make complex commutes than white Georgians and Georgians of other races (see chapter 2, Overview of Commuters). Black Georgians' commute distances and overall commute PMT are similar to those of whites, but their commute times are longer (see chapter 2, Demographic Differences and Demographic Differences in Commute Duration). Part of this difference in commute duration is due to Black commuters' higher usage of slower modes, such as transit and nonmotorized travel (see chapter 2, Commute Mode by Person). Black Georgians are more likely than Georgians of other races to spend 2 or more hours on their daily commute (see chapter 2, Total Daily Commute Burden). Black Georgians are also more likely to say that transit safety is a concern (see chapter 1, Transit Service Preferences Among Workers). Additionally, white Georgians are more likely to have the flexibility to set their own schedule or work from home than are Black Georgians and Georgians of other races (see chapter 3, Work Flexibility for Whom?).
There are also racial differences in the uptake of new services and technologies. White Georgians are more likely to purchase alternate-fuel vehicles than are Georgians of other races, qualifying them for tax credits and other benefits of owning an expensive but efficient vehicle (see chapter 4, Alternative-fuel Vehicles). Similarly, white households are more likely to be online shoppers than nonwhite/mixed race households, and among online shopping households make more purchases on average (see chapter 4, Online Shopping). The users of carsharing and bikesharing, in contrast, are disproportionately Black (see chapter 4, Shared Vehicles: Bikesharing and Carsharing). There were not pronounced differences in the percent of people
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who used ridehailing by gender or race. However, among ridehailing users, Black riders made more trips than whites and people of other races" (see chapter 4, Ridehailing).
Black Georgians walk more than white Georgians and those of other races (e.g., Latino, Asian, Native American), but bike slightly less (see chapter 6, Travel Day Walking and Biking by Georgia Adults). Leisure accounts for 15 percent of Black Georgians' nonmotorized travel trips and legs, less than half the share for whites and Georgians of other races. NMT is more prominent as a mode of transit access/egress travel for Black Georgians. Among children, white children make fewer NMT trips than Blacks and children of other races (see chapter 6, Frequency of Nonmotorized Travel by Children). Among children who did walk or bike, average minutes of NMT is much higher for Black children (38.8) versus other races (25.7) and whites (21.4).
In this chapter, the researchers will discuss racial differences in immobility and travel time and compare the mobility of immigrant and nonimmigrant Georgians (see chapter 5, Key Equitable Mobility Indicators).
Income and Vehicle Ownership Low-income Georgians make fewer trips and are more likely to be immobile on the travel day (see chapter 1, Household and Personal Mobility). Trips, PMT, and VMT all increase with income (see chapter 1, Household and Personal Mobility). High-income people make more discretionary trips, though discretionary trips also make up a comparatively high share of trips by very low-income people (see chapter 1, Trip Purpose). It is possible that this latter spike is related to unemployment or underemployment: removal of commute and work-related trips from
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the "pie" will necessarily increase the share (of a smaller total number) of trips pertaining to some other purposes.
Nearly one third of the lowest-income households (those making less than $15,000 per year) own no vehicle (see chapter 1, Vehicle Ownership). This share decreases dramatically among the next-lowest income group; 6.9 percent of households making $15,00024,999 own zero vehicles. Just 0.1 percent of the highest-income households own zero vehicles. Low- and moderateincome households are also more likely to have a vehicle deficit (at least one vehicle, but fewer vehicles than potential drivers). Because zero-vehicle households are so uncommon outside of the lowest income bracket, these households are best thought of as carless by necessity rather than "car-free" by choice.
Among vehicle-owning households, the lowest and highest income households are more likely to have purchased at least one vehicle in the past 12 months (see chapter 1, Vehicle Ownership). However, while the average age of a vehicle purchased by a household earning at least $100,000 (the top 22 percent) is 5 years, the average vehicle purchased by a household earning less than $15,000 per year (the bottom 17 percent) is more than 11 years old and has more than 130,000 miles on the odometer.77 Vehicles near the end of their useful lives are financially more accessible to low-income households, but higher maintenance costs, high-interest auto loans, and predatory sales practices for used vehicles mean that the poor pay a premium for basic mobility (Karger 2003). It also means that low-income drivers must replace their vehicles more frequently.
77 The difference in median age is even larger. The median age of a vehicle purchased by a high-income household is 2 years, while the median age for low-income households is 12 years.
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The highest-income households (earning at least $100,000 per year) are substantially more likely to have hybrid or electric vehicles (see chapter 4, Alternative-fuel Vehicles). More than 9 percent of these households have at least one hybrid or electric vehicle, versus 6 percent of households making $75,00099,999 per year and fewer than 1 percent of vehicle-owning households making less than $25,000 per year. The income-based differences in vehicle age and quality are indicative of a de facto tax on poverty, where the poor pay more (Caplovitz 1967, Karger 2003).
Trips for the purpose of transporting someone else spike among lower-middle income households ($35,000$44,999 annual income) (see chapter 1, Trip Purpose). These households are more likely to own a vehicle than lower-income households, but more likely to have a vehicle deficit than higher-income households. An adult in one of these lower-middle income households will make 73 more person trips per year than someone in the next income category down, but 22 of those trips, or 30 percent, will be to transport someone else. Vehicle-deficit households are more mobile than carless households, but the disproportionate spike in trips to transport others suggest that the increase in the utility derived from travel is somewhat smaller.
Turning to work travel, commute times are longest for zero-vehicle households, and approximately equivalent in vehicle-deficit and nondeficit households (see chapter 2, Commute Times by Mode and Vehicle Ownership). By distance, in contrast, low-income workers have the shortest commutes and upper-middle income workers have the longest (see chapter 2, Demographic Differences). High-income workers are more likely to have the ability to set their own work schedules or work from home and are more likely to take advantage of that opportunity (see chapter 3, Descriptive Statistics on Access to Flexible Schedule and Work Location, and Travel Day Telecommuting).
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Mode choice has a U-shaped relationship with income, with walking, biking, and transit being most common among low-income commuters, least common among middle-income commuters, and somewhat more common among high-income commuters (see chapter 2, Commute Mode by Person). Low- and high-income commuters' views of what makes transit a "good option" for commutes differ, with higher-income Georgians more likely to prioritize convenience (schedule and location of stops) and lower-income Georgians more likely to prioritize cost and safety (see chapter 1, Transit Service Preferences Among Workers). High-income commuters are more concerned about speed, but there were no significant differences by income in preferences for consistent travel times.
However, there is a notable difference in level of service between captive and choice transit commuters: the average transit commute for a captive rider is 20.4 minutes longer than the average choice transit commute, despite the fact that the average distance of captive riders' commutes is 3.1 miles shorter than a choice rider's transit commute (see chapter 2, Commute Times by Mode and Vehicle Ownership). Commuters who, by necessity, use nonmotorized modes likewise walk or bike longer than commuters who have chosen these modes, suggesting that the "captive" pedestrians and cyclists documented in other countries (Pendakur 2011) also exist in Georgia. In chapter 5, Captive Travel will document that this difference between captive and choice travelers also applies to nonwork travel, and that the time penalty for captive transit users persists even after controlling for purpose, distance, and other variables.
Turning to new technologies and services, bikeshare and carshare users come disproportionately from vehicle-deficit and zero-vehicle households (see chapter 4, Shared Vehicles: Bikesharing and Carsharing). However, among users, residents of vehicle-deficit and zero-vehicle households
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use the service less than users from nondeficit households. The pattern for ridehailing usage is the opposite: adoption is higher among high-income people, but among users, low-income people use the service more frequently.
Walking and biking are most common among residents of the lowest income (less than $15,000 per year) and highest income ($100,000 or more per year) households, compared to their incidence among other income groups (see chapter 6, Access and Egress Travel). However, lowincome pedestrians and cyclists are more likely to walk/bike to access transit or desired destinations (instrumental travel), whereas higher income Georgians are more likely to walk and bike for leisure. Captive pedestrians' and cyclists' instrumental trips are longer than those of choice pedestrians and cyclists, even after controlling for other factors (see chapter 6, Captive and Choice Nonmotorized Travel). Among children, the percent who walk or bike on the travel day is similar across income brackets. However, the amount of time spent walking or biking varies substantially: an average of 52 minutes for children in the lowest-income households (< $15,000 per year) versus 2226 minutes for children in other income brackets (see chapter 6, Frequency of Nonmotorized Travel by Children).
In this chapter, the researchers will discuss the different conditions confronting captive and choice transit users (see chapter 5, Vehicle Access), the relationship between income and vehicle ownership and mobility (see chapter 5, Key Equitable Mobility Indicators), the association between disability and poverty (see chapter 5, Health and Disability), and how vehicle-deficit households allocate family vehicles (see chapter 5, Intra-Household Vehicle Allocation: Who Gets the Family Car?).
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Gender On average, men and women make a similar number of person trips. However, men make more vehicle trips (see chapter 1, Household and Personal Mobility). The average distance for men's trips is greater, and men generate more PMT and VMT. Additionally, a higher percentage of women are immobile on the travel day. More detailed analysis of the relationship between gender, age, and immobility appears in chapter 5, How Much and What For: The Interrelated Effects of Gender and Age on Mobility and Trip Purpose.
Women are more likely to make complex commutes than men, making it critical to accurately measure full work journeys rather than basing estimates on a single leg of the trip (see chapter 2, Overview of Commuters and Summary of Methods Implications for Upcoming Report Sections). Women's commute distances are shorter, but their commute times are not (see chapter 2, Demographic Differences in Commute Duration). Further, when commute distance is disaggregated by education level, it becomes apparent that the gender gap in distance only applies to workers without a college degree (see chapter 2, Demographic Differences).
Women's complex commutes are at least in part a reflection of their elevated "second shift" responsibilities in maintaining the household and providing childcare. However, they have less access to work accommodations, such as teleworking and flexible scheduling, that would make it easier to meet these commitments (see chapter 3, Work Flexibility for Whom?).
Women are more likely than men to commute by transit (see chapter 2, Commute Mode by Person), and more likely to list safety as an important aspect of transit service (see chapter 1, Transit Service Preferences Among Workers). Fewer women report walking and biking to be their "usual" commute mode, but when observed work journeys on the travel day are examined,
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there is no meaningful gender difference in the rate of nonmotorized commuting (see chapter 2, Commute Mode by Person).
Women are more likely to walk than men, but less likely to bike (see chapter 6, Overview). However, bikeshare users are more evenly divided by gender than cyclists as a whole (see chapter 4, Shared Mobility). Women are more likely than men to be discouraged from walking and biking by safety issues such as lack of sidewalks, heavy traffic, and inadequate night lighting (see chapter 6, Barriers to Walking and Biking More Frequently).
Men are more likely to own AFVs than women (see chapter 4, Alternative-fuel Vehicles). Multicaregiver families with children are more likely than single adults to take advantage of online shopping. However, single-parent households, which are predominantly female-headed, are less likely to have placed orders online (see chapter 4, Online Shopping).
In this chapter, the researchers will discuss how gender shapes women's mobility differently among different age groups: young women tend to be more mobile than average, while older women are substantially more likely to be immobile than older men and younger Georgians of all ages (see chapter 5, Key Equitable Mobility Indicators). This chapter will also discuss gender differences in vehicle allocation (see chapter 5, Intra-Household Vehicle Allocation: Who Gets the Family Car?) and the interaction between disability and gender (see chapter 5, Risk Factors for Immobility among Adults with Mobility Impairments).
Age and Disability Travel day immobility is very high among the elderly and those with a mobility impairment (see chapter 1, Household and Personal Mobility). In addition to making fewer trips, trips by elderly
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people and those with mobility impairments cover shorter distances. People with mobility impairments report very few work trips (see chapter 1, Trip Purpose).
Younger adults--millennials and members of Gen Z--were more likely to use ridehailing apps and made more trips than older users; thus, ridehailing services may be underused as tools for improving seniors' mobility (see chapter 4, Ridehailing). Similarly, elderly Georgians are the least likely to take advantage of online shopping (see chapter 4, Online Shopping).
Walking has a higher mode share for Georgians with mobility impairments than for those without; this counterintuitive finding is likely related to the fact that people with mobility impairments disproportionately find themselves in groups that walk more by necessity (lowincome, nondrivers, etc.) (see chapter 6, Travel Day Walking and Biking by Georgia Adults). Otherwise, walking and biking tend to decrease with age, though older adults make more leisure and loop trips than younger adults (see chapter 6, Travel Day Walking and Biking by Georgia Adults, and Model Structure).
In this chapter, the researchers provide more in-depth analysis of the travel patterns and immobility of people with mobility impairments (see chapter 5, Health and Disability) and consider how they intersect with gender differences (see chapter 5, How Much and What For: The Interrelated Effects of Gender and Age on Mobility and Trip Purpose). We find that among Georgians with mobility impairments, groups who are already marginalized for other reasons-- the poor, elderly, and women--are more likely to be immobile than Georgians with disabilities from other backgrounds. We further find that existing mobility services for these populations are inadequate.
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Choice of Equity Indicators The ultimate goal of increasing the equity of transportation is to improve Georgians' quality of life. Therefore, this report focuses on indicators that can best serve as proxies for quality of life in terms of access to destinations and time expended in order to access those destinations. From a utility perspective, the distance of each trip is less important than the existence of the trip and the time the traveler expends on it. As an equity indicator, travel distance (i.e., PMT and VMT) is less directly connected to the utility of that trip.
Accordingly, this report's key equity indicators are:
1. Travel day trips per capita, or the total number of trips divided by the total adults in the population or subpopulation.
2. Travel day trips per capita, active travelers only. This measurement, which is based only on adults with at least one trip on the travel day, indicates how many trips are made on days when members of a given group do not stay in the same place all day. Together, indicators 1 and 2 document the total average mobility, and differentiate between mobility differences based on a reduced number of trips on a typical travel day and those stemming from a reduced number of travel days.
3. Travel day immobility (zero trips on the travel day). This indicator captures differences in how often members of different groups stay at home for an entire day.
4. Chronic immobility (zero trips in the past 7 days). This indicator helps differentiate between respondents who happened to stay home on the travel day and those whose travel is severely restricted.
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The report also considers vehicle sufficiency (having enough vehicles for each potential driver in the household). Chapter 1, Vehicle Ownership has described demographic patterns in vehicle sufficiency at the household level. This chapter provides similar analysis for immigrants, who were not considered as a separate subpopulation in that section. The researchers then consider vehicle access within the household (identified by the primary driver for each vehicle), and vehicle access as a predictor of travel time. We are particularly interested in "captive" transit users, pedestrians, and cyclists.
KEY EQUITABLE MOBILITY INDICATORS
On an average day, 18.5 percent of Georgians make zero trips. However, over the course of a typical week, 97.6 percent of Georgia adults will leave home at least once. Not having made a single trip in the past 7 days, then, is an indicator of more chronic immobility. Georgians with mobility impairments are more than twice as likely as Georgians without mobility impairments to stay home on the travel day, but they are also six times as likely to be more chronically immobile.
While Georgians with mobility impairments are the most likely to have been immobile for the past 7 days, 7-day immobility is also comparatively high among low-income people, residents of carless and vehicle-deficit households, Georgians with low educational attainment, and women. Seven-day immobility is lower among white non-Hispanic and Latino Georgians, and higher among Georgians who identify as Black, Asian, or some other race. In chapter 5, How Much and What For: The Interrelated Effects of Gender and Age on Mobility and Trip Purpose, the researchers use logistic regression to examine the interrelated effects of gender, age, and various demographic characteristics on the risk of chronic immobility. For example, after controlling for
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other factors, being female is associated with an increased likelihood of being housebound, specifically among older adults.
Table 122 shows summary demographic statistics for Georgians, and for two subpopulations of interest: (1) Georgians with mobility impairments, and (2) immigrants. According to the U.S. Census Bureau, Georgia's median household income is $55,679. Since the NHTS income data are categorical, the closest boundary marker available is at $50,000. Based on the NHTS data analyzed in this report, 46 percent of Georgians live in households earning less than $50,000 per year. However, 74 percent of Georgians with disabilities live in households earning less than $50,000. Educational attainment and workforce participation are also lower. As a reminder, participants were asked about mobility impairments and not impairments that directly affect academic or professional functioning. It should therefore not be assumed that these differences stem from impaired scholastic or professional abilities.
Immigrants are less likely to reside in households earning less than $50,000 per year than nonimmigrants. As shown in table 123, this pattern is true for all immigrants without a 4-year college degree. Immigrants with a bachelor's degree or higher are somewhat likelier than nonimmigrants of the same education level to live in a lower-income household. There are several potential explanations for this. Some immigrants with professional certifications that are not recognized in the U.S. may find themselves working in a lower-paid industry. Additionally, many international students come to Georgia to study for advanced degrees, during which period they likely have lower incomes than their nonimmigrant counterparts with bachelor's or higher degrees, since many of the latter are already in regular employment whereas F-1 visa holders (the
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category of visa held by most international students) have restrictions on such employment.78 In view of F-2 visa restrictions preventing the spouses of international students from working, nonimmigrants in graduate school are also more likely than immigrant graduate students to live in households with at least one regularly employed worker. Low-education and high-education immigrants also hail from different parts of the world. The majority of immigrants with a high school education or less are Latino, while a plurality of higher education immigrants identify as Asian or some other race.
Table 124 shows differences in mobility by various demographic factors. On a typical day (averaging weekdays and weekends), 18.5 percent of Georgia adults stay home.79 This travel-day immobility is lowest in Atlanta and highest in non-MPO counties. Immobility decreases as education level increases. In terms of household income, immobility is highest among lowincome Georgians and lowest among those living in households earning $75,00099,999 per year. Vehicle-sufficient households have the lowest travel-day immobility. Interestingly, travelday immobility is higher among people in vehicle-deficit households than people in zero-vehicle houses. This may signify a pattern of consolidating trips into a smaller number of days for the former group: on active travel days, Georgians from vehicle-deficit households make more trips than Georgians from zero-vehicle households.
On an average day, 18.5 percent of Georgians make zero trips. However, over the course of a typical week, 97.6 percent of Georgia adults will leave home at least once. Not having made a single trip in the past 7 days, then, is an indicator of more chronic immobility. Georgians with
78 https://www.uscis.gov/working-united-states/students-and-exchange-visitors/students-and-employment. 79 For weekdays only, this figure is 16.1 percent.
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mobility impairments are more than twice as likely as Georgians without mobility impairments to stay home on the travel day, but they are also six times as likely to be more chronically immobile. While Georgians with mobility impairments are the most likely to have been immobile for the past 7 days, 7-day immobility is also comparatively high among low-income people, residents of carless and vehicle-deficit households, Georgians with low educational attainment, and women. Seven-day immobility is lower among white non-Hispanic and Latino Georgians, and higher among Georgians who identify as Black, Asian, or some other race. In chapter 5, How Much and What For: The Interrelated Effects of Gender and Age on Mobility and Trip Purpose, the researchers use logistic regression to examine the interrelated effects of gender, age, and various demographic characteristics on the risk of chronic immobility. For example, after controlling for other factors, being female is associated with an increased likelihood of being housebound, specifically among older adults.
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Table 122. Demographic characteristics by disability and national origin.

Population (thousands)* MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO Race White non-Hispanic only Black and Black multiracial
Hispanic (any race) Asian or other Education Level High school or less
Some college or associate degree

Mobility Impairment

National Origin

All Adults

Absent

Present

Nonimmigrant

7,704

6,967

732

6,771

Immigrant 927

54.1% 15.8% 10.1% 20.0%

55.1% 15.9% 10.3% 18.7%

44.6% 16.9%
9.2% 29.3%

52.0% 16.3% 10.7% 21.0%

73.5% 13.5%
5.9% 7.1%

55.2%
30.8% 8.5% 5.5%

53.6%
31.5% 9.2% 5.7%

51.4%
37.2% 7.0% 4.5%

57.9%
33.6% 5.8% 2.7%

15.2%
18.1% 36.1% 30.6%

31.8%

34.6%

57.5%

37.3%

31.5%

30.5%

28.7%

25.5%

29.3%

21.0%

Bachelor's degree or higher

37.7%

36.7%

17.0%

33.3%

47.4%

Annual Household Income

<$15,000

14.6%

12.3%

36.8%

15.0%

8.4%

$15,000 to $24,999

9.4%

9.3%

16.6%

10.0%

8.4%

$25,000 to $34,999

10.1%

10.1%

11.5%

9.7%

13.9%

$35,000 to $49,999

12.0%

12.0%

9.1%

11.7%

12.2%

$50,000 to $74,999

16.6%

16.6%

12.7%

16.4%

15.4%

$75,000 to $99,999

11.8%

12.2%

6.5%

11.4%

15.1%

$100,000+

25.5%

27.6%

6.8%

25.8%

26.7%

Household Vehicle Ownership

Zero-vehicle

4.9%

4.0%

16.0%

5.1%

3.9%

Vehicle-deficit

25.9%

25.3%

38.8%

25.1%

37.0%

Vehicle-sufficient

69.2%

70.7%

45.2%

69.8%

59.2%

Gender

Male

47.9%

49.2%

41.2%

48.9%

45.8%

Female

52.1%

50.8%

58.8%

51.1%

54.2%

Mobility Impairment

Absent

90.5%

-

-

89.9%

95.2%

Present

9.5%

-

-

10.1%

4.8%

National Origin

Nonimmigrant

88.0%

87.3%

94.0%

-

-

Immigrant

12.0%

12.7%

6.0%

-

-

* Unweighted sample size: 15,222. Mobility impairment is unknown for 8 respondents (weighted to represent 4,400 Georgians) and

national origin is unknown for 7 respondents (weighted to represent 6,000 Georgians).

Excluding Black Hispanic.

Vehicle-deficit households have at least one vehicle, but fewer vehicles than potential drivers. Vehicle-sufficient households have at least

one vehicle for every potential driver.

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Table 123. Demographics of nonimmigrant and immigrant Georgians by level of education.

Category Race
White non-Hispanic only Black and Black multiracial

Nonimmigrant

Low

Medium

High

HS or Less Some College Bachelor's

or Associate or Greater

Immigrant

Low

Medium

HS or Less Some College

or Associate

High Bachelor's or Greater

52.6% 39.5%

57.3% 34.7%

69.6% 24.7%

8.3% 15.2%

13.6% 28.0%

19.6% 16.6%

Hispanic (any race)

5.5%

5.5%

3.8%

58.4%

35.6%

22.5%

Asian or other Annual Household Income <$15,000

2.5% 27.3%

2.5% 12.5%

2.0% 3.6%

18.1% 16.0%

22.8% 5.7%

41.3% 5.0%

$15,000 to $24,999

13.6%

11.1%

4.0%

14.2%

8.5%

5.0%

$25,000 to $34,999

13.1%

9.8%

6.0%

19.1%

15.4%

7.5%

$35,000 to $49,999

11.2%

15.3%

9.4%

14.0%

15.9%

9.7%

$50,000 to $74,999

15.2%

19.3%

16.5%

12.9%

19.1%

15.0%

$75,000 to $99,999

7.0%

12.2%

15.0%

11.0%

14.6%

18.2%

$100,000+ Household Vehicle Ownership
Zero-vehicle

12.5% 10.2%

19.8% 3.4%

45.7% 0.8%

12.7% 4.8%

20.6% 1.5%

39.7% 4.1%

Vehicle-deficit

36.3%

24.5%

14.6%

48.0%

36.6%

30.2%

Vehicle-sufficient MPO Tier 1. Atlanta MPO

53.6% 43.3%

72.1% 49.3%

84.5% 64.0%

47.2% 66.0%

62.0% 73.0%

65.8% 77.9%

2. Medium MPOs

15.8%

17.4%

15.5%

11.5%

10.9%

15.9%

3. Small MPOs

12.0%

12.1%

7.9%

9.9%

6.8%

3.1%

4. Non-MPO Gender Male

28.9% 50.7%

21.3% 47.2%

12.7% 46.8%

12.6% 47.1%

9.3% 40.7%

3.0% 44.8%

Female Excluding Black Hispanic.

49.3%

52.8%

53.2%

52.9%

59.3%

55.2%

Vehicle-deficit households have at least one vehicle, but fewer vehicles than potential drivers. Vehicle-sufficient households have at least one vehicle for every potential driver.

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Table 124. Key mobility indicators for Georgia adults by geography, race, education, income, vehicle ownership, gender, and disability.

Daily Trips

Per Capita

Active*

Travelers Only

All adults

1.86

2.27

MPO Tier

1. Atlanta MPO

3.45

4.17

2. Medium MPOs

3.30

4.08

3. Small MPOs

3.36

4.11

4. Non-MPO

3.10

3.99

Race

White non-Hispanic only

3.38

4.10

Black and Black multiracial

3.34

4.17

Hispanic (any race)

3.39

4.14

Asian or other

2.95

3.90

Education Level

High school or less

2.84

3.82

Some college or associate

3.30

4.05

Bachelor's degree or higher

3.81

4.38

Annual Household Income

<$15,000

2.94

4.04

$15,000 to $24,999

3.04

3.90

$25,000 to $34,999

3.35

4.13

$35,000 to $49,999

3.55

4.27

$50,000 to $74,999

3.37

4.10

$75,000 to $99,999

3.56

4.11

$100,000+

3.55

4.20

Household Vehicle Ownership

Immobility

Travel

Past Seven

Day

Days

18.5%

2.4%

17.1% 19.2% 18.0% 21.9%

2.2% 2.5% 2.4% 2.7%

17.4%
19.7% 18.0% 24.1%

1.5%
3.6% 1.6% 6.2%

25.2% 18.6% 12.8%

3.7% 2.4% 1.2%

27.0% 22.1% 18.7% 16.7% 17.8% 13.4% 15.3%

3.9% 3.2% 2.0% 3.1% 2.9% 2.5% 0.6%

Zero-vehicle Vehicle-deficit Vehicle-sufficient Gender Male Female Mobility Impairment Absent Present

2.82

3.70

22.9%

5.0%

3.03

4.10

26.1%

4.8%

3.50

4.14

15.4%

1.3%

3.33

3.99

16.5%

1.8%

3.36

4.23

20.3%

2.9%

3.45

4.13

16.3%

1.6%

2.32

3.84

39.5%

10.0%

* Reporting at least one trip on the travel day.

Excluding Black Hispanic.

Vehicle-deficit households have at least one vehicle, but fewer vehicles than potential drivers.

Vehicle-sufficient households have at least one vehicle for every potential driver.

Table 125 compares mobility by educational attainment between immigrant and nonimmigrant Georgians. Table 126 shows what portion of Georgians are drivers and how many vehicles their
276

household owns, subdivided by national origin, race, and other demographic variables. This report classifies vehicle ownership status based on the ratio between household vehicles and potential drivers (household members ages 16+). A vehicle-deficit household has at least one vehicle, but fewer vehicles than potential drivers. A nondeficit (or vehicle-sufficient) household has at least one vehicle for each potential driver.
Table 125. Mobility indicators by national origin and educational attainment among Georgia adults.
Taken together, the two tables show that immigrant Georgians are somewhat less mobile than nonimmigrant Georgians at all educational levels. Immigrants of all education levels are less likely to be drivers than nonimmigrants. They are also less likely to live in vehicle-sufficient households and more likely to live in vehicle-deficit households. Because vehicle-deficit households are especially common among immigrants, low-education immigrants are also less likely than low-education nonimmigrants to live in carless households. High-education
277

immigrants, in contrast, are slightly more likely to be carless than their nonimmigrant counterparts.

Table 126. Driver status and vehicle ownership by Georgia adults of different demographic characteristics.

Vehicle Sufficiency*

Driver Status "Does this
person drive?"

Nondeficit At least one household vehicle for each potential
driver

Deficit At least one household vehicle, but fewer vehicles than potential
drivers

ZeroVehicle Carless
household

All Adults

89.0%

69.2%

25.9%

4.9%

Mobility Impairment

Absent

95.1%

82.2%

15.1%

2.7%

Present

67.2%

59.3%

28.2%

12.5%

National Origin

Nonimmigrant

92.5%

80.7%

15.5%

3.8%

Immigrant

87.8%

69.6%

27.2%

3.2%

Nonimmigrants by Education Level

High school or less

81.8%

66.4%

24.3%

9.3%

Some college or associate degree

95.2%

81.4%

15.9%

2.7%

Bachelor's or higher

97.9%

89.7%

9.4%

0.9%

Immigrants by Education Level

High school or less

73.6%

52.8%

41.3%

5.9%

Some college or associate degree

90.8%

76.4%

20.8%

2.8%

Bachelor's or higher

92.7%

74.2%

23.5%

2.3%

Race

White non-Hispanic only

94.9%

85.6%

12.9%

1.5%

Black and Black multiracial

84.6%

65.4%

23.9%

10.8%

Hispanic (any race)

89.1%

67.2%

28.5%

4.4%

Asian or other

88.4%

68.6%

27.1%

4.3%

Annual Household Income

<$35,000

82.4%

61.1%

26.9%

12.0%

$35,000 to $49,999

92.7%

77.1%

22.3%

0.5%

$50,000 to $74,999

95.3%

85.5%

13.9%

0.6%

$75,000 to $99,999

96.7%

88.1%

11.7%

0.1%

$100,000+

98.3%

93.2%

6.8%

0.1%

Sex

Male

93.4%

80.5%

16.2%

3.3%

Female

91.1%

79.1%

16.7%

4.1%

* Like income, vehicle sufficiency is calculated at the household level. The figures here summarize the status of adults within those

households. They do not indicate whether an individual is the main driver for any of the household vehicles.

This is the question wording used by NHTS. NHTS does not ask about driver's licensing.

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VEHICLE ACCESS
In chapter 5, Key Equitable Mobility Indicators, this report documented demographic differences in vehicle availability. This section examines the effects of vehicle access (or the lack thereof) on the travel time of "captive travelers" who take transit, walk, or bike. The researchers find that captive travelers are doubly penalized. First, transit and walking are, in most cases, slower than driving. Second, captive transit users receive an additional time penalty in comparison to similar trips by choice riders. Finally, we consider how households allocate vehicles among their members. In vehicle-deficit households, we find that women are less likely to be the main driver of a household vehicle than are men. This effect is reversed, however, for female caregivers of young children.
Captive Travel Conceptually, transit users, cyclists, or pedestrians are "captive" when they are traveling by that mode out of necessity because a private automobile is not available. A choice traveler, in contrast, has the option of taking a private auto, but opts not to do so. In this analysis, a captive transit or nonmotorized trip meets the following criteria:
1. The traveler is from a carless or vehicle-deficit household. The researchers include vehicle-deficit households in the definition of captive travel because, while a car is available to some household members for some trips, for other trips, household members will have more limited options.80
80 Additionally, in Georgia, vehicle-sufficient households are the norm. The overwhelming majority of Georgia households (74 percent) have a vehicle for every potential driver. Zero-vehicle households are very uncommon (7 percent). In a state or country where zero-vehicle and vehicle-deficit households are more common, it might make sense to subdivide captive travel from travelers in each of those types of households.
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2. The household earns less than $50,000 per year.81 The income criterion is designed to screen out travel by people who are "car-free" by choice versus those who are financially unable to afford vehicles for every potential driver in the household.
Table 127 presents the distribution of trip purposes by mode, distinguishing between choice and captive trips for the transit and NMT modes. Table 127 also presents the unweighted sample sizes for the trips on which all descriptive and statistical analyses in this section are based. As table 127 shows, the purposes of choice transit and NMT trips differ from the purposes of captive trips. A plurality of choice NMT trips are for recreation or fitness, or travel for its own sake. Captive pedestrians and cyclists, in contrast, are usually walking or biking because they are trying to reach a destination. On the transit side, trips to work account for nearly a third of choice trips, compared to just 22 percent of captive trips. Medical and dental trips account for 8 percent of captive transit trips versus just 1 percent of choice transit trips.
81 We chose this value because it is the closest approximation of Georgia's median income ($55,679) achievable with NHTS data. We conducted sensitivity analysis using different income cutoffs, and the models presented here were found to be robust.
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Table 127. Purpose by mode for captive, choice, and POV trips (weighted percent and unweighted sample sizes).

Purpose for Captive*, Choice, and POV Trips (weighted)

Purpose

NMT (walk or bike)

Transit

Other

Choice Captive Choice Captive

POV

Mode

All

Work or school

13.4%

6.4%

31.7%

22.1%

15.9%

20.0%

15.7%

Household-serving travel

9.3%

25.7%

24.0%

23.0%

29.0%

12.2%

27.6%

Recreation or fitness

40.9%

21.1%

5.7%

1.6%

4.4%

6.6%

6.9%

Other discretionary trip

12.1%

13.4%

4.1%

8.4%

14.5%

12.7%

14.2%

Medical or dental

0.3%

1.0%

1.0%

7.9%

1.6%

5.8%

1.6%

Return home

21.9%

31.0%

29.2%

35.5%

33.9%

36.8%

33.2%

Other purpose Unweighted Sample Sizes

2.1%

1.4%

4.2%

1.5%

0.7%

5.8%

0.9%

NMT (walk or bike)

Transit

Choice Captive Choice Captive

POV

Other Mode

Total

Work or school

306

55

106

65

6,736

89

7,357

Household-serving travel

270

247

48

85 13,214

53 13,917

Recreation or fitness

1,197

185

12

7

1,950

39

3,390

Other discretionary trip

300

122

17

31

6,591

37

7,098

Medical or dental

11

15

5

24

982

30

1,067

Return home

530

283

79

113 14,845

132 15,982

Other purpose

58

18

11

5

311

19

422

Total

2,672

925

278

330 44,629

399

* Transit and nonmotorized trips are considered captive if the traveler is from a vehicle-deficit or zero-vehicle household with an annual household income <$50,000. Trips by travelers that do not meet these criteria are categorized as choice. Based on sample of trips by Georgia residents that take place entirely within Georgia. Excludes 1,270 POV trips and 131 non-POV trips for which income is unknown, as well as 77 POV and 13 non-POV trips for which duration, purpose, or disability status are unknown.

Mean trip duration differs not just by mode, but between captive and choice users of the same mode (table 128). The difference is especially notable among transit users, where the mean travel time for a captive transit trip is 20 minutes longer than that of choice users. The average durations of captive and choice NMT trips are similar, largely because a preponderance of lengthy recreation trips by choice pedestrians and cyclists counterbalances the fact that captive

281

pedestrians' and cyclists' nonrecreational trips are longer than those of choice walkers and bikers.

Table 128. Mean duration and distance of captive, choice, and POV trips.

Mode Choice NMT (walk or bike)

Percent of Trips
5.5%

Mean Duration in Minutes

All

Recreation and

Purposes

Fitness Trips

18.3

26.4

Nonrec. Only

Mean Distance
(Miles)
Nonrec. Trips Only*

12.7

0.7

Captive NMT (walk or bike)

2.9%

19.2

23.3

18.1

0.8

Choice transit

1.1%

43.0

78.2

40.8

11.2

Captive transit

1.1%

63.4

60.6

63.4

9.7

POV incl. rental car

88.5%

21.9

26.4

21.7

9.1

Other mode

1.0%

31.8

21.7

32.5

12.6

* Recreational trips and fitness trips are excluded from distance because the NHTS's "shortest path" method of calculating

distance is expected to be less accurate for trips where the traveler is likely to choose a circuitous route. The distance column

also excludes 3 POV trips, 29 NMT trips, 3 transit trips, and 3 other trips for which distance is missing.

Transit and nonmotorized trips are considered captive if the traveler is from a vehicle-deficit or zero-vehicle household with an

annual household income <$50,000. Trips by travelers that do not meet these criteria are categorized as choice.

Because differences in duration between choice and captive travelers are confounded with differences in trip purpose and other factors, we used linear regression to isolate the effect of captivity on trip duration for transit and NMT trips (table 129). We find that captive transit users face a time penalty of 15.7 minutes per trip compared to choice transit users, after controlling for trip purpose, MPO, and demographic factors. Captive pedestrians and cyclists do not face the same captivity time penalty as captive transit users. Rather, the longer average duration of captive NMT trips is better explained by the dominance of purposes that are associated with longer walk or cycle trips.

282

Table 129. Linear regressions: Effect of captive travel on duration of transit and nonmotorized trips.
283

Table 130 shows an alternate estimation of the time penalty on captive transit trips in comparison not just with choice transit trips, but also with trips by POV. The model controls for the same purpose and demographic variables, and additionally controls for trip distance. This model estimates that a trip by public transit will take approximately 10 minutes longer than a comparable trip by POV (or 17.7 minutes in Atlanta). The time penalty for captive transit trips is an additional 20.2 minutes. In other words, the full time penalty for a captive transit user instead of using a POV if it had been available is 38 minutes per trip in Atlanta and 30 minutes per trip elsewhere in the state. A traveler who chooses transit for two trips a day is accepting an average increase of 2035 minutes in travel time, perhaps in exchange for increased convenience, lower cost, or other benefits (see chapter 1, Transit Service Preferences Among Workers). Captive transit users, on the other hand, lose more than an hour of their day, regardless of their mode preferences.
284

Table 130. Linear regression: Duration in minutes of trips by POV and transit. 285

Intra-Household Vehicle Allocation: Who Gets the Family Car? Not every household member has equal access to the family car. As shown in table 133, overall, 90 percent of Georgia drivers (ages 16+) are listed as the main driver for at least one family vehicle (in other words, it is "their") car. In vehicle-deficit households, only 70 percent of drivers have their own car, versus 97.5 percent of drivers in nondeficit households.
When vehicles are scarce, to whom do households allocate them? We examine this question by using logistic regression to model the probability that a driver ages 16+ will be the main driver of a household vehicle in three circumstances:
1. Among households with a vehicle deficit (where multiple drivers must compete for a limited number of vehicles), we model the probability that a driver is listed as the main driver for any household vehicle.
2. Among households that acquired a vehicle in the past 12 months, we model the probability that a household member will be listed as the main driver for a newly purchased vehicle (whether that vehicle was new or used at the time of purchase).
3. Among households with at least two vehicles, we model the probability of being the main driver for the vehicle with the most recent model year (i.e., the newest vehicle chronologically).
Table 131 and table 132 present the weighted and unweighted sample distributions on which these models are based. While the researchers do not present a model based on drivers in all households with 2+ vehicles (deficit and nondeficit combined), we present descriptive statistics about this population. Accordingly, we also provide a sample distribution for this group.
286

Table 131. Weighted sample characteristics of drivers by household vehicle ownership.

Driver Characteristics

Household Type

2+ Potential Drivers Vehicle-Deficit

2+ Household Vehicles

Newly Purchased Vehicle(s)

All drivers ages 16+ Male Female By Caregiver Status

100.0% 51.1% 48.9%

100.0% 47.3% 52.7%

100.0% 52.4% 47.6%

100.0% 50.9% 49.1%

Male noncaregiver Female noncaregiver Male caregiver, youngest 04 Female caregiver, youngest 04 Male caregiver, youngest 515 Female caregiver, youngest 515

33.5% 30.5%
9.1% 9.1% 8.5% 9.2%

31.7% 33.3%
8.4% 9.1% 7.2% 10.4%

33.9% 29.6%
9.3% 9.1% 9.2% 8.9%

32.4% 28.7%
9.5% 10.6%
9.0% 9.8%

Age Teen (1617) Working-age adult (1864) Senior (6579) Elderly (80+)

2.4% 84.1% 11.9%
1.6%

3.0% 84.3% 10.7%
1.9%

1.9% 85.3% 11.5%
1.3%

3.9% 87.3%
8.0% 0.8%

Worker Status Nonworker Worker

34.4% 65.6%

43.5% 56.5%

30.4% 69.6%

33.9% 66.1%

Mobility Impairment Absent Present

94.2% 5.8%

90.8% 9.2%

90.8% 9.2%

95.1% 4.9%

Vehicle Sufficiency

Nondeficit, 2+ potential drivers

73.6%

0.0%

87.8%

78.3%

Deficit of 1+ vehicles

26.4%

100.0%

12.2%

21.7%

Deficit of exactly 1 vehicle

19.6%

74.3%

9.6%

15.3%

Deficit of 2+ vehicles

6.8%

25.7%

2.6%

6.4%

Number of Household Vehicles 1

13.8%

52.3%

0.0%

54.6%

2

43.6%

31.4%

54.8%

54.1%

3+

42.6%

16.3%

41.5%

43.4%

Unweighted sample size

11,042

1,954

9,513

3,532

Note: Weighted column percentages shown. For consistency between models and descriptive tables, 348 observations with missing

household income have been excluded from this table.

Based on drivers in households with two or more members of driving age (16+).

Vehicle-deficit households have at least one vehicle, but fewer vehicles than potential drivers.

Household has purchased a new or used vehicle within past 12 months.

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household

with a child of 515 years old.

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Table 132. Unweighted sample characteristics of drivers by household vehicle ownership.

Driver Characteristics

2+ Potential Drivers

Household Type

Vehicle-Deficit

2+ Household Vehicles

Newly-Purchased Vehicle(s)

All drivers ages 16+ Male Female By Caregiver Status

11,042 48.3% 51.7%

1,954 45.1% 54.9%

9,513 49.5% 50.5%

3,532 47.9% 52.1%

Male noncaregiver Female noncaregiver Male caregiver, youngest 04 Female caregiver, youngest 04 Male caregiver, youngest 515 Female caregiver, youngest 515 Age Teen (1617) Working-age adult (1864) Senior (6579) Elderly (80+) Worker Status Nonworker Worker Mobility Impairment Absent Present Vehicle Sufficiency Nondeficit, 2+ potential drivers Deficit of 1+ vehicles
Deficit of exactly 1 vehicle Deficit of 2+ vehicles Number of Household Vehicles 1 2 3+

36.5% 38.1%
5.8% 6.6% 6.0% 7.1%
1.9% 73.3% 21.7%
3.2%
41.3% 58.7%
93.6% 6.4%
82.3% 17.7% 14.6%
3.1% 0.0% 10.3% 48.4% 41.4%

33.7% 39.4%
5.5% 6.9% 5.8% 8.6%
3.0% 73.6% 18.9%
4.5%
51.9% 48.1%
88.4% 11.6%
0.0% 100.0%
82.6% 17.4%
0.0% 58.0% 29.4% 12.5%

37.1% 36.8%
6.0% 6.7% 6.4% 7.0%
1.5% 74.7% 21.2%
2.6%
37.9% 62.2%
95.1% 4.9%
93.1% 6.9% 5.7% 1.2% 0.0% 0.0%
53.4% 46.6%

35.0% 36.3%
6.5% 7.8% 6.5% 8.0%
3.3% 78.6% 16.3%
1.8%
37.4% 62.6%
94.3% 5.8%
85.6% 14.4% 11.4%
3.0% 0.0% 5.8% 38.8% 55.4%

Note: Unweighted column percentages shown. For consistency between models and descriptive tables, 348 observations with missing

household income have been excluded from this table.

Based on drivers in households with two or more members of driving age (16+).

Vehicle-deficit households have at least one vehicle, but fewer vehicles than potential drivers. Household has purchased a new or used vehicle within past 12 months. A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household

with a child of 515 years old.

To understand vehicle access, the research team examined the percentage of drivers who are listed as the "main driver" for a household vehicle (table 133). Because household members may share access to a vehicle, not having a vehicle that is officially "theirs" does not necessarily

288

mean a driver never has vehicle access. However, someone who is the main driver for a vehicle likely has more reliable access than someone who is not. Unsurprisingly, the larger the vehicle deficit is in any given household, the less likely individual drivers are to have their own vehicle. Among households with at least two drivers, 98 percent of drivers in nondeficit households are the main driver of a household vehicle. In contrast, 74 percent of drivers in a household with a one-vehicle deficit are listed as a main vehicle driver, and only 56 percent of those in a household with a deficit of two or more vehicles. On average, women are less likely than men to be the main driver of a household vehicle, though this pattern reverses among caregivers for young children. Nonworkers and people with mobility impairments are less likely to be main drivers than workers and nondisabled household members, respectively. Analysis using logistic regression indicates that these patterns continue to be significant after controlling for other factors (table 134).
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Table 133. Percent of drivers listed as the "main" driver for a household vehicle.

Personal Vehicle Access:

Main Driver of a Household Vehicle*

All Households with

2+ Potential Drivers

Vehicle-Deficit Households Only

Nondeficit Households Only

All drivers ages 16+
Male
Female By Caregiver Status

90.1% 91.4% 88.8%

69.6% 70.5% 68.8%

97.5% 98.1% 96.8%

Male noncaregiver Female noncaregiver Male caregiver, youngest 04 Female caregiver, youngest 04

90.6% 87.3% 91.1% 90.3%

69.5% 64.5% 67.8% 72.3%

97.6% 96.6% 98.5% 96.6%

Male caregiver, youngest 515

94.6%

77.6%

99.5%

Female caregiver, youngest 515

92.2%

79.4%

97.6%

Age Teen (1617)

65.8%

18.0%

89.5%

Working-age adult (1864)

91.1%

71.6%

98.0%

Senior (6579)

89.4%

67.5%

96.2%

Elderly (80+)

82.8%

72.3%

87.7%

Worker Status Nonworker

81.6%

55.7%

94.6%

Worker

94.6%

80.2%

98.8%

Mobility Impairment Absent

90.9%

70.8%

97.8%

Present

77.5%

57.7%

91.4%

Vehicle Sufficiency Nondeficit, 2+ potential drivers

97.5%

-

97.5%

Deficit of 1+ vehicles

69.6%

69.6%

-

Deficit of exactly 1 vehicle

74.3%

74.3%

-

Deficit of 2+ vehicles

56.0%

56.0%

-

Unweighted sample size

11,042

1,954

9,088

* Participant is listed as the "main driver" for one or more household vehicles.

Based on drivers in households with two or more members of driving age (16+). For consistency between descriptive

analysis and models, households with missing income values have been excluded from this table.

Vehicle-deficit households have at least one vehicle, but fewer vehicles than potential drivers.

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in

a household with a child of 515 years old.

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Table 134. Logistic regression: Vehicle allocation within vehicle-deficit households.

Logistic Regression: Probability of Being Main Driver for Any Household Vehicle

Odds Robust

Ratio

SE

T

P-Value

Female

0.633

0.087

Caregiver status by age of youngest child (reference: noncaregiver)

-3.32

0.001 ***

Caregiver, youngest child 04 years old

0.543

0.135

-2.45

0.014 **

Caregiver, youngest child 515

0.971

0.252

-0.11

0.911

Female x Caregiver, youngest child 04

2.655

0.993

2.61

0.009 ***

Female x Caregiver, youngest child 515

1.928

0.702

1.80

0.071 *

Age Age2
Annual household income (reference: <$35,000)

1.097 0.0155 0.999 0.000141

6.59 -5.09

0.000 *** 0.000 ***

$35,000 to $49,999

0.986

0.109

-0.13

0.896

$50,000 to $74,999

0.912

0.105

-0.80

0.422

$75,000 to $99,999

1.119

0.183

0.68

0.494

$100,000+

1.056

0.149

0.38

0.702

Worker

3.050

0.393

8.66

0.000 ***

Mobility impairment Deficit of 2+ vehicles (versus deficit of exactly 1)

0.687 0.532

0.114 0.057

-2.25 -5.84

0.024 ** 0.000 ***

Constant

0.161

0.052

-5.69

0.000 ***

Model Indicators

Number of cases, N

1,954

Final log likelihood, LL()

-1,078

Log likelihood of constants-only model, LL(C) McFadden's Pseudo-R2: 1-LL()/LL(C)

-1,198 0.0999

Sample: drivers age 16+ in vehicle-deficit households (households with at least one vehicle, but fewer vehicles than

potential drivers).

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in

a household with a child of 515 years old.

Deficit size is the number of potential drivers in a household minus the number of vehicles.

A related question concerns the quality of household vehicles, which we examine by looking at the newest household vehicle. We consider vehicles that were the newest chronologically (most recent model year) and also vehicles that were newly purchased, whether new or used at time of purchase. As shown in table 135, women are less likely to be recipients of a newly purchased

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vehicle, but, especially for caregivers, more likely to have the newest vehicle by model year. Logistic regression indicates that these patterns hold even after controlling for worker status, mobility impairment, and other factors (table 136).

Table 135. Percent of drivers who are listed as main driver for newest household vehicle.

By Model Year

Newly Purchased

All drivers ages 16+ Male Female By Caregiver Status
Male noncaregiver Female noncaregiver Male caregiver, youngest 04 Female caregiver, youngest 04

48.1% 41.1% 55.9%
42.2% 52.3% 38.3% 60.9%

48.1% 49.5% 46.6%
49.9% 43.1% 51.8% 51.7%

Male caregiver, youngest 515 Female caregiver, youngest 515 Age Teen (1617) Working-age adult (1864) Senior (6579) Elderly (80+)

39.8% 62.6%
23.5% 48.2% 51.1% 55.0%

46.0% 51.6%
43.3% 48.4% 48.9% 34.9%

Worker Status Nonworker

48.1%

43.6%

Worker

48.1%

50.5%

Mobility Impairment Absent

48.2%

48.5%

Present

47.3%

40.2%

Number of Household Vehicles 1

-

54.6%

2

54.8%

54.1%

3+

41.5%

43.4%

Unweighted sample size

9,513

3,532

* For consistency between descriptive analysis and models, households with missing income values

have been excluded from this table. Sample: drivers age 16+ in household with 2+ vehicles and 2+ potential drivers. In case of a tie, both

vehicles are coded as newest.

Vehicles purchased in the past 12 months, whether purchased new or used. Based on sample of drivers

age 16+ in household with newly purchased vehicle(s).

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household with a child of 515 years old.

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Table 136. Logistic regression: Intra-household allocation of new vehicles.

Newly Purchased

By Model Year

Odds Ratio

P-Value Odds Ratio

P-Value

Female

0.692 0.000 ***

Caregiver status by age of youngest child (reference: noncaregiver)

1.623 0.000 ***

Caregiver, youngest child 04 years old

0.876 0.383

0.838 0.063 *

Caregiver, youngest child 515

0.701 0.015 **

0.813 0.023 **

Female x Caregiver, youngest child 04

1.420 0.155

1.823 0.000 ***

Female x Caregiver, youngest child 515

1.908 0.005 ***

1.653 0.002 ***

Age Age2
Annual household income (reference: <$35,000)

1.003 1.000

0.750 0.992

1.044 0.9997

0.000 *** 0.000 ***

$35,000 to $49,999

0.986 0.856

0.961 0.362

$50,000 to $74,999

1.036 0.609

0.877 0.000 ***

$75,000 to $99,999

0.954 0.525

0.843 0.000 ***

$100,000+

0.925 0.233

0.819 0.000 ***

Worker

1.242 0.006 ***

1.145 0.006 ***

Mobility impairment

0.672 0.010 **

Number of household vehicles (reference: exactly one vehicle)

0.839 0.071 *

Exactly two

0.769 0.013 **

-

-

Three or more

0.554 0.000 ***

Number of household vehicles (reference: exactly two vehicles)

-

-

Three or more

-

-

0.690 0.000 ***

Constant

1.431 0.166

0.269 0.000 ***

Model Indicators

Number of cases, N

3,352

9,513

Final log likelihood, LL()

-2411.30

-6366.71

Log likelihood of constants-only model, LL(C) McFadden's Pseudo-R2: 1-LL()/LL(C)

-2448.19 0.0151

-6590.44 0.0339

Vehicles purchased in the past 12 months, whether purchased new or used. Sample: drivers age 16+ in household with newly

purchased vehicle(s).

Sample: drivers age 16+ in household with 2+ vehicles and 2+ potential drivers. In case of a tie, both vehicles are coded as

newest.

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a

household with a child of 515 years old.

An examination of teen drivers' vehicles specifically indicates that gender affects vehicle access for the newest drivers. As shown in table 137, male teen drivers are slightly more likely to have their own car than female teen drivers. However, teen girls are slightly more likely to have a
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newly purchased car, and more than three times as likely to have the newest vehicle in the house by model year. Teen girls, it seems, are provided with cars that are in better condition than are teen boys, whether because they are considered more trustworthy drivers, because of greater concern about their safety in case of mechanical difficulties, or both.

Table 137. Percent of teen drivers who are the main driver for household vehicles, by sex.

Teen Drivers only

All

Boys

Girls

Any vehicle

64.9%

65.4%

64.6%

Newest by model year

15.0%

6.3%

21.6%

Newly-purchased

24.4%

22.4%

25.9%

Sample: all drivers ages 16-17 in households with at least one vehicle

HOW MUCH AND WHAT FOR: THE INTERRELATED EFFECTS OF GENDER AND AGE ON MOBILITY AND TRIP PURPOSE
Gender profoundly influences how and why people travel. However, the effects are not the same across all ages and life stages. How can researchers best understand the interrelated effects of gender and age? Working-age women have more complex tripmaking patterns than men, and are disproportionately responsible for household-serving travel (trips made to maintain the household or its members) (Taylor, Ralph, and Smart 2015). Mothers are more likely than fathers to be responsible for transporting children to school and elsewhere, affecting day-to-day mode choice and long-term career and residential location decisions (Gimenez-Nadal and Molina 2016, Jun and Kwon 2015). Elderly women are likely to give up driving sooner than men, and suffer disproportionately from being immobile or housebound (Loukaitou-Sideris and Wachs 2018). All of these effects can reduce women's quality of life, but many of them remain hidden if researchers do not incorporate age, gender, and the interactions between the two into their analyses.

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This section examines trends in mobility and trip purpose by gender and age using binary and multivariate probit (MVP) models to analyze travel diary data. The researchers model: (1) the likelihood of being housebound (having made no trips outside the home within the past 7 days) and, (2) among nonhousebound respondents, the likelihood of having made trips of various purposes on the travel day. The MVP structure facilitates efficient modeling of the "simultaneous but separate" choices (Choo and Mokhtarian 2008, p. 147) to make or not make various types of trips in a single day.
The section begins by briefly discussing planning for women's needs and the importance of additionally considering the divergent needs of women of different ages. We then discuss the methods used; in particular, we address why an MVP model is useful for analyzing trip purpose.
The effects of gender on mobility and trip purpose are found to be substantial, but strongly agedependent. All else equal, young women are not substantially more likely to be chronically housebound than young men. Among older adults, however, being female is associated with an increased likelihood of being housebound, ranging from 2.3 percentage points for seniors ages 7579 to 8 percentage points for ages 85+, even after controlling for disability. This finding suggests that if being female were not associated with an elevated risk of becoming housebound, there would be 26,800 fewer housebound seniors in Georgia.
The findings on trip purpose show a consistent pattern. With one exception (trips for dining), among younger adults, being female is associated with an increased likelihood of making every kind of trip. With no exceptions, among older adults, being female is associated with a decreased likelihood of making every kind of trip measured. These findings underscore how the loss of
295

mobility reduces older women's ability to attend social and recreational activities, maintain their household, and even seek medical care.
As for younger women and men, we find that equal mobility does not imply equal utility. Working-age women are disproportionately responsible for household-serving travel, and the extra responsibilities for transporting family members fall more heavily on mothers and other female caregivers. This finding holds even after controlling for employment. Further, the age at which men's tripmaking eclipses women's is much younger for trips that benefit the individual traveler (i.e., leisure) than for household-serving trips (i.e., errands, transporting others).
The final discussion in the section is the policy implications. It is vital to facilitate older women's mobility. For younger women who are already mobile, it may be more important to focus on providing a high level of service for the household-serving trips for which women are disproportionately responsible, and to facilitate access to recreational and fitness opportunities. This analysis is a reminder that women's travel needs are not constant at every stage of life.
Background There is a substantial body of evidence that crafting transportation policy without taking gender into account results in worse outcomes for women (Loukaitou-Sideris 2016, Fainstein and Servon 2005). On average, women's travel behavior and vulnerabilities differ from those of men. In the U.S., the gender gap in household-serving and child-serving travel has persisted, even as gaps in labor force participation and overall mobility have narrowed (Taylor, Ralph, and Smart 2015, Craig and van Tienoven 2019). In part because of this disproportionate responsibility for household labor and childcare, women's commutes tend to be shorter than men's, but their
296

journeys are more complex and involve more trip chaining (McQuaid and Chen 2012, Loukaitou-Sideris 2016).
While traditionally, and currently in some developing countries, women have been less mobile than men (Loukaitou-Sideris 2016), many American women today are constrained by an abundance of household- and child-serving trips at the expense of other uses of their time and travel (Loukaitou-Sideris 2016, Craig and van Tienoven 2019); Hanson (2010) refers to this conundrum as hypermobility. Transportation planning centered around travel directly between home and workplace, a pattern more typical of men, disadvantages women who may need to drop off a child on the way to work and run a series of errands on the way home.
Researchers and practitioners have argued that women's needs are best served when a gendered lens is applied to policymaking (Fainstein and Servon 2005). While progress has been uneven (Loukaitou-Sideris and Fink 2009), many organizations now incorporate gender into transportation planning and policymaking. However, women's needs are not monolithic. Just as planning for the needs of an "average" traveler often neglects the needs of women, planning for the needs of the "average" woman can disadvantage women who are also poor, disabled, or members of a racial minority.
Age strongly affects women's travel behavior and needs. All of the trends described here are true of American women "on average."82 As this report will show, none of these trends are true of elderly women. While there is an increasing amount of literature on older adults, substantially fewer studies explicitly focus on gender. While common to note that women are more affected
82 This includes those studies that discuss "women's needs" but did not include older adults.
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because more seniors are women, it is significantly less common to examine how being female affects older women's travel behavior in comparison to that of older men.
Much transportation research on older adults has focused on driving behavior. Among the oldest seniors, driver licensing rates are lower among women than among men; this is not true of younger senior cohorts (Loukaitou-Sideris and Wachs 2018). For licensed drivers, researchers have focused on safety risks and the self-imposed limitations older drivers may place on their driving. Studies have consistently found that women are more likely to self-limit than men, and do so at younger ages (see Wong et al. 2016 for a review). Advocates, and an increasing number of researchers, have commented on the shortsightedness of a focus on helping seniors know when to stop driving without providing adequate transportation alternatives once they do so (Loukaitou-Sideris and Wachs 2018, Wong et al. 2016). Many U.S. seniors live in areas with anemic public transit coverage, and where transit exists, it may be physically and/or cognitively difficult for elderly riders to navigate. The resulting isolation adversely impacts seniors' mental and physical health (Decker 2006).
Gender, then, shapes the travel behavior and quality of life of working-age adults and seniors. Even female children are given less leeway to travel independently than male children (McDonald 2012). To meet women's transportation needs across an entire lifespan, it is critical to understand the separate and interconnected effects of gender and age on travel behavior.
Methods Table 138 shows descriptive statistics about the sample. Unweighted data are used for the models of the likelihood of being housebound, and of trip purpose; in general terms, models do
298

not require representative data to identify empirical relationships. Unless otherwise stated, the NHTS's person-weights are applied to descriptive statistics and average marginal effects.

Table 138. Sample characteristics.

Category
Female Mean age Adult 1864 Senior (ages 6579) Elderly (ages 80+) Number of Trips on Travel Day
0 12 35 6+ Out of country (N=57)* Chronically housebound (no trips within past seven days) Full-time worker Part-time worker Nonworker Unknown worker status (N=294) Mobility impairment absent Mobility impairment present Unknown disability status (N=8) Caregiver for child(ren) ages 015 Driver Race: white non-Hispanic Race: Black, Black multiracial, or Black Hispanic Race: other Annual Household Income $0 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+ Unknown (N=3,393)
Table continues on next page.

All Adults (N= 15,222) 55.0% 53.2 70.8% 23.7% 5.5%

Unweighted Men
(N=6,845)
53.0 71.0% 24.0%
5.1%

16.9% 24.6% 37.6% 20.5%
0.4%

14.7% 27.1% 38.0% 19.9%
0.4%

2.1% 42.3% 10.2% 45.5%
1.9% 89.2% 10.7%
0.1% 21.4%
92.1% 69.9%

1.6% 50.5%
8.5% 39.1%
1.9% 90.8%
9.1%
0.0% 20.7%
93.4% 73.0%

22.2% 7.9%

19.3% 7.7%

19.0% 9.3%
12.2% 17.5% 13.2% 25.4%
3.3%

15.8% 8.7%
12.1% 17.9% 14.3% 28.2%
3.0%

Women (N=8,377)
53.3 70.6% 23.5%
5.9%
18.7% 22.6% 37.3% 21.1%
0.4%
2.4% 35.7% 11.6% 50.8%
1.9% 87.9% 12.0%
0.1% 22.0% 91.1% 67.3%
24.6% 8.1%
21.6% 9.8%
12.3% 17.2% 12.3% 23.2%
3.6%

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Table 138. (Continued).

Continued from previous page.

Category

Weighted (Non-missing Only)

All Adults

Men

Women

Female

52.1%

Mean age

46.1

45.1

46.9

Adult 1864

83.1%

84.8%

81.6%

Senior (ages 6579)

13.7%

13.0%

14.5%

Elderly (ages 80+)

3.1%

2.3%

3.9%

Number of Trips on Travel Day

0

18.4%

16.4%

20.2%

12

26.2%

29.1%

23.5%

35

36.5%

36.5%

36.5%

6+ Chronically housebound (no trips within past 7 days)

18.9% 2.4%

18.0% 1.8%

19.8% 2.9%

Full-time worker

48.7%

58.3%

39.9%

Part-time worker

11.9%

9.9%

13.7%

Nonworker

39.4%

31.8%

46.4%

Mobility impairment absent

90.5%

92.2%

Mobility impairment present Caregiver for child(ren) ages 015
Driver

9.5% 31.8% 89.0%

7.8% 30.1% 90.5%

11.1% 33.3% 87.6%

Race: white non-Hispanic

55.2%

59.5%

51.3%

Race: Black, Black multiracial, or Black Hispanic

31.6%

27.2%

35.7%

Race: other

13.1%

13.3%

13.0%

Annual Household Income

$0 to $24,999

24.0%

20.7%

27.0%

$25,000 to $34,999

10.1%

9.5%

10.8%

$35,000 to $49,999

12.0%

12.2%

11.8%

$50,000 to $74,999

16.6%

16.7%

16.4%

$75,000 to $99,999

11.8%

12.9%

10.9%

$100,000+

25.5%

28.1%

23.1%

* NHTS automatically records a trip total of zero for participants who were out of the country.

For the purposes of this paper, it is more useful to treat these observations as missing data.

Unknown here includes any kind of missing data, including "Don't Know," refusal to answer, and

left blank. A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old,

and any adult age 22+ in a household with a child of 515 years old.

The question asked by the NHTS is "Do you/does this person drive?" Driver rates in the data

decrease among both male and female seniors; some seniors categorized as nondrivers likely

have driver's licenses.

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Seventeen percent of adults reported making zero trips on their travel day. Travel patterns vary over the course of the week, so it would be incorrect to assume all those respondents are housebound. We define "chronically housebound" as having made no trips outside the home within the past 7 days; this describes 2.4 percent of all Georgians and 8.7 percent of elderly or disabled residents.83
Table 139 shows annual per-capita rates of tripmaking by purpose, gender, and age. These descriptive statistics show clear gender differences in tripmaking patterns in every age group, but the form of those differences varies by age. Among the elderly population, women are significantly less mobile than men, making only two thirds as many trips. This mobility disadvantage is not present among working-age women. In fact, working-age women travel slightly more than working-age men (an extra 1.2 trips per week).
Even though working-age men and women make a similar quantity of trips, there are evident differences in the purposes of those trips. On average, over the course of a week, a working-age man will make 1.4 more work or school trips than a working-age woman, while a working-age woman will make an extra 2.2 household-serving trips.
These patterns are obviously entangled with broader demographic trends, such as women's lower participation in the labor market. To clarify the roles of gender and age, a holistic modeling approach is needed. This report uses a two-stage process to model mobility and trip purpose.
83 Excluding residents of institutions such as nursing homes, who were not included in the sample.
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Table 139. Annual trips per capita by purpose, gender, and age (weighted).

Working-Age

Senior

Elderly

Trip Purpose

Men Women Men Women 1864 1864 6579 6579

Men 80+

Women 80+

All purposes

1,223 1,285 1,196 1,062

948

636

Mandatory Travel

256

182

83

41

28

4

Work commute

215

149

73

35

26

1

Other work-related travel

18

15

8

4

2

1

Attend school or daycare

23

18

1

2

2

Household-Serving Travel

286

399

405

399

345

242

Transport someone

72

116

61

53

23

28

Shopping or errands

203

260

318

312

284

187

Medical/dental services

11

23

26

33

37

26

Discretionary Travel

262

275

306

259

256

175

Social/recreational

130

136

139

119

133

85

Dining

103

100

117

86

74

45

Community/religious

29

38

49

53

49

45

Other Travel Return home

419

429

403

362

320

215

407

414

389

355

312

209

Other

13

15

14

7

7

7

Based on all adults (N=15,222). Estimates are weighted using NHTS's trip-weights, which are annualized

versions of the person-weights.

We analyze mobility by using binary probit to model the likelihood that a person is chronically housebound (i.e., zero trips outside the home in the past 7 days). We chose to model chronic immobility rather than number of trips because the NHTS diary covers a single day for each respondent. The models therefore need to distinguish between respondents who completed their diaries on a day they happened to stay home and respondents who reported no trips because their ability to travel is more chronically impaired. We exclude people who were out of the country, as well as people missing either worker or disability status.84

84 Income was the only variable with numerous missing values. To avoid compromising the representativeness of the sample by discarding these observations, a dummy variable was created for missing income. Where applicable, NHTS-imputed values for race, sex, and age are used.
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The second model is a multivariate discrete choice (probit) model of the likelihood of having made at least one trip for each of five common trip purposes: transporting someone else, shopping/errands, medical/dental, social/recreational/fitness, and dining. Multivariate probit allows for the simultaneous inclusion of multiple dependent variables. It is useful for any situation where there are multiple binary outcomes to be modeled, and where those outcomes are separate but not necessarily independent. The model is based on the same sample as the binary model, but excludes those who are chronically housebound.
Although work is a common trip purpose, the research team opted not to include it in this analysis because the overwhelming determinant of whether or not people make work trips is their worker status. Worker status is instead included as a control variable, allowing us to assess whether different rates of household-serving travel persist even between men and women with the same employment status. Trips to return home are also not included because they are essentially the complement of having made any other trip.
To capture the separate and interrelated effects of gender and age on travel, the models incorporate multiple interaction terms between gender and age, and between gender and other relevant factors. While this provides more nuanced findings, the large number of interaction terms makes discerning the effects of gender directly from model coefficients more complicated. This report therefore provides visual representations of the average marginal effect of gender by age group and discusses joint significance of interaction terms.
Analysis for this project was conducted in Stata 15 software (StataCorp 2017). The MVP model was estimated using the mvProbit program created by Cappellari and Jenkins (2003).
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Results Table 140 shows the results of the first model: a binary probit model of the likelihood of being housebound. For both men and women, one of the strongest predictors of being housebound is disability. On average, having a disability is associated with an increase of 4.6 percentage points in the probability of being housebound; the average marginal effect (AME)85 of having a disability is even larger for seniors (7.6 percentage points, versus 4.0 points for adults younger than 65). However, the model shows that even after controlling for disability, gender affects the risk of being housebound. When the interactions between gender and age are accounted for, gender per se is not significant. Rather, gender's effects are captured by the interaction terms between gender and age, which are jointly significant (=.05). The combined effect of gender and age is depicted in figure 29.
85 To control for gender-related differences in the explanatory variables, AMEs are calculated here by predicting the probability of being housebound as if the whole sample had a mobility impairment, predicting the probability as if the whole sample did not have a mobility impairment, and subtracting the former from the latter. AMEs by age are the average of respondents from a given age category.
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Table 140. Binomial probit model of likelihood of being chronically housebound (having made no trips within the past 7 days).

Covariate

Coefficient P-Value

Female

0.251

0.514

Age Age2

-0.0220 0.00019

0.088 * 0.126

Female x Age Female x Age2

-0.0161 0.000213

0.291 0.129

Worker

-0.346

<0.001 ***

Mobility impairment, adult ages 1864

0.666

<0.001 ***

Mobility impairment, adult ages 65+

0.774

<0.001 ***

Vehicle Ownership Category (Reference: Vehicle-Nondeficit Household with 2+ Drivers)

Single-driver vehicle-nondeficit

-0.174

0.051 *

Vehicle-deficit

0.404

<0.001 ***

Zero-vehicle

0.141

0.233

Race (reference: white non-Hispanic) Black

0.267

<0.001 ***

Other

0.293

0.003 ***

Built Environment Type (Reference: Urban/Second City)

Rural

0.115

0.164

Small town

0.0135

0.862

Suburban

0.0102

0.908

Annual Household Income (Reference: <$25,000)

$25,000 to $34,999

-0.152

0.157

$35,000 to $49,999

-0.00525

0.956

$50,000 to $74,999

0.00719

0.940

$75,000 to $99,999

0.0108

0.923

$100,000+

-0.390

0.001 ***

Unknown (missing)

-0.0699

0.628

Constant

-1.773

0.000 ***

Number of cases, N

15,155

Final log likelihood, LL()

-1194.2

Initial log likelihood LL(0) Pseudo-R2: 1-LL()/LL(0)

-1477.6 0.192

* Denotes significance for = .10 ** Denotes significance for = .05 *** Denotes significance for = .01 The test of joint significance of female x age and female x age 2 returned a P-value of .020, indicating that they are jointly

significant for = .05. The P-value for a joint significance test of female, female x age, and female x age 2 was .006. Vehicle-deficit households have at least one car, but fewer cars than potential drivers. Includes Black Hispanic and Black multiracial.

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Figure 29. Histogram. Average marginal effect of being female on probability of being housebound, by age group (percentage points).
All else equal, young women are not substantially more likely to be chronically housebound than young men; the average marginal effect (AME)86 of gender among working-age adults is essentially zero. However, it begins to increase in late middle age. Among seniors ages 7579, being female is associated with an increase of 2.3 percentage points in the probability of being housebound. The AME for seniors in their early 80s is 4.1 percentage points. At age 85, being female is associated with an increase of 8 percentage points. The AME of gender and age is in addition to the effects of age alone; elderly women's total probability of being housebound is 14.5 percent, more than double that of elderly men (figure 30). To put these numbers in perspective, if being an older woman were not associated with an elevated risk of being housebound even after controlling for disability, there would be 26,800 fewer housebound seniors in Georgia.
86 To control for gender-related differences in the explanatory variables, AMEs are calculated here by predicting the probability of being housebound as if the whole sample were male, predicting the probability as if the whole sample were female, and subtracting the former from the latter. AMEs by age are the average of respondents from a given age category. Five-year age groups are used to ensure that each estimate is based on an average over at least 300 respondents.
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Figure 30. Histogram. Probability of being housebound by gender and age. Trip Purpose Table 141 shows results of the multivariate probit model of trip purpose. Before describing findings, this section briefly assesses the strength of the model as a whole and presents McFadden's pseudo-R2 statistics based on several different benchmarks (Mokhtarian 2016) (table 142). The juxtaposition of a relatively low 2(MS) with higher 2(EL) and 2(NC) indicates that compared to a model with only constant terms, the full model represents a modest improvement in the researchers' ability to predict what trips a person will have made and a larger increase in our power to explain the factors that contribute to the observed trip patterns. The 2(EL) of 0.380 is considered very good, especially for a multivariate model with 64 (=26) possible outcomes.
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Table 141. Multivariate probit model of likelihood of making trips for various purposes.

Covariate Female

Community/ Religious
-0.040

Transport Other
-0.330

Shopping/ Errands
0.235

Medical/ Dental
0.405

Age

0.0156 *

0.0236 *** 0.0401 *** 0.0268 ***

Age2

-0.000068

-0.000209 *** -0.000295 *** -0.000087

Female x Age
Female x Age2 Mobility-impaired non-senior

-0.001277 -0.000004 -0.194 **

0.0153 -0.000204 ** -0.149 **

-0.000213 -0.000081 -0.0569

-0.00725 -0.000017 0.497 ***

Driver

0.064

0.390 *** 0.441 *** -0.0102

Female x Driver

0.246 *

0.245

0.0954

0.230 *

Full-time worker

-0.305 *** -0.118 *** -0.452 *** -0.456 ***

Part-time worker

0.002

0.0859 * -0.234 *** -0.319 ***

Caregiver for Child <16 Years Old* (Reference: Noncaregiver)

Caregiver

-

-

-0.0322

0.0915

Female x Caregiver

-

-

-0.00338

0.00631

Caregiver Status by Age of Youngest Child in Years (Reference: Noncaregiver)

Youngest child 04

0.197 ** 0.723 ***

-

-

Youngest child 515

0.0652

0.796 ***

-

-

Female x Youngest 04

-0.0607

0.196 **

-

-

Female x Youngest 515

-0.0336

0.122 *

-

-

Vehicle Ownership Category (Reference: Vehicle-Nondeficit Household with 2+ Drivers)

Single-driver vehicle-nondeficit

0.0904 *

0.0696

0.291 *** 0.0313

Vehicle-deficit

-0.0253

0.0511

0.0324

0.00898

Zero-vehicle

0.142

-0.163

0.413 *** 0.168 *

Race (Reference: White Non-Hispanic)

Black

0.0827 *

0.0737 *

0.054 *

0.108 **

Other

0.0506

-0.004

0.008

0.053

Table continues on next page.

Social/ Recreational -0.189 0.00903 -0.000096 * 0.00228 -0.000061 -0.273 *** 0.118 0.206 ** -0.426 *** -0.225 ***
-0.102 ** 0.00592
- - - -
0.292 *** -0.0400
0.213 ***
-0.160 *** 0.027

Dining -0.164 0.00347 -0.00003 0.0109 -0.000138 ** -0.0936 0.241 *** -0.0211 -0.0432 0.0447
-0.0354 -0.0889 *
- - - -
0.149 *** -0.107 ** -0.0147
-0.140 *** -0.238 ***

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Table 141. (Continued). 309

Table 142. Summary statistics and indicators for multivariate probit model.

Indicator

Number of cases, N

Final log likelihood, LL()

Log likelihood of constants-only (market share) model, LL(MS)

Equally likely benchmark, LL(0)

Log likelihood without constants, LL(NC) McFadden's Pseudo-R2 Measures

Market share benchmark,

2 (MS)

:

1-LL()/LL(MS)

Equally-likely benchmark,

2 (EL)

:

1-LL()/LL(EL)

No-constant benchmark,

2 (NC)

:

1-LL()/LL(NC)

Share of Cases Making Each Type of Trip

Community or religious

Transport other

Shopping/errands

Medical/dental

Social, recreational, or fitness

Dining Correlation Between Error Terms for Outcomes

21

0.1302

31

0.0741

32

0.1637

41

-0.0087

42

0.0825

43

0.1384

51

0.0382

52

0.1348

53

0.1703

54

-0.0282

61

0.1454

62

0.2109

63

0.3011

64

0.1851

65

0.1803

14,854 -38323.0 -40562.7 -61776.0 -38497.8
0.055 0.380 0.377
8.6% 13.0% 42.7%
6.8% 27.9% 25.6%
P-value 0.000 *** 0.000 *** 0.000 *** 0.803 0.002 *** 0.000 *** 0.080 * 0.000 *** 0.000 *** 0.236 0.000 *** 0.000 *** 0.000 *** 0.000 *** 0.000 ***

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As with the model of immobility, the researchers find that gender significantly affects trip purpose, but its effects are dependent on age. The gender/age interactions are jointly significant (=.01) for all trip purposes except community/religious trips.87 There are additional interactions between gender and being a parent or other caregiver. Parenthood is associated with an increase in the likelihood of making a trip to transport someone else, but the increase is larger for female caregivers (e.g., mothers and grandmothers) than it is for fathers and other male caregivers. Female caregiving is also associated with a disproportionate decrease in dining trips. Using the same methods as for the immobility model, figure 31 shows the average marginal effect of being female for each outcome, across the entire sample and incorporating all interactions. The AME of being female on the likelihood of making each type of trip is relatively modest, ranging from an increase of about 4 percentage points in the probability of making a trip for transporting someone or shopping, to an approximately 1 percentage point decrease in the likelihood of making a trip for dining or socialization/recreation.
87 For community/religious trips, gender is significant when all interaction terms are jointly tested (=.01).
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N=14,854 Figure 31. Bar graph. Average marginal effect of gender on probability of making trips of
various purposes on travel day (in percentage points).
However, these apparently modest effects are deceptive. Because the size and, more importantly, direction of the effect of being female varies by age, a single average marginal effect understates the degree to which gender affects tripmaking.
Figure 32 provides a visual representation of the effect of gender across different ages,88 underscoring the dramatically different effect gender may have on the trip patterns of younger and older adults. For example, while the overall AME of gender on the probability of making trips to shop or run errands is +4.0 percentage points, the gender AME by age ranges from +9.2 percentage points for the youngest adults to -12.3 for the oldest, a range of 21.5 percentage points. The smallest range of effects is 3.5 percentage points (for community/religious trips), and the average is 11.1 percentage points.89
88 Five-year age groups are used rather than individual years to ensure that each estimate is based on an average over at least 300 respondents.
89 The two trip types with the smallest marginal effect, community/religious and medical/dental, were the least common at the outset. The fact that the effects are presented as percentage points limits how much values that are already close to zero can be reduced.
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Figure 32. Histograms. Average marginal effects of gender by age (in percentage points). There is a consistent pattern: for younger adults, being female is associated with an increased likelihood of making a trip of that type, but at some point the trend reverses; among seniors and
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the elderly, being female is associated with a decreased likelihood. The sole exception to this is dining, where women are already slightly less likely than men to eat out when young, and become increasingly less likely to do so as they age. Trips to transport others are highest during the years when many people are responsible for the care of a child who is not old enough to drive. However, this caregiver increase is higher for female caregivers than for male caregivers (figure 33).
Figure 33. 3D histogram. Predicted probability of making a trip to transport others by gender and age.
Another notable variation is the age at which being female switches from being associated with an increased likelihood to being associated with a decreased likelihood. For household-serving travel (i.e., transport others, shopping/errands, medical/dental), this transition occurs somewhere between ages 65 and 75, depending on the specific purpose. For social and recreational travel,
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this switch occurs much earlier, at age 45.90 The latest transition is for community/religious trips, which also shows the smallest decrease. This suggests that as older women are less able to make trips, they are likely to highly prioritize community and religious excursions.
Gender also has significant interactions with driver status. Driving increases the likelihood of making trips for several purposes. For women, this increase is greater, though the interaction terms themselves are not universally significant.
Discussion While gender clearly influences both the likelihood of being housebound and the purposes of the trips a person makes, these results suggest that there is no monolithic effect of being female. Rather, the effect is strongly mediated by age, and for some purposes, by parenthood and ability to drive. Other studies have documented how the effects of gender are also influenced by travelers' race, income, and other demographic factors (Loukaitou-Sideris 2016).
Failing to attend to the interactions between gender and age can obscure troubling gendered inequalities. The fact that women are, on average, more likely to make shopping trips than men is likely of little comfort to an elderly woman who is waiting to fill a prescription until her adult daughter is able to give her a ride to the pharmacy. That daughter may be making just as many trips as men her age; however, if her travel is disproportionately devoted to chauffeuring family members and doing errands, she may have less time to socialize or go out to eat, and as a result derive fewer personal benefits from her travel.
90 The AME of being female on dining trips is always zero or negative.
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A simple gendered lens can help craft transportation policy that does not disadvantage the average woman. To attend to the needs of all women, a compound gendered lens that clarifies the layered effects of gender, age, and other sources of inequality is needed. The following subsections discuss some of the key findings by age group.
Working-Age Women: Equal Mobility, Unequal Utility Historically in the U.S. and currently in some developing cities, women were or are less mobile than men (Loukaitou-Sideris 2016). This study finds no significant discrepancy in the quantity of trips made by working-age men and women in Georgia. There are, however, gendered differences in the purposes of those trips and, therefore, we would argue, in the personal benefit that men and women derive from their tripmaking.
In particular, women are disproportionately likely to engage in household-serving travel, even after controlling for worker status. For Georgians who become parents, the burden of transporting their new family falls more heavily on mothers than on fathers. In contrast, the differences between men and women for trip purposes that directly benefit the traveler (i.e., socialization, recreation, dining) are much smaller. This is consistent with findings about hypermobility, suggesting that despite their high levels of mobility, working-age women may derive less utility from those trips (Craig and van Tienoven 2019).
From a policy standpoint, the coexistence of equal mobility and unequal utility is a classic "wicked" problem (Rittel and Webber 1973). Gendered inequality in trip purpose is likely a symptom of other gendered inequalities, such as distributions of household labor, expectations of parents, and, for purposes like dining out, income. Given that transportation professionals have few tools for addressing these structural social forces, how can these planners nevertheless
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improve transportation for working-age women? One method is to focus on providing a high level of service for the types of trips that women disproportionately make, particularly household-serving travel.
Public transit is a good example of a system that can inadvertently provide a lower level of service to women because their trip purposes and patterns differ from men's.91 Radial public transit networks, which are designed to transport workers to and from a city center, are often poorly suited for suburb-to-suburb commuting and the more complex trip patterns required to run errands and drop off or pick up children on the way to or from work. Additionally, if riders are required to pay a new fare for each stop they make, the cost of doing errands can rise quickly. Making sure transit routes adequately serve shopping and residential areas and providing free transfers can make transit more useful for household-serving travel, thereby disproportionately benefiting women.
Similarly, household-serving travel, especially transporting children, causes many women to spend long hours driving, leaving limited time for leisure and exercise. Active transport such as bicycling can do "double duty" for completing responsibilities but simultaneously enjoying exercise. Since unsafe traffic conditions are known to particularly deter female would-be cyclists (Emond, Tang, and Handy 2009), improving bicycle infrastructure could result in fewer women stuck behind the steering wheel.
91 As a side note, transit may provide equal mobility but unequal utility in another way: sexual assault during transit trips does not always stop women from riding transit, but it drastically increases the emotional and psychological cost (Kash 2019).
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Transportation professionals could also improve travel for women who are caregivers by improving travel for students. For example, the need to drop children off at school makes it difficult for working parents (disproportionately, working mothers) to walk or bike for their commute. Improving school transportation may also free up students' parents.
Older Women Face a Crisis of Immobility Mobility decreases with age for both men and women, but for women, the effect is much stronger. Elderly men make 77 percent as many trips as working-age men, but elderly women make just half as many trips as working-age women. Even after excluding people who live in a supportive care facility, 13 percent of elderly women (ages 80+) are chronically housebound, more than triple the rate among elderly men (3.6 percent).
For every trip purpose measured, there is an age past which women are less likely to make that trip than are men of the same age. However, men's likelihood of making leisure trips eclipses women's in middle age, several decades before they eclipse women's rates of household-serving trips.
Given that women are documented to utilize medical services at higher rates than men, it is striking that among older adults, being female is associated with a decreased likelihood of making medical trips. Being a driver increases the likelihood of making medical trips for women, but has no significant effect for men. This suggests that women make fewer medical trips not because they have no need of a doctor, but because they have no means of reaching the doctor's office.
The only trip purpose for which there is not a large gender discrepancy among older adults is community and religious activities. This finding may indicate that older women especially value
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trips to stay connected to community and church, but may also reflect a greater availability of transportation; these trips take place primarily on weekends, when relatives are more likely to be available to provide a ride. Additionally, many churches provide free transportation to congregants in need.
The results of this study indicate that the most pressing transportation need for older women is simply more transportation. Rates of driving are lower among senior women than senior men. Often, seniors who stop driving are left with few alternatives to replace their private auto; even where public transit networks are robust, the systems are difficult to navigate for riders who gave up driving due to physical (or cognitive) limitations (Decker 2006).
Many social service organizations that serve seniors provide shuttle service to take advantage of programming. Transportation to nonorganizational destinations may be more difficult to come by. Taxi service is not affordable for many seniors. Utilization of ridehailing services such as Uber and Lyft is relatively low among seniors; thus, this category may present an opportunity for improving senior mobility (Shirgaokar 2018). In fact, Fulton County (which includes Atlanta) recently began offering subsidized ridehailing for elderly residents.92
A Change of Pace: Women in Late Middle Age and Younger Seniors This report has discussed hypermobility among young women and immobility among the elderly. Middle-aged women and young seniors are at a point of transitioning between the two states of mobility. Is it possible to help middle-aged women and young seniors maintain their mobility and avoid the challenges facing the cohort before them? As men retire from the
92 For more information, visit the website for Fulton County Senior Services: https://www.fultoncountyga.gov/services/senior-services/transportation.
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workforce, their overall travel declines, but there is some substitution of leisure and household trips for work trips. Curiously, women's leisure trips decline around the same age many children become more independent or leave home, and decline further after retirement. Are there policies that might encourage women to engage in more recreational travel as their childcare and employment responsibilities diminish?
Limitations and Directions for Future Research The principal limitation of this study is that it is cross-sectional rather than longitudinal. The primary advantage of a cross-sectional approach is, of course, that the data are much more readily attainable.93 In a cross-sectional study, it is important to ask whether the observed effects that this study has attributed to age may, in fact, be related to cohort (generation). After all, when today's 18-year-old reaches her 80th birthday, the world will be rather different than it is today. However, gendered differences in trip purpose are evident even among the youngest of respondents, and studies have found that gendered divisions of household labor have diminished only slightly over recent decades (Crane 2007, Loukaitou-Sideris 2016). Though the research team believes the effects we have uncovered are predominantly age effects, they are not inevitable. Transportation professionals, in other words, may be able to prevent the epidemic of immobility currently afflicting older adults from reaching today's young women.
However, regardless of whether these same issues will face older women decades from now, there is a clear policy need for better transportation for today's seniors. Future research can provide guidance for evidence-based practice. For example, how could ridehailing services help
93 While the NHTS has been repeated a number of times, it uses a unique pool of respondents each time. Additionally, question structures and sampling methods have changed substantially over the years.
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more older adults? Is it simply a question of increasing older adults' comfort with technology? Or are there aspects of the service that are unfriendly to elderly adults who may have physical or cognitive limitations?
Gender identifiably affects the travel behavior of adults as young as 18, raising the question of when this differentiation begins. Therefore, it would also be valuable to analyze gendered differences in trip purpose and independent mobility among children and adolescents. Finally, just as it is important to pay attention to the intersection of age and gender, more research is needed on other factors that interact with gender. For instance, parenthood affects low-income women and single mothers differently than it does women with working partners.
A Compound Gendered Lens While gender strongly influences travel behavior, there is no such thing as a monolithic "women's travel needs." This study has found that the magnitude and direction of the effects of gender vary with age. Examining gender without age therefore produces an incomplete, and in some cases misleading, portrait of the challenges facing women. In particular, hypermobility among working-aged women can obscure the crisis of immobility that leaves many older women isolated in their own homes.
To attend to the needs of all women, researchers need a compound gendered lens, where they account for the layered effects of gender, age, and other sources of inequality. Methodologically, these findings underscore the importance of incorporating gender and age into analysis, not only separately, but also jointly. From a policy standpoint, this study's results suggest a need for interventions aimed at improving two distinct transportation problems facing women of different ages. Elderly women, who no longer feel comfortable driving or can no longer navigate public
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transit, urgently need viable replacements to improve their mobility. Working-age women, in contrast, face equal mobility but unequal utility compared to working-age men. In the face of persistent gender differences in household division of labor, we argue that planners should work to ensure that the level of service for the household-serving trips generally allocated to women is not lower than for the simpler work journeys that have been the traditional focus of planning. Additionally, modes that can do double duty as transportation and exercise (such as cycling) should also be made more friendly to women. The specific policies needed to promote equitable transportation vary by age group, but this study reveals one constant: a gendered lens is critical when planning for the needs of travelers of every age.
HEALTH AND DISABILITY
This section examines equity concerns related to disability status, health, and physical activity. It first investigates mobility differences by age and health among Georgians with and without a mobility impairment. Even among Georgians with no mobility impairment, the researchers find a strong link between low income and poor health, and indications that captive walking trips among the lowest-income Georgians are not a public health panacea. The report then turns to a more detailed analysis comparing different subgroups of Georgians with disabilities.
In the context of this analysis, a mobility impairment is a medical condition that interferes with travel. While it is sometimes used interchangeably with disability, it does not include disabilities that have no direct effect on travel (as defined by the participants). It is also important to note that the NHTS sample excludes institutionalized populations. The disabilities being analyzed here are, therefore, not severe enough to require institutionalization. Given that this most infirm
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population has already been screened out, the differences in mobility between Georgians with and without mobility impairments are striking. Georgians with mobility impairments face many challenges. Some of these challenges are related directly to the presence of a mobility impairment, but many are not. For example, figure 34 compares the annual household incomes of Georgians with and without mobility impairments. Georgians with mobility impairments are likely to be impoverished. Thirty-seven percent have an annual household income of less than $15,000, putting them near or below the poverty line regardless of household size. Only 12 percent of adults without mobility impairments fall in this category. Georgians with mobility impairments are overrepresented in all income groups under $35,000 per year and underrepresented in all income groups above that point. Regardless of the cause of this discrepancy, low-income people with disabilities have fewer resources to address the mobility challenges that they face.
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Figure 34. Stacked bar graph. Annual household income of Georgians with and without mobility impairments.
Health and Physical Activity among Disabled and Nondisabled Georgians A mobility impairment is not synonymous with being elderly or infirm. As shown in table 143, a plurality of Georgians with mobility impairments are in good health (42 percent) and the majority are younger than age 65 (60 percent).94 However, these shares are far smaller among Georgians with mobility impairments than they are for nondisabled Georgians (93 percent and 86 percent, respectively).
94 Table 143 also contains information about the unweighted and weighted distribution of the sample used for table 144 to table 149.
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Table 143. Health and age of Georgia adults with and without mobility impairments.

All adults Health
Good, very good, or excellent Fair health Poor health Age Working age (1864) Senior (6579) Elderly (80+)

Population (Weighted)

Sample (Unweighted)

With Mobility Impairment

Without Mobility Impairment

With Mobility Impairment

Without Mobility Impairment

723,241 6,930,155

1,609

13,487

41.5% 37.3% 21.1%
59.8% 27.8% 12.4%

92.9% 6.4% 0.7%
85.7% 12.2%
2.1%

43.1% 37.1% 19.8%
47.8% 33.4% 18.8%

92.5% 6.7% 0.7%
73.7% 22.4%
3.9%

As shown in table 144, 37 percent of Georgians with a mobility impairment live in very lowincome households (those with an annual income of less than $15,000), triple the rate for nondisabled adults. The difference in the share of low-income residents between disabled and nondisabled Georgians is actually largest among the young and those in good health. Among working-age Georgians with a mobility impairment, 44 percent live in a very low-income household, versus just 12 percent of nondisabled adults of the same age. The only subpopulation where disability is not linked to an increased likelihood of living in a low-income household is among Georgians in poor health; more than 40 percent of Georgians in poor health live in the lowest-income households, regardless of the presence or absence of a mobility impairment.

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Table 144. Driver status and income of Georgians with and without mobility impairments, by health and age.

With Mobility Impairment

Very Low

Income

Driver*

(<$15,000)

Without Mobility Impairment

Very Low

Income

Driver*

(<$15,000)

All Adults Health

61.6%

36.7%

91.9%

12.1%

Good, very good, or excellent Fair health Poor health Age

63.7% 68.4% 36.2%

63.4% 62.9% 55.8%

92.7% 83.7% 62.7%

10.6% 30.8% 42.8%

Working age (1864)

63.7%

44.3%

92.1%

12.3%

Senior (6579)

68.4%

25.1%

93.5%

10.2%

Elderly (80+)

36.2%

26.3%

75.9%

16.6%

Note: Within each impairment group (with and without), the numbers represent the share of those possessing the row

characteristic who also possess the column characteristic. For example, among those with mobility impairment, 63.7% of

those in good health are drivers.

* NHTS uses the following question wording: "Do you/does this person drive?"

Based on annual household income. Income data are missing for 54 respondents with disabilities (3.4% unweighted) and

421 respondents without disabilities (3.1% unweighted). Statistics are based on nonmissing observations.

There is a strong link between income and health among all ages and levels of ability (table 145). On average, 12 percent of Georgians describe themselves as being in fair or poor health.95 However, for low-income Georgians, this figure is 30 percent, compared to fewer than 4 percent of high-income Georgians. The association between poverty and worse health persists for disabled and nondisabled Georgians, both working-age adults and seniors.

95 This report combines fair and poor as measures of being less healthy because just 0.7 percent of nondisabled Georgians described themselves as being in poor health, compared to 21.1 percent of Georgians with a mobility impairment. When focusing specifically on Georgians with mobility impairments, we disaggregate fair and poor health.
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Table 145. Georgians in poor or fair health by income, age, and disability.

All Adults (Ages 18+)

Working Age (1864) Older Adults (65+)

Annual Household

Income

All

Non-

Non-

Non-

disabled* Disabled disabled Disabled disabled Disabled

All Adults

11.9%

7.1%

58.5%

6.3%

54.0%

11.8%

65.1%

<$15,000

30.5%

18.6%

68.0%

30.5%

67.6%

28.1%

69.2%

$15,000 to $24,999

20.3%

11.7%

60.6%

20.3%

47.3%

14.7%

75.6%

$25,000 to $34,999

13.1%

8.1%

53.1%

13.1%

42.8%

10.9%

64.6%

$35,000 to $49,999

10.2%

6.6%

58.1%

10.2%

54.9%

11.7%

60.5%

$50,000 to $74,999

9.2%

5.7%

56.7%

9.2%

50.0%

9.0%

63.1%

$75,000 to $99,999

5.6%

3.5%

44.4%

5.6%

44.8%

8.2%

43.8%

$100,000+

3.7%

2.8%

36.8%

3.7%

26.2%

6.6%

53.7%

* No mobility impairment.

Note: Health is based on self-assessment with response options poor, fair, good, very good, and excellent.

Further, the association between low income and poor health persists even when comparing adults with the same level of physical activity (figure 35). Among Georgians who are physically inactive, 22 percent of adults from the lowest-income households are in fair or poor health, compared to just 15 percent of inactive adults from households earning more than $15,000 per year. Among adults who engage in vigorous physical activity, the percentage of adults in the lowest-income households who consider themselves to be in fair or poor health is 10 times that of adults from other income brackets (11 percent versus 1.1 percent).

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Figure 35. Bar graph. Health of nondisabled Georgia adults by income and level of physical activity in a typical week.
On average, nondisabled adults in poor health walk more than those in better health (table 146), likely due to the strong correlation between poor health and poverty. This fact is a reminder that while promoting walking and cycling is an important public health intervention, other policies are needed to support the health of Georgians who are already walking because they have no choice.
A majority of both disabled and nondisabled Georgians report making at least one walk trip in the past 7 days (table 146). On average, nondisabled Georgians are more likely than their mobility-impaired counterparts to report at least one walk trip (73 percent versus 60 percent), and are more likely to be physically active (88 percent versus 65 percent). Among working-age Georgians and those in good health, the difference between disabled and nondisabled Georgians with respect to walking is relatively small. In fact, working-age Georgians with disabilities walk
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slightly more on average than nondisabled working-age Georgians (6.3 average trips versus 5.8 trips), though a somewhat smaller percentage of them report at least one walking trip. The gap in overall physical activity is somewhat larger, however. The same pattern exists among Georgians in good health. Disability is most strongly associated with decreased physical activity and walking among Georgians who are older or in poor health. Mobility Differences between Georgians with and without Mobility Impairments Georgians with mobility impairments are more likely than nondisabled Georgians to be immobile on the travel day, over the past few days, and over the past week (table 147). On a typical day, 4 in 10 Georgians with mobility impairments will not leave the house, compared to 16 percent of nondisabled Georgians. One out of 10 disabled Georgians reports having made no trips in the past 7 days, compared to 1.6 percent of nondisabled Georgians. Mobility impaired Georgians also report fewer trips per capita, and on active travel days (table 148). Unlike differences in physical activity, these gaps persist among the young and the healthy. This suggests that many obstacles to mobility for Georgians with disabilities are not related to health or physical capability.
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Table 146. Walking and physical activity among Georgians with and without mobility impairments, by health and age.

With Mobility Impairment

At Least One Number of

Walk Trip

Walk Trips

(Past 7 Days)

Physically Active*

Without Mobility Impairment

At Least One Number of

Walk Trip

Walk Trips

(Past 7 Days)

Physically Active*

All adults

60.1%

5.01

64.8%

73.4%

5.81

87.5%

Health

Good, very good, or excellent

70.5%

5.93

75.6%

74.1%

5.81

89.0%

Fair health

55.8%

3.95

65.0%

64.5%

5.46

68.4%

Poor health

47.1%

5.07

43.4%

67.2%

8.84

67.5%

Age

Working age (1864)

70.2%

6.26

69.3%

73.4%

5.78

87.0%

Senior (6579)

48.1%

3.67

62.4%

74.8%

6.07

90.6%

Elderly (80+)

37.8%

1.97

48.7%

67.7%

5.35

91.3%

Note: Within each impairment group (with and without) percentages represent the share of those possessing the row characteristic who also

possess the column characteristic. For example, 70.5 percent of people with mobility impairments who are in good health made at least one

walk trip in the past 30 days, versus 74.1 percent of people in good health who do not have a mobility impairment.

* In a typical week, respondent engages in some light, moderate, or vigorous physical activity.

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Table 147. Immobility by disability, health, and age.

With Mobility Impairment

Without Mobility Impairment

Travel Day

Past Few Days

Past Seven Days

Travel Day

Past Few Days

Past Seven Days

All adults Health

39.5% 15.8% 10.0% 16.3%

2.7%

1.6%

Good, very good, or excellent health Fair health Poor health Age

33.8% 41.7% 46.7%

8.3% 16.8% 28.8%

4.4% 10.3% 20.6%

15.6% 22.8% 41.9%

2.4% 5.6% 16.6%

1.4% 4.2% 6.8%

Working age (1864) Senior (6579) Elderly (80+)

34.1% 41.9% 60.1%

13.0% 16.2% 29.0%

8.4% 8.2% 22.2%

14.8% 23.6% 33.8%

2.4% 4.2% 6.1%

1.5% 1.8% 2.7%

Note: Immobility refers to zero reported trips during the specified timeframe.

Respondents with zero trips on the travel day were asked about the date of their most recent trip. Response options included 1. "The day before," 2. "A few days before," 3. "A week before," 4. "More than a week but within a month," and 5. "More than a month." We consider respondents to be immobile for the past few days if they selected response 3, 4, or 5 and immobile for the past week if they selected response 4 or 5. A respondent who has been immobile for the past week has also been immobile for the past few days and on the travel day.

Table 148. Average daily trips by disability, health, and age.

With Mobility Impairment

Without Mobility Impairment

Per Capita

All adults

2.33

Health

Good, very good, or

excellent health

2.53

Fair health

2.21

Poor health

2.14

Age

Working age (1864)

2.57

Senior (6579)

2.28

Elderly (80+)

1.28

* Reporting at least one trip on the travel day

Active Travelers Only*
3.85
3.82 3.79 4.02
3.89 3.92 3.20

Per Capita 3.46
3.50 3.08 2.34
3.51 3.30 2.55

Active Travelers Only* 4.13
4.14 3.99 4.03
4.12 4.31 3.85

A number of behavioral adaptations take place as mobility becomes more difficult. The NHTS asked about such adaptations of all respondents age 80 and older, and of younger individuals who reported mobility limitations. Seventy percent of Georgians with mobility impairments
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reported reducing day-to-day travel (table 149). This figure was 77 percent for elderly disabled Georgians, versus just 31 percent for elderly nondisabled Georgians.

Table 149. Behavioral adaptations among disabled and older adults.

Behavioral Adaptation

With Mobility Impairment

Working

All Adults Age Seniors Elderly

18+

(1864) (6579) (80+)

Without Mobility Impairment
Elderly (80+)

Reduced day-to-day travel

69.6% 67.5% 70.8% 76.8%

30.9%

Asked others for rides

39.9% 44.1% 31.0% 38.5%

20.1%

Limited driving to daytime

26.7% 24.8% 31.5% 22.9%

34.9%

Given up driving altogether

24.9% 18.4% 27.0% 52.0%

15.8%

Used the bus or subway less frequently

8.1%

9.8%

5.8%

5.0%

4.0%

Used special transportation services such as Dial-A-Ride

9.8% 11.3%

7.5%

7.0%

0.5%

Used a reduced fare taxi

1.9%

1.4%

2.1%

3.7%

0.1%

Note: Values shown are percentage of people in each age group (column) who reported engaging in the listed behavior

(row).

Compared to nondisabled elderly Georgians, disabled elderly Georgians are also more likely to ask others for rides and use reduced-fare taxis and special transit services. They are slightly more likely to reduce their usage of regular public transit. More than half of elderly Georgians with a mobility impairment have given up driving, versus just 15.8 percent of those without. Elderly Georgians without a mobility impairment were instead more likely to limit driving to daytime (34.9 percent).
Children with Mobility Impairments Among Georgia children ages 517, 2.2 percent have a mobility impairment (table 150). Half of these children were immobile on the travel day, versus 22 percent of children without a mobility impairment. However, the sample contains just 42 children with a mobility impairment, and thus
332

the statistics presented here should be interpreted with caution. Nevertheless, the high frequency of immobility among disabled children is indicative of a mobility disadvantage that might benefit from more targeted data collection and study.

Table 150. Comparison of children (ages 517) with and without mobility impairments.

Demographics for Children Ages 517
Mobility impairment present Mobility impairment absent

Percent (weighted)
2.2% 97.8%

Mobility Aids (children with mobility impairments)

None

86.4%

W heelchair

12.2%

Vision aid (white cane)

0.6%

Other (not specified)

0.8%

Immobility Among Children Ages 517 (Weighted)

Mobility impairment present Mobility impairment absent

Immobile on Travel Day 50.5% 22.0%

Sample Size (unweighted)
42 2,416
32 6 2 2
Immobile for Past Seven Days 7.1% 2.1%

Variations among Adults with Mobility Impairments Table 151 shows demographic characteristics of Georgia's 723,000 residents with mobility impairments.96 One out of 20 mobility impairments are short term (having lasted fewer than 6 months).97 Women account for 61 percent of people with mobility impairments. This is not

96 Table 151 also contains sample information for table 152 to table 154. 97 We considered eliminating or separating those with short-term disabilities from the analysis of those with longer-term limitations. However, short-term is not synonymous with temporary: an unknown share of disabilities that are less than 6 months old may, in fact, simply be newly occurring long-term disabilities. In view of this fact, plus the relatively small share of short-term disabilities in the sample (5 percent), we chose to retain all cases of mobility limitation for analysis. Short-term disability is included as a covariate in the regressions.
333

fully accounted for by women's longer life expectancies; disability is also more prevalent among working-age women compared to working-age men.
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Table 151. Demographic characteristics of Georgians with mobility impairments.

Population*

Sample* (unweighted)

All adults with mobility impairment

723,241

1,609

Mobility Aid Usage None Wheelchair (incl. wheelchair and other) Cane, walker, or other
Duration of Mobility Impairment

43.2% 19.1%
37.8%

38.9% 20.4%
40.6%

Long-term (more than 6 months) Short-term (6 months or less)

95.0% 5.0%

93.2% 6.8%

Age Working age (1864) Senior (6579) Elderly (80+)

59.8% 27.8% 12.4%

47.8% 33.4% 18.8%

Men Only Working age (1864) Senior (6579) Elderly (80+)

39.2% 25.1% 11.1%
3.0%

38.3% 18.8% 13.4%
6.2%

Women Only Working age (1864) Senior (6579) Elderly (80+)

60.8% 34.7% 16.7%
9.4%

61.7% 29.0% 20.0% 12.7%

Driving and Paratransit

Driver Nondriver Paratransit user Paratransit nonuser

61.6% 38.4% 10.8% 89.2%

67.2% 32.8%
6.7% 93.3%

MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO Annual Household Income

44.0% 16.7%
9.1% 30.2%

23.3% 36.4% 25.1% 15.2%

<$15,000 $15,000 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+

36.7% 17.3% 11.9%
8.7% 11.9%
6.7% 6.8%

27.5% 16.5% 12.6% 11.9% 13.0%
8.0% 7.2%

Workforce Participation

Nonworker

87.7%

89.9%

Worker

12.3%

10.1%

* Population is based on nonmissing observations and sample includes missing observations.

Income data are missing for 54 observations (3.4%) and duration of mobility impairment is missing for one observation. Respondents were asked, "Do you/does this person drive?" Paratransit includes reduced-fare taxis and services like Dial-A-Ride.

Includes crutches, white cane, service dog, and other (specify). Other (specify) includes brace, respiratory assistance,

and prosthesis.

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The majority of Georgians with mobility impairments drive, but a substantial minority (38 percent) do not. However, just 11 percent of Georgians with mobility impairments use paratransit services. Given how prevalent immobility is among Georgians with disabilities (see table 151), the difference between the number of Georgians with mobility impairments who do not drive and the number who use paratransit suggests unmet transportation needs. Two out of five people with mobility impairments use no mobility aids. One out of five uses a wheelchair, sometimes in conjunction with other mobility aids. As shown in figure 36, the most common mobility aid reported by respondents was a cane (36.5 percent), followed by a walker (22.9 percent) and a wheelchair (regular, motorized, or motorized scooter) (19.2 percent). Respondents could report multiple aids; 72.1 percent of wheelchair users reported using at least one other mobility aid. For example, someone who uses a wheelchair to travel outside of the house may also use a walker to travel short distances or transfer between rooms in their own home. The much higher prevalence of crutches among Georgians with short-term disabilities (18, versus 2 percent of those with long-term disabilities) suggests that some short-term disabilities are more likely to be acute orthopedic injuries.
Figure 36. Bar graph. Mobility aids used by adults with mobility impairments.
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Figure 34 compared the annual household incomes of adults with mobility impairments and adults without mobility impairments; table 152 further examines demographic differences in annual household income and driving specifically among Georgians with mobility impairments. More than half of people with disabilities have an annual household income of less than $25,000 (figure 34). More than a third fall into the very lowest income category (less than $15,000 per year), including 20 percent of workers (table 152). For comparison, this is more than twice the share of nondisabled workers in very low-income households (8 percent--not shown in the table). Mobility-impaired women of all ages are more likely than men in that same age group to be in a very low-income household, and nearly half of working-age women with disabilities are in very low-income households. Poverty is, perhaps unsurprisingly, less common among Georgians with short-term disabilities, of whom 10 percent live in a very low-income household (compared to 38 percent of those whose disabilities have lasted longer than 6 months).
Table 153 shows differences in mobility and immobility among Georgians with mobility impairments. In general, wheelchair users are more likely to be immobile than other Georgians with disabilities, as are those whose disability is short term, the elderly (but markedly more so for elderly women), nonworkers, and nondrivers. These patterns will be explored further in chapter 5, Risk Factors for Immobility among Adults with Mobility Impairments.
Table 154 shows walking and physical activity among Georgians with mobility impairments. Wheelchair users are less likely to report walking than other Georgians, but it is notable that 34 percent of wheelchair users report making at least one walking trip. This may reflect part-time wheelchair users, but it is also possible that participants are reporting wheelchair trips as
337

"walking" because the vocabulary used in the NHTS questions does not address pedestrian travel by wheelchair users.
338

Table 152. Driver status and income of Georgia adults with mobility impairments.

Driver*

Very Low Income (<$15,000)

All adults with mobility impairment

61.6%

36.7%

Mobility Aid Usage None Wheelchair (incl. wheelchair and other) Cane, walker, or other

61.4% 52.8%
66.2%

36.9% 34.3%
37.8%

Duration of Mobility Impairment Long-term (more than 6 months) Short-term (6 months or less)

61.0% 72.2%

38.2% 9.8%

Age Working age (1864) Senior (6579) Elderly (80+)

63.7% 68.4% 36.2%

44.3% 25.1% 26.3%

Men Only Working age (1864) Senior (6579) Elderly (80+)

61.1% 56.7% 73.2% 52.3%

32.8% 39.8% 21.2% 17.1%

Women Only Working age (1864) Senior (6579) Elderly (80+)

61.9% 68.7% 65.2% 31.0%

39.3% 47.6% 27.8% 29.3%

Driving and Paratransit Driver Nondriver Paratransit user (e.g., reduced fare taxi or Dial-a-Ride) Paratransit nonuser

100.0% --
36.2% 64.7%

31.1% 46.0% 70.8% 32.6%

MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO Annual Household Income

55.7% 62.0% 59.9% 70.5%

32.2% 37.1% 38.5% 42.6%

<$15,000 $15,000 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+

52.5% 65.6% 67.7% 63.8% 68.1% 73.2% 70.9%

100.0% -- -- -- -- -- --

Workforce Participation

Nonworker

59.3%

39.0%

Worker

77.9%

20.3%

* Respondents were asked, "Do you/does this person drive?"

Annual household income is missing for 54 observations and duration of mobility impairment is missing for one observation.

All figures based on nonmissing observations. Includes crutches, white cane, and service dog, and other (specify). Other (specify) includes brace, respiratory assistance,

and prosthesis.

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Table 153. Immobility and average daily trips of Georgians with mobility impairments.

Immobility (Zero Trips in Time Frame)

Daily Trips

Travel Day

Past Few Past Seven Per

Days

Days

Capita

Active Travelers Only

All adults with mobility impairment

39.5%

15.8%

10.0%

2.33

3.85

Mobility Aid Usage

None

33.7%

12.8%

8.9%

2.35

3.54

Wheelchair (incl. wheelchair and other)

50.7%

24.8%

13.2%

1.77

3.58

Cane, walker, or other

40.4%

14.8%

9.7%

2.58

4.34

Duration of Mobility Impairment

Long-term (more than 6 months)

39.4%

15.6%

9.6%

2.31

3.82

Short-term (6 months or less)

40.1%

20.9%

19.0%

2.59

4.33

Age Working age (1864) Senior (6579) Elderly (80+)

34.1%

13.0%

8.4%

2.57

3.89

41.9%

16.2%

8.2%

2.28

3.92

60.1%

29.0%

22.2%

1.28

3.20

Men Only Working age (1864) Senior (6579) Elderly (80+)

37.3%

14.8%

11.0%

2.36

3.77

36.2%

15.1%

10.9%

2.39

3.75

39.7%

13.8%

10.6%

2.37

3.92

37.6%

16.2%

12.7%

2.09

3.35

Women Only Working age (1864) Senior (6579) Elderly (80+) Driving and Paratransit

40.9%

16.5%

9.4%

2.31

3.90

32.5%

11.5%

6.5%

2.69

3.99

43.4%

17.7%

6.6%

2.22

3.93

67.2%

33.0%

25.2%

1.02

3.11

Driver Nondriver Paratransit user Paratransit nonuser

31.9%

9.6%

4.5%

2.74

4.02

51.6%

25.9%

18.9%

1.67

3.45

31.8%

10.6%

9.1%

2.39

3.51

40.4%

16.5%

10.1%

2.32

3.89

MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO

39.2%

17.5%

11.7%

2.22

3.66

37.2%

14.1%

9.9%

2.49

3.97

39.9%

17.7%

10.9%

2.57

4.28

40.9%

13.8%

7.3%

2.32

3.92

Annual Household Income <$15,000 $15,000 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+

38.7%

14.1%

8.3%

2.43

3.97

35.4%

15.2%

12.2%

2.41

3.74

48.3%

23.7%

11.9%

1.81

3.50

29.9%

14.8%

11.4%

2.81

4.01

46.1%

19.1%

12.4%

2.14

3.98

34.2%

11.7%

7.1%

2.73

4.15

46.8%

13.7%

7.0%

1.91

3.58

Workforce Participation Nonworker Worker

42.1%

17.2%

10.6%

2.21

3.82

20.9%

6.1%

5.7%

3.16

3.99

Based on 1,609 adults with mobility impairments. Income is missing for 54 observations and duration of impairment for one observation. Reporting at least one trip on the travel day. Includes crutches, white cane, service dog, and other (specify). Other (specify) includes brace, respiratory assistance, and prosthesis. Respondents were asked, "Do you/does this person drive?" Paratransit includes reduced-fare taxis and special services such as Dial-A-Ride.

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Table 154. Walking and physical activity among Georgians with mobility impairments.

Walk Trips (Past 30 Days) Physical Activity Level (Typical Week)

Any Walk Trip

Number of Walk Trips

Active (some light, moderate, or vigorous
activity)

Inactive (rarely or never)

All adults with mobility impairment

60.1%

5.0

64.8%

35.2%

Mobility Aid Usage

None

72.8%

6.1

Wheelchair (incl. wheelchair and other)

34.4%

2.3

Cane, walker, or other

58.5%

5.1

71.8% 51.8% 63.4%

28.2% 48.2% 36.6%

Duration of Mobility Impairment

Long-term (more than 6 months) Short-term (6 months or less)

60.4%

5.1

54.6%

2.7

63.9% 81.8%

36.1% 18.2%

Age Working age (1864) Senior (6579) Elderly (80+)

70.2%

6.3

48.1%

3.7

37.8%

2.0

69.3% 62.4% 48.7%

30.7% 37.6% 51.3%

Men Only Working age (1864) Senior (6579) Elderly (80+)

62.5%

5.4

70.7%

6.4

46.6%

3.6

52.7%

3.2

65.3% 68.7% 56.8% 68.0%

34.7% 31.3% 43.2% 32.0%

Women Only Working age (1864) Senior (6579) Elderly (80+) Driving and Paratransit

58.5%

4.8

69.9%

6.2

49.1%

3.7

33.1%

1.6

64.5% 69.7% 66.1% 42.5%

35.5% 30.3% 33.9% 57.5%

Driver Nondriver Paratransit user Paratransit nonuser

62.4%

5.6

56.3%

4.1

62.8%

4.5

59.7%

5.1

69.0% 58.1% 63.9% 64.9%

31.0% 41.9% 36.1% 35.1%

MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO

57.6%

4.5

58.4%

3.8

65.3%

4.7

63.0%

6.4

65.6% 68.1% 71.9% 59.6%

34.4% 31.9% 28.1% 40.4%

Annual Household Income <$15,000 $15,000 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+

69.6%

6.1

50.9%

3.7

60.9%

6.2

55.9%

3.2

52.5%

5.7

62.0%

3.8

43.5%

2.8

69.1% 57.8% 64.8% 70.8% 56.7% 71.6% 60.7%

30.9% 42.2% 35.2% 29.2% 43.3% 28.4% 39.3%

Workforce Participation Nonworker Worker

58.0%

4.9

75.1%

6.1

63.4% 75.0%

36.6% 25.0%

Based on 1,609 adults with mobility impairments. Income is missing for 54 observations and duration of impairment for one observation. Includes crutches, white cane, service dog, and other (specify). Other (specify) includes brace, respiratory assistance, and prosthesis. Respondents were asked, "Do you/does this person drive?" Paratransit includes reduced-fare taxis and special services such as Dial-A-Ride.

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Risk Factors for Immobility among Adults with Mobility Impairments
The researchers use logistic regression to disentangle the demographic factors associated with immobility among people with mobility impairments (table 155).98 We created three binary logit models looking at the likelihood of being immobile on the travel day, immobile for "the past few days," and immobile for the past week.99 Table 156 isolates the AME of key covariates.
Being in poor health is associated with a 12-percentage-point increase in the probability of being immobile on the travel day, compared to a mobility-impaired Georgian in good health. It is also associated with an 11-percentage-point increase in the probability of being immobile for the past few days, and a 7-percentage-point increase in the probability of being immobile for the past week. Being in fair health has a similar effect on the travel day (an 11-percentage-point increase in the probability of travel day immobility), but the effect drops off sharply when examining longer term immobility. In other words, on any given day, disabled Georgians in fair or poor health are equally likely to be housebound, but Georgians in fair health are more likely to be able to leave the house at least a few times per week.
Wheelchair use is also associated with an increased likelihood of immobility, and working outside the home and being a driver are associated with a decreased likelihood of immobility. Living alone is associated with a decreased likelihood of immobility, but this may be because people able to live independently are also more able to travel independently. Paratransit use is
98 Income was excluded from the models because it was not significant and was missing for 3.3 percent of observations.
99 Respondents with zero trips on the travel day were asked about the date of their most recent trip. Response options included: (1) "The day before," (2) "A few days before," (3) "A week before," (4) "More than a week but within a month," and (5) "More than a month." This report considers respondents to be immobile for the past few days if they selected response 3, 4, or 5 and immobile for the past week if they selected response 4 or 5. A respondent who has been immobile for the past week has also been immobile for the past few days and on the travel day.
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associated with a lower probability of immobility, but the effect is not significant. Being female is associated with an increased chance of immobility, but only among elderly residents. Urban neighborhoods appear to confer some mobility benefits; people with mobility impairments living in urban neighborhoods are less likely to be immobile on the travel day and over the past several days; there is no significant difference in weekly immobility.
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Table 155. Logistic regression: Immobility among Georgia adults with a mobility impairment. 344

Table 156. Selected average marginal effects on probability of being immobile among adults with a mobility impairment, percentage points.

1. Immobile on

Travel Day

Mean predicted probability x 100

38.58

Average Marginal Effects (percentage points)

2. Immobile, Past Few Days
15.95

3. Immobile, Past Seven
Days
10.00

Health (reference: good, very good, or excellent)

Fair

10.46 ***

5.17 **

3.45 **

Poor

11.93 ***

11.39 ***

7.31 ***

Wheelchair user

7.74 **

5.40 **

2.92

Live alone Worker Status (reference: nonworker)
Work outside of home (nonhome-based)

-7.75 *** -16.88 ***

-2.12 -7.98

0.31 -1.79

Work from home (home-based)

0.65 *

-5.58

-4.44

Driver Urban neighborhood

-19.18 *** -7.03 **

-14.58 *** -3.84 *

-10.26 *** -1.57

* Indicates that model coefficient was significant for = .10, ** for = .05, and *** for = .01. Respondents with zero trips on the travel day were asked about the date of their most recent trip. Response options included 1. "The day before," 2. "A few days before," 3. "A week before," 4. "More than a week but within a month," and 5. "More than a month." This report considers respondents to be immobile for the past few days if they selected response 3, 4, or 5 and immobile for the past week if they selected response 4 or 5. A respondent who has been immobile for the past week has also been immobile for the past few days and on the travel day. Weighted mean predicted probability based on unweighted logit models. This report displays predicted probabilities as a percentage rather than observed means for consistency. (The two values are not identical because survey weights have been applied to results produced by an unweighted model. For all variables, the two values differ by less than one percentage point.) Since the explanatory variables presented are dummy variables indicating the presence of a certain characteristic, the AME for each variable is calculated by predicting the probability of being immobile as if the whole sample did not have the characteristic in question, predicting the probability as if the whole sample did have the characteristic in question, subtracting the former from the latter, and averaging the predicted marginal effect for all observations in the sample. Effects shown are weighted means based on the unweighted logit models.

For nonhome-based workers, worker = 1 and work from home = 0. For home-based workers, both worker and work from home = 1. Urban or second-city neighborhood type versus suburban, small-town, or rural.

Taken together, the findings presented here indicate that Georgians with disabilities face multiple barriers to mobility. Policy is needed to address accessibility barriers in public and private

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transportation and on the public right of way, as well as to address related sources of social exclusion (Beyzak et al. 2019, Decker 2006, National Council on Disability 2015).
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CHAPTER 6. NONMOTORIZED TRANSPORTATION AND ACCESS/EGRESS TRAVEL
CHAPTER 6 SUMMARY
This chapter examines nonmotorized travel (i.e., walking and biking) and access/egress travel, or travel to reach a primary mode of transportation.
Overview provides an overview of how many Georgians walk and bike over a typical week and examines some barriers to walking and biking more frequently. It discusses gender differences in concerns about the safety of NMT and reviews the available data about physical activity.
Access and Egress Travel discusses access and egress travel. It reviews how the NHTS measures access/egress legs for transit and nontransit trips and discusses related measurement issues. Access/egress travel is particularly important when studying nonmotorized travel because walking and biking account for a large proportion of access/egress travel; including access/egress legs increases the number of instances of walking and biking per capita by 28 percent versus including separately recorded nonmotorized trips alone. This section especially examines access/egress to public transit, for which NMT is a dominant mode.
Travel Day Walking and Biking by Georgia Adults examines travel-day walking and biking by Georgia adults. It incorporates the trips analyzed throughout this report, as well as the legs analyzed in Access and Egress Travel in this chapter. Frequency of walking and biking, purpose of NMT trips/legs, and duration of trips by purpose are examined and demographic differences explored. Time of day is also examined.
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Captive and Choice Nonmotorized Travel examines captive and choice nonmotorized travel. As with public transit, some pedestrians and cyclists choose to walk and bike, while other use these modes by necessity. Captive pedestrians and cyclists spent more time traveling than their choice counterparts did, after controlling for trip quantity and purpose. Captive nonmotorized travelers also tended to make more nonmotorized trips, compounding the differences in total travel time. While increased walking and biking is broadly considered a public health goal, it is important to remember that some travelers are already walking or riding more than they would like.
Children's Nonmotorized and School Travel examines nonmotorized and school travel for children ages 517. After describing the research team's methods for identifying school trips, which are analogous to the methods used to define adults' work commutes in chapter 2, this section reviews children's observed and usual modes of travel to and from school. Walking and biking account for approximately 5 percent of school travel in Georgia. It then describes children's nonmotorized travel for all purposes. Children are more likely to have walked or biked than are adults. The difference is particularly pronounced for cycling; 30.5 percent of children reported cycling in the past 7 days versus 5.5 percent of adults. Cycling is most common among children under the age of 9 and declines steadily as they approach adulthood.
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OVERVIEW As shown in figure 37, in a typical week, 72.6 percent of Georgia adults will walk, ride a bike, or both.100 Cyclists are, in general, also pedestrians; only 0.4 percent of Georgians reported biking but not walking.
Figure 37. Pie chart. Use of nonmotorized modes by Georgia adults (past 7 days). As shown in table 157, walkers average 7.9 walking trips per week, and cyclists average 3.1 bike trips. When walking and biking are combined, nonmotorized travelers make an average of 8.1 nonmotorized trips per week.
100 The precise questions were: "In the past seven days, how many times did you: 1. "...take a walk outside including walks to exercise, go somewhere, or to walk the dog (e.g., walk to a friend's house, walk around the neighborhood, walk to the store, etc.)?" 2. "... ride a bicycle outside including bicycling to exercise, or to go somewhere (e.g., bike to a friend's house, bike around the neighborhood, bike to the store, etc.)?
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There are some differences between MPO tiers, but the larger differences are by neighborhood type, which can vary substantially within an MPO. NMT is most common in the densest urban neighborhood type (which, in Georgia, is only found in Atlanta). Women are more likely to walk than men, but less likely to bike. Biking is most common among those ages 1852 compared to other age groups, while walking is most common from ages 3764. Pedestrians and cyclists are most common among residents of the lowest-income (less than $15,000 per year) and highest-income ($100,000 or more per year) households, compared to their incidence among other income groups. However, low-income pedestrians and cyclists walk and ride their bikes more frequently than do their wealthier counterparts (9.9 times and 7.3 times a week, respectively).
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Table 157. Walking and biking among Georgia adults (past 7 days).

Percent of Adults who have Used Mode (Past 7 Days)

Mean Trips among Mode Users (Past 7 Days)

All adults ages 18+ MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO counties Urbanicity (Neighborhood Type) Rural Small town Suburban Second city Urban Sex Male Female Age Cohort Millennial and Gen Z (1836) Gen X (3752) Pre-retirement age Boomer (5364) Retirement Age (65+) Caregiver Status Noncaregiver Caregiver, youngest child ages 015 Race White non-Hispanic only Black, Black multiracial & Black Hisp. Other Table continues on next page.

Walk 72.2%

Bike 5.5%

Any NMT
72.5%

Walk 7.9

Bike 3.1

Any NMT
8.1

71.7%

5.1% 72.0%

7.2

2.6

7.3

72.1%

7.6% 72.8%

8.0

4.1

8.3

72.4%

6.1% 73.0%

8.6

3.4

8.9

73.3%

4.4% 73.5%

9.6

3.3

9.8

72.9%

4.0% 73.1%

9.2

2.9

9.3

67.4%

5.6% 68.0%

8.0

2.9

8.2

72.5%

5.5% 72.8%

7.1

3.0

7.3

75.0%

5.3% 75.4%

7.4

3.7

7.7

91.0% 18.5% 91.7%

9.3

3.5

10.0

71.6%

6.8% 72.2%

8.2

3.3

8.4

72.7%

4.3% 72.9%

7.7

2.9

7.9

70.9%

6.2% 71.2%

8.3

3.3

8.5

75.3%

6.3% 75.6%

7.3

3.3

7.6

74.1%

5.3% 74.7%

8.3

3.0

8.4

67.1%

2.8% 67.4%

7.9

2.4

8.0

72.2%

5.1% 72.7%

8.0

3.3

8.2

72.1%

6.3% 72.3%

7.7

2.8

8.0

74.2%

5.9% 74.6%

8.3

3.0

8.5

69.0%

4.8% 69.3%

7.5

3.7

7.7

71.3%

5.2% 71.5%

7.2

2.4

7.4

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Table 157. (Continued).
Barriers to Walking and Biking More Frequently How could Georgians be enticed to walk or bike more? Table 158 shows perceived barriers to walking and biking more frequently. The most common complaints for pedestrians are missing or inadequate sidewalks and insufficient night lighting. For cyclists, heavy traffic is the most common complaint, with missing/inadequate sidewalks and a lack of nearby paths or trails coming in second and third.
352

Table 158. Perceived barriers to walking and biking more frequently (pedestrians and cyclists ages 18+).

Reason for not walking/biking more

Walk more

Bike more

No nearby paths or trails

18.1%

20.0%

No nearby parks

14.4%

13.3%

No sidewalks or sidewalks are in poor condition

22.5%

20.9%

Street crossings are unsafe

11.7%

15.6%

Heavy traffic with too many cars

17.4%

27.5%

Not enough lighting at night

21.4%

19.1%

None of the above

38.3%

33.4%

Participants were allowed to select multiple response options. Walk questions were asked of people who walked at least once in the past 7 days; bike questions were asked of people who biked at least once in the past 7 days.

These questions were only asked of people who had walked or biked at least once in the 7 days previous to the survey. As such, they describe barriers to walking and biking more, but not necessarily barriers to walking and biking at all. The NHTS does not have direct information about the concerns of the 94.5 percent of Georgians who have not biked recently; more information is needed about how to attract new pedestrians and cyclists.
As shown in figure 38, there are notable gender differences in perceived barriers to walking and biking. Women selected most barriers more frequently than men (with the sole exception of a lack of nearby parks, which male cyclists selected more than female cyclists). The largest gender gaps among pedestrians focused on safety: night lighting (9.8 percentage point difference between men and women) and sidewalks (7.2 percentage point difference). Among cyclists, the largest gender gaps were around sidewalk condition (9.8 percentage points), heavy traffic (9.5 percentage points) and a lack of nearby paths or trails (9.2 percentage points). A number of studies have documented how unfavorable traffic and safety conditions disproportionately discourage female nonmotorized travelers (e.g., Emond, Tang, and Handy 2009).

353

Figure 38. Bar graphs. Gender differences in perceived barriers to walking and biking more frequently among pedestrians and cyclists ages 18+.
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Physical Activity In this section, we examine physical activity between different groups of Georgians, and in relationship to walking and biking. As shown in table 159, 85 percent of Georgians are at least somewhat physically active in a typical week; 61.5 percent reported some light or moderate physical activity. Twenty-four percent reported vigorous physical activity; whether these respondents also engaged in light or moderate physical activity is not recorded. Respondents who engaged in light/moderate physical activity reported an average of four sessions of at least 30 minutes per week, while those who reported vigorous activity reported an average of 5.2 sessions. Residents of urban neighborhoods are less likely to be inactive than residents of other types of neighborhoods (6.5 percent versus 1416 percent elsewhere). They are much more likely to engage in vigorous physical activity (41 percent versus 2225 percent elsewhere). However, urban residents engaged in vigorous physical activity report fewer instances per person than their vigorously active counterparts in other types of areas (4 percent versus 4.95.8 percent elsewhere). Men are more active than women. Low-income people, nondrivers, and people with mobility impairments are much more likely to report no physical activity. As discussed in chapter 5, Health and Disability, there is considerable overlap between those groups.
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All adults ages 18+ MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO counties Urbanicity (Neighborhood Type) Rural Small town Suburban Second city Urban Sex Male Female Age Cohort Millennial and Gen Z (1836) Gen X (3752) Pre-retirement age Boomer (5364) Retirement Age (65+) Driver Status Nondriver Driver Table continues on next page.

Table 159. Physical activity among Georgia adults.

Physical Activity Level (Row Percentages )*

Rarely or Never 14.6%

Some Light or Moderate 61.5%

Some Vigorous
23.8%

Weekly Instances of Physical Activity

Light/Moderate (Among

Vigorous (Among

Light/Moderately Active Respondents with Vigorous

Respondents)

Physical Activity)

4.03

5.22

14.2%

61.9%

23.9%

3.94

4.84

15.4%

61.1%

23.5%

3.77

5.44

13.6%

63.0%

23.4%

3.98

5.51

15.8%

60.3%

23.9%

4.51

5.96

15.1%

59.5%

25.4%

4.41

5.81

15.7%

61.6%

22.7%

3.88

5.18

13.8%

62.7%

23.5%

3.99

4.92

14.9%

63.4%

21.8%

3.86

5.24

6.5%

52.5%

41.0%

4.29

4.05

13.8%

55.8%

30.4%

4.15

5.48

15.4%

66.9%

17.8%

3.93

4.83

15.5%

56.1%

28.4%

4.08

5.53

13.8%

61.4%

24.7%

3.91

5.04

12.5%

66.0%

21.5%

3.99

5.14

16.8%

67.4%

15.8%

4.17

4.72

27.0%

59.4%

13.6%

4.03

6.64

13.1%

61.8%

25.1%

4.03

5.13

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Table 159. (Continued).

Continued from previous page.

Physical Activity Level (Row Percentages )*

Weekly Instances of Physical Activity

Light/Moderate (Among

Vigorous (Among

Rarely or Some Light or

Some

Light/Moderately Active Respondents with Vigorous

Never

Moderate

Vigorous

Respondents)

Physical Activity)

All adults ages 18+

14.6%

61.5%

23.8%

4.03

5.22

Race

White non-Hispanic only

12.7%

60.3%

26.9%

4.23

5.30

Black, Black multiracial & Black Hispanic

17.2%

62.9%

19.9%

3.66

4.85

Other

16.3%

63.6%

20.1%

4.09

5.69

Mobility Impairment: a "condition or handicap that makes it difficult to travel outside of the home."

Absent

12.5%

61.8%

25.7%

4.11

5.22

Present

35.4%

59.1%

5.5%

3.22

5.44

Annual Household Income

<$15,000

19.7%

64.0%

16.3%

4.13

6.04

$15,000 to $49,999

15.7%

62.6%

21.7%

4.10

5.46

$50,000 to $99,999

13.6%

64.5%

21.8%

4.05

5.32

$100,000+

11.7%

55.3%

33.0%

3.82

4.73

Vehicle Deficit Category of Household (a vehicle-deficit household owns at least one vehicle, but fewer vehicles than potential drivers.)

Zero-vehicle

16.3%

68.1%

15.6%

3.80

5.66

Deficit

20.1%

60.5%

19.3%

3.96

5.60

Nondeficit (sufficient/surplus)

12.5%

61.5%

26.1%

4.07

5.10

Worker Status

Nonworker

18.8%

65.4%

15.8%

4.20

5.13

Worker

12.0%

59.1%

28.8%

3.91

5.26

* Question wording: "Which of the following statements best describes how physically active you are in a typical week? (1) I rarely or never do any physical activity; (2) I do

some light or moderate physical activity; (3) I do some vigorous physical activities." Respondents who reported light moderate physical activity were asked about light/moderate physical activity. Respondents who reported vigorous activity were only asked

about vigorous activity; data on light/moderate physical activity by this group were not collected.

N=15,120. Table excludes observations missing number of walk or bike trips, physical activity, health, or disability for later comparison on these variables.

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How does the physical activity of the 73 percent of Georgians who reported walking and/or biking in the previous week compare to the physical activity of the 27 percent who did not walk or bike? As shown in table 160, pedestrians and cyclists are less likely to be sedentary than Georgians who do not walk or bike. Only 8.8 percent of Georgians who walk or bike reported rarely or never engaging in physical activity, versus 30.2 percent of those who did not walk or bike. Among the physically active, pedestrians and cyclists also report a higher number of activity sessions. Cyclists are the most physically active group. Nearly half of cyclists report vigorous physical activity, versus one quarter of Georgians who walked only and 16 percent of Georgians who neither walked nor biked.
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Table 160. Usual physical activity of Georgia adults by walking and biking behavior (past 7 days).

Physical Activity Level (Row Percentages )*

Rarely or Some Light or

Never

Moderate

All adults ages 18+

14.6%

61.5%

Any Nonmotorized Travel (Past Seven Days)

None (zero walk and bike trips)

30.2%

54.2%

Some (1+ walk or bike trip(s))

8.8%

64.3%

Types of Nonmotorized Travel (Past Seven Days)

None

30.2%

54.2%

Walk only

9.0%

65.7%

Bike only and bike + walk

5.2%

47.2%

Number of Nonmotorized Trips (Walk + Bike) (Past 7 Days)

0

30.2%

54.2%

15 (group mean: 3.1 trips)

10.5%

67.0%

Some Vigorous
23.8%
15.6% 26.9%
15.6% 25.2% 47.6%
15.6% 22.5%

Weekly Instances of Physical Activity

Light/Moderate (Among

Vigorous (Among

Light/Moderately Active Respondents with Vigorous

Respondents)

Physical Activity)

4.03

5.22

3.41

5.00

4.23

5.27

3.41

5.00

4.23

5.33

4.18

4.87

3.41

5.00

3.44

4.54

6+ (group mean: 13.8 trips)

6.7%

61.2%

32.0%

5.21

5.86

Number of Walk Trips (Past 7 Days)

0

30.1%

54.1%

15.9%

3.41

5.01

15 (group mean: 3.1 trips)

10.3%

66.7%

23.0%

3.44

4.52

6+ (group mean: 13.8 trips)

6.8%

61.7%

31.5%

5.26

5.94

Number of Bike Trips (Past 7 Days)

0

15.2%

62.4%

22.4%

4.02

5.27

12 (group mean: 1.4 trips)

6.2%

49.1%

44.7%

3.99

4.99

3+ (group mean: 5.6 trips)

3.7%

44.6%

51.7%

4.47

4.72

* Question wording: "Which of the following statements best describes how physically active you are in a typical week? (1) I rarely or never do any physical activity; (2) I do

some light or moderate physical activity; (3) I do some vigorous physical activities." Respondents who reported light moderate physical activity were asked about light/moderate physical activity. Respondents who reported vigorous activity were only asked

about vigorous activity; data on light/moderate physical activity by this group were not collected. Category boundaries were chosen based on the median number of trips of each type. Fifty-three percent of NMT travelers made 15 trips, 55 percent of walkers made

15 walking trips, and 59 percent of cyclists made 12 trips.

N=15,120. Table excludes observations missing number of walk or bike trips, physical activity, health, or disability for later comparison on these variables.

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Not surprisingly, pedestrians and cyclists are more likely to report being active in general. It would be useful to know whether NMT replaces other physical activity, or supplements it. However, the following data limitations make it difficult to document the relationship between number of walking and biking trips and the specific number of sessions of physical activity:
Because people who reported engaging in vigorous physical activity were not asked how many times they engage in moderate or light physical activity, there is no measure of total physical activity for each respondent. There is no way to distinguish between someone who went to the gym once and also engaged in light exercise throughout the rest of the week from someone who went to the gym once and engaged in no other exercise.
The measures of physical activity are generic (a typical week) while the measures of walking and biking are specific (the past 7 days).
Information about how many of the instances of physical activity reported are walking versus biking is not available. The extent of overlap between reported walk/bike trips and recorded physical activity bouts is, therefore, unclear.
To facilitate more in-depth analysis of this topic, the NHTS could consider:
Asking participants about specific instances of light, moderate, and physical activity in the past 7 days.
Recording whether bouts of physical activity are walking/running, biking, or something else.
Alternatively, asking participants to report total minutes of light, moderate, and vigorous physical activity over the past week.
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ACCESS AND EGRESS TRAVEL

As shown in table 161, Georgians make more than 950,000,000 nonmotorized trips per year. However, in addition to these trips, which are analyzed throughout this report, Georgians also walk and bike as a way to get to and from other modes of transportation (i.e., access/egress legs). As shown in table 161, Georgians used walking or biking to access/egress another mode more than 260,000,000 times per year.101

Table 161. Total annual trips and access/egress legs by nonmotorized modes.

Total Nonmotorized Trips* per Year

Walk

Bike

All NMT

All Georgians Ages 5+

887,174,800

66,663,125

953,837,925

MPO Tier

1. Atlanta MPO

539,657,200

23,250,820

562,908,020

2. Medium MPOs

141,069,100

17,425,210

158,494,310

3. Small MPOs

74,786,200

6,015,215

80,801,415

4. Non-MPO counties

131,662,300

19,971,880

151,634,180

* A trip is a unit of travel from one origin to one destination. Total Nonmotorized Access/Egress Legs per Year

Walk

Bike

All NMT

All Georgians Ages 5+

260,397,290

3,534,818

263,932,108

MPO Tier

1. Atlanta MPO

203,008,300

2,342,461

205,350,761

2. Medium MPOs

32,361,740

1,192,357

33,554,097

3. Small MPOs

12,374,430

-

12,374,430

4. Non-MPO counties

12,652,820

-

12,652,820

An occasion where a respondent walks or bikes to access or egress another mode of transportation (e.g., public

transit, commuter train). These legs are usually included as part of the trip by the primary mode rather than as

individual trips. Thus, nonmotorized access/egress legs are essentially mutually exclusive with nonmotorized trips.

101 Two additional sources of nonmotorized travel are not reflected in the NHTS: stationary fitness NMT (i.e., a stationary bicycle or treadmill) and walking within a destination (e.g., a shopping mall or amusement park).
361

The following subsections will describe how the NHTS is designed to measure access/egress travel, discuss some deviations between the survey as written and some of the resulting data, and then discuss trends in access/egress travel.
Measurement of Access/Egress Travel The NHTS has different procedures for measuring access/egress travel for public transit versus other modes. For any mode besides public transit, each section of a multimodal sequence of trips is recorded as its own trip. So, for example, if someone walks to meet up with a carpool to work, the travel would be recorded as a walk trip for the purpose of changing mode of transportation, followed by a car trip with the purpose of work. The same is true for other mixes of modes (e.g., bike to car, take a taxi to the airport). These sequences would be recorded as two trips, and a longer sequence would be recorded as three or more trips.
For public transit, in contrast, access and egress legs are considered part of the main transit trip (and accordingly, throughout this report we will distinguish access and egress "legs" from separately recorded "trips"). So, for instance, a trip where someone walked to the bus stop, took the city bus, and then got off the bus and walked to the office would be recorded as a single trip by public transit (rather than two walking trips and a transit trip); details about the walking are included as additional variables about the transit trip. As a result, these access/egress legs are occasions of walking (or other modes, including bicycling) that are not included in trip counts and mode shares.
Incorporating transit access and egress legs into public transit trips (as opposed to treating each portion as a separate trip) has a number of advantages. First, a large quantity of analysis is done at the trip level rather than the more complicated tour level. Including access/egress travel within
362

a transit trip therefore ensures that the full, door-to-door durations of public transit trips are considered. Additionally, it ensures that number of trips is a reasonable proxy for number of destinations accessed, rather than including extra trips that are generated entirely by the need to take public transit. This is important in particular for assessing the mobility of transit-dependent people.

However, because walking and biking account for 70 percent of access and egress legs, not including those legs results in an undercount of the total amount of walking and biking. As shown in table 162, walking and biking account for 8.6 percent of all trips made by Georgians ages 5+. However, when all instances of travel, including both trips and access/egress legs by any mode are considered, the apparent mode share of nonmotorized travel increases to 10.6 percent.

Table 162. Mode share of walking and biking trips and legs.

Trips Only

Trips and Access/Egress Legs*

Walk

Bike All NMT Walk

Bike

All NMT

All Georgians Ages 5+

8.0%

0.6%

8.6%

10.0%

0.6%

10.6%

MPO Tier

1. Atlanta MPO

8.7%

0.4%

9.1%

11.5%

0.4%

11.9%

2. Medium MPOs

8.1%

1.0%

9.1%

9.7%

1.0%

10.7%

3. Small MPOs

6.7%

0.5%

7.2%

7.7%

0.5%

8.2%

4. Non-MPO counties

6.5%

1.0%

7.4%

7.0%

1.0%

8.0%

* A leg is an occasion where a respondent walks or bikes to access or egress another mode of transportation (e.g.,

public transit, commuter train). These legs are usually included as part of the trip by the primary mode rather than

as individual trips.

As table 163 shows, including access/egress legs also increases the number of instances of travel-day walking and biking per capita by 28 percent, from 100 trips to 127 trips and legs. The effect is even more pronounced in the Atlanta MPO, where including access/egress legs

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increases the measured instances of walking and biking per capita by 36 percent (from 109 trips to 148 trips and legs).

Table 163. Instances of walking and biking per person ages 5+.

Trips Only

Trips and Access/Egress Legs*

Walk

Bike All NMT Walk

Bike All NMT

All Georgians Ages 5+

92.8

7.0

99.8

120.1

7.3

127.4

MPO Tier

1. Atlanta MPO

104.1

4.5

108.6

143.3

4.9

148.3

2. Medium MPOs

92.1

11.4

103.5

113.3

12.2

125.4

3. Small MPOs

76.9

6.2

83.0

89.6

6.2

95.8

4. Non-MPO counties

70.4

10.7

81.1

77.2

10.7

87.8

* A leg is an occasion where a respondent walks or bikes to access or egress another mode of transportation (e.g.,

public transit, commuter train). These legs are usually included as part of the trip by the primary mode rather than

as individual trips.

There is no settled answer as to whether access and egress legs should be counted as separate trips for the purposes of calculating mode share, and indeed the NHTS's approach to the question has not been constant over the years.102 For broad comparability, most of this current analysis has followed the NHTS's practice of considering access and egress legs to be part of a larger trip by the mode accessed by those legs. However, for the purpose of measuring the number of occasions and amount of time Georgians spend walking and biking, including access/egress legs is imperative for getting a more accurate picture.
The data contain a second problem: 95 percent of trips by public transit have at least one access and/or egress mode listed, but additionally, 0.4 percent of trips by modes other than public transit

102 In 2009, NHTS respondents were directed not to include trips to change the type of transportation. In 2017, travel to access/egress public transit was intended to be excluded while access/egress legs for modes other than public transit were to be recorded as trips with the purpose of changing mode of transportation. In practice, as NHTS Access/Egress Questions and Instructions will show, this procedure was not always followed.
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incorrectly have access/egress mode(s) recorded (accounting for 10 percent of the access/egress legs recorded). As table 164 shows, the Georgia subsample of the NHTS includes 1,319 access/egress legs. Of these, 977 were to access/egress public transit, 212 were for transit-like modes (e.g., paratransit, charter or intercity bus, ferry), and 130 for modes that were neither public transit nor transit-like. Legs are divided by the mode used for the leg itself, and the mode of the trip for which the leg served as access or egress (the "parent" mode).

Table 164. Access and egress legs by mode and parent mode (unweighted).

Leg Mode

Mode Being Accessed/Egressed (Parent Mode)

Public Transit

Transit-like*

Other

All legs by Georgians ages 5+

977

212

130

Adults ages 18+

949

183

122

Walk

670

105

59

Bike

26

0

0

POV

54

7

18

School bus

81

2

0

Public or paratransit

65

8

0

Other bus (e.g. intercity, private, or charter)

0

12

2

Air

0

0

28

Other Walk + other(s)

18

39

10

32

8

3

Multiple, not including walk or bike

3

2

2

Children ages 517

28

29

8

Walk

15

26

6

POV

10

1

1

School bus

3

0

0

Other

0

1

0

Walk + other(s)

0

1

1

Note: Each trip can have both an access and an egress leg. The 1,319 legs here were attached to 666 trips.

* Legs to access/egress modes that are similar to public transit. For adults, this includes paratransit (N=39 legs), other bus (139), and ferry (6). For children, this includes other bus (28) and school bus (1).

Legs to access/egress a mode that is not transit or transit-like. For adults: air (N=57 legs), POV (33), taxi/ridehail/limo (22), walking (2), and other (10). For children: POV (2) and walking (2).

Includes one instance of walk + bike.

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Access/egress legs for nontransit modes would have been more accurately recorded as trips for the purpose of changing mode of transportation.103 Describing the specific questions asked by the NHTS helps to explain how some (albeit not all) of these mistaken records may have originated.
NHTS Access/Egress Questions and Instructions Before reporting about individual trips, respondents were asked: "Did you use a bus, subway, train, or some other type of public transportation during your travel day?" If they answered yes, they were prompted after each reported trip, "Did you take a bus, subway, train, or some other type of public transportation from [PreviousPlace] to [CurrentPlace]?" If they answered in the affirmative, after reporting the trip's mode, they were asked, "How did you get to the [mode]?" and "How did you get from the [mode] to [location]?"
The fact that the definition of public transit for these questions includes "some other form of public transportation" likely explains why a number of transit-like modes have associated access/egress legs: some respondents would conclude that an intercity bus or a commuter ferry are public transit. Even if subsequent data cleaning would classify the trip itself as nontransit, the recorded access/egress leg(s) would remain with the data. Some other respondents may have had
103 Interestingly, a disproportionate share of incorrectly reported legs comes from higher income respondents. Respondents with an annual household income of at least $50,000 account for 26 percent of correctly reported legs to access/egress public transit, but 48 percent of incorrectly reported legs to access/egress a nontransit trip (unweighted). Put differently, 38 percent of access/egress legs reported by people earning $50,000 or more are for an inappropriate mode, versus just 21 percent of legs reported by people earning less than $15,000 and 17 percent of legs reported by people making $15,00049,999 (unweighted).
366

expansive views of public transit; air was the most common nontransit and nontransit-like parent mode. The remainder can likely be attributed to user error.104 Access/egress legs for parent modes besides public transit were incorrectly recorded, but they do represent real instances of travel. A majority of these misrecorded legs are nonmotorized, and they are equivalent to close to 5 percent of the total of correctly recorded nonmotorized trips (unweighted). To avoid missing these unintentionally hidden instances of walking, subsequent sections of this chapter recategorize access/egress legs for modes other than transit as trips to change mode of transportation. Access/Egress Travel by Georgia Residents Table 165 shows the weighted mode share of access legs for public transit and other parent modes.
104 The inverse error--reporting transit access/egress travel as separate trips for the purpose of change mode of transportation--also occurred. It was, however, less common. The researchers identified three nonmotorized trips that would have more accurately been considered transit access/egress legs. For the sake of simplicity, those trips were not reclassified.
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Table 165. Mode share of legs to access/egress public transit and other modes.
Nonmotorized modes account for three quarters of transit access/egress legs, and a plurality (46 percent) of legs to access/egress other modes.105 Walking accounts for the overwhelming majority of access/egress legs. The second-most common type of transit access mode is another transit or transit-like mode (17.4 percent). A commuter might, for instance, take a local bus to get to the train station. POVs account for 6 percent of transit access/egress legs. Interestingly, no access/egress legs by taxi or other vehicle-for-hire were recorded. As shown in table 166, the average leg to access/egress public transit is 11.6 minutes. Nonmotorized legs are, on average, shorter than motorized legs. Walk legs are the shortest at 8.6 minutes.
105 The difference between the unweighted and weighted proportion of nonmotorized legs to incorrect access modes is likely due to the fact that high-income people, who account for the majority of these trips, are weighted less heavily in order to account for the greater difficulty in eliciting survey responses from low-income households.
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Table 166. Mean duration in minutes of access/egress legs by mode.

Mode Being Accessed/Egressed (Parent Mode)

Leg Mode

Public Transit

Transit-like* and Other

All access/egress legs by Georgians ages 18+

11.6

15.5

All nonmotorized legs

9.2

7.2

Walk

8.6

7.4

Bike

11.9

-

Walk + other(s)

19.3

1.8

All motorized

18.9

21.9

Transit and transit-like

18.2

22.6

POV

20.2

13.7

Air

-

74.8

Other or multiple without NMT

21.1

9.8

* Transit-like includes school, intercity, private, or charter bus. Ferry is included as a transit-like parent mode, but was not a listed response option for leg modes.

Table 167 shows the mode of public transit access/egress legs disaggregated by various demographic factors.

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Table 167. Mode share of public transit access/egress legs by demographic characteristics (row percentages).

NMT (Walk, Bike, Transit or

Other

Walk + Other)

Transit-like*

Motorized*

Legs by all adults ages 18+

74.6%

17.3%

8.0%

MPO Tier

1. Atlanta MPO

73.8%

17.7%

8.5%

2. Medium MPOs

80.7%

16.1%

3.2%

3-4. Small MPOs & non-MPO counties

75.0%

12.8%

12.2%

Urbanicity (Neighborhood Type)

Rural & small town

62.5%

24.9%

12.5%

Suburban

77.4%

11.7%

10.9%

Second city

67.5%

26.4%

6.1%

Urban

87.5%

12.0%

0.6%

Sex

Male

68.3%

22.6%

9.0%

Female

79.4%

13.3%

7.3%

Age Cohort

Millennial and Gen Z (1836)

77.9%

14.0%

8.0%

Gen X (3752)

67.1%

24.1%

8.8%

Pre-retirement age Boomer (5364)

75.7%

16.3%

8.0%

Retirement age (65+) Caregiver Status

83.4%

11.5%

5.0%

Noncaregiver

73.3%

17.8%

8.8%

Caregiver, youngest child ages 015

79.4%

15.5%

5.1%

Race

White non-Hispanic only

78.1%

10.6%

11.3%

Black, Black multiracial & Black Hispanic

76.5%

15.5%

8.0%

Other

57.7%

40.2%

2.0%

Driver Status

Nondriver

81.5%

15.1%

3.4%

Driver

70.4%

18.7%

10.9%

Mobility Impairment: a "condition or handicap that makes it difficult to travel outside of the home."

Absent

73.9%

17.7%

8.4%

Present

78.7%

15.2%

6.1%

Annual Household Income

<$15,000

80.7%

14.3%

5.0%

$15,000 to $24,999

83.3%

4.5%

12.3%

$25,000 to $34,999

88.6%

11.4%

0.0%

$35,000 to $49,999

79.6%

17.8%

2.6%

$50,000 to $74,999

66.0%

17.6%

16.4%

$75,000 to $99,999

52.5%

30.6%

16.9%

$100,000+

59.1%

28.9%

12.0%

Table continues on next page.

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Table 167. (Continued).

Continued from previous page.

NMT (Walk, Bike, Transit or

Legs by all adults ages 18+

Walk + Other) 74.6%

Transit-like* Other Motorized

17.3%

8.0%

Vehicle Deficit Category of Household

Zero-vehicle

81.7%

15.5%

2.9%

Deficit

77.0%

12.6%

10.4%

Nondeficit (sufficient/surplus)

64.2%

23.7%

12.1%

Educational Attainment

High school or less

74.8%

19.2%

6.1%

Some college or associate degree

82.6%

9.8%

7.6%

Bachelor's or higher

67.3%

21.9%

10.8%

Worker Status

Nonworker

87.9%

9.9%

2.3%

Worker

68.0%

21.1%

10.9%

Occupational Category (Workers Only)

Sales or service

80.2%

15.9%

3.9%

Clerical or administrative support Blue collar

65.7% 91.3%

20.5% 4.6%

13.8% 4.0%

Professional, managerial, or technical

59.1%

26.7%

14.2%

* Transit-like includes paratransit and school, intercity, private, or charter bus. Other motorized includes POV, air,

other, and multiple (not including walk or bike). A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age

22+ in a household with a child of 515 years old.

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than potential drivers.

Blue collar refers to manufacturing, construction, maintenance, or farming.

NMT is the most common transit access/egress mode for every subpopulation examined, with mode shares ranging from 52.5 percent (for travelers with a household income of $75,000$99,999) to 87.9 percent (among nonworkers). There are some differences in mode share by MPO tier, but because the built environment differs strongly within an MPO region, the difference is clearer when looking at neighborhood type. The NMT mode share is highest in urban neighborhoods (87.5 percent) and lowest in small towns and rural areas (62.5 percent). NMT comprises a larger share of transit access/egress legs among women (as compared to men), whites and Blacks (as compared to people of another race), nondrivers versus drivers, and people with mobility impairments versus those without. NMT is a more common access mode among
371

lower-income households compared to those of higher incomes and among nonworkers compared to workers. For travelers from zero-vehicle households, NMT accounts for 81.7 percent of legs, versus 77.0 percent for vehicle-deficit households and 64.2 percent for nondeficit households. Table 168 shows the unweighted sample distribution on which table 167 is based.
372

Table 168. Unweighted sample sizes of public transit access/egress legs.

NMT (Walk, Bike, Transit or

Other

Walk + Other)

Transit-like*

Motorized*

Legs by all adults ages 18+

728

146

76

MPO Tier

1. Atlanta MPO

420

90

52

2. Medium MPOs

212

41

11

3-4. Small MPOs & non-MPO counties

96

15

13

Urbanicity (Neighborhood Type)

Rural & small town

81

21

18

Suburban

282

45

39

Second city

282

71

17

Urban

83

9

2

Sex

Male

320

72

32

Female

408

74

44

Age Cohort

Millennial and Gen Z (1836)

235

38

21

Gen X (3752)

194

52

22

Pre-retirement age Boomer (5364)

188

43

25

Retirement age (65+) Caregiver Status

111

13

8

Noncaregiver

615

130

67

Caregiver, youngest child ages 015

113

16

9

Race

White non-Hispanic only

203

36

31

Black, Black multiracial & Black Hispanic

447

92

43

Other

78

18

2

Driver Status

Nondriver

270

52

18

Driver

458

94

58

Mobility Impairment: a "condition or handicap that makes it difficult to travel outside of the home."

Absent

611

125

68

Present

117

21

8

Annual Household Income

<$15,000

334

64

18

$15,000 to $24,999

79

8

7

$25,000 to $34,999

53

7

0

$35,000 to $49,999

57

10

3

$50,000 to $74,999

41

11

12

$75,000 to $99,999

41

14

11

$100,000+

108

31

25

Table continues on next page.

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Table 168. (Continued).

Continued from previous page.

NMT (Walk, Bike, Transit or

Other

Walk + Other)

Transit-like*

Motorized*

Legs by all adults ages 18+

728

146

76

Vehicle Deficit Category of Household

Zero-vehicle

363

63

14

Deficit

145

27

18

Nondeficit (sufficient/surplus)

220

56

44

Educational Attainment

High school or less

272

60

22

Some college or associate degree

175

22

13

Bachelor's or higher

281

64

41

Worker Status

Nonworker

337

45

18

Worker

391

101

58

Occupational Category (Workers Only)

Sales or service

113

21

4

Clerical or administrative support Blue collar

59

13

12

41

6

1

Professional, managerial, or technical

176

58

38

* Transit-like includes paratransit and school, intercity, private, or charter bus. Other motorized includes POV, air, other,

and multiple (not including walk or bike).

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age

22+ in a household with a child of 515 years old.

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than potential drivers.

Blue collar refers to manufacturing, construction, maintenance, or farming.

Table 169 shows differences in the mean transit access/egress leg duration by mode and demographic groups. Motorized access/egress legs have an average duration more than twice that of nonmotorized legs (18.9 minutes versus 9.2 minutes). Some of the differences in average access/egress leg duration between different groups shown in table 169 are artifacts of differences in the motorized and nonmotorized mode shares of access/egress legs made by members of those groups (table 167). Groups with a higher mode share of motorized legs (e.g., residents of rural areas) will generally have a higher average leg duration than groups with a higher nonmotorized share (e.g., residents of urban areas).

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However, there are also intergroup differences in the duration of legs by any given mode. For example, workers are more likely than nonworkers to access transit using a motorized mode. However, because the average duration of a nonmotorized leg is longer for nonworkers than for workers (10.7 minutes versus 8.2 minutes), the difference in the average leg duration for the two groups is smaller than what the mode share might imply.
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Table 169. Duration in minutes of public transit access/egress legs by demographic characteristics.

NMT (Walk, Bike,

All Legs

Walk + Other)

Motorized*

Legs by all adults ages 18+

11.6

9.2

18.9

MPO Tier

1. Atlanta MPO

11.9

9.3

19.2

2. Medium MPOs

10.0

8.7

15.5

3-4. Small MPOs & non-MPO counties

11.1

8.5

19.0

Urbanicity (Neighborhood Type)

Rural & small town

17.3

13.7

23.2

Suburban

10.7

7.9

20.1

Second city

12.8

10.4

17.8

Urban

9.4

9.2

11.1

Sex

Male

13.4

10.2

20.4

Female

10.2

8.5

17.0

Age Cohort

Millennial and Gen Z (1836)

11.3

8.6

21.0

Gen X (3752)

12.6

10.5

16.9

Pre-retirement age Boomer (5364)

11.6

9.2

19.7

Retirement age (65+)

8.9

7.8

14.7

Caregiver Status

Noncaregiver

11.9

9.5

18.7

Caregiver, youngest child ages 015

10.4

8.2

19.8

Race

White non-Hispanic only

10.8

8.1

20.5

Black, Black multiracial & Black Hispanic

11.9

9.7

19.4

Other

11.4

8.3

15.7

Driver Status

Nondriver

10.2

9.1

15.4

Driver

12.5

9.3

20.2

Mobility Impairment: a "condition or handicap that makes it difficult to travel outside of the home."

Absent

11.7

9.1

19.1

Present

11.2

9.7

17.0

Annual Household Income

<$15,000

11.6

10.2

17.5

$15,000 to $24,999

11.3

9.2

21.5

$25,000 to $34,999

9.6

7.8

23.6

$35,000 to $49,999

9.1

6.4

19.7

$50,000 to $74,999

9.7

6.1

16.7

$75,000 to $99,999

19.9

18.1

21.8

$100,000+

11.3

6.3

18.5

Table continues on next page.

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Table 169. (Continued).

Continued from previous page.
Legs by all adults ages 18+ Vehicle Deficit Category of Household Zero-vehicle Deficit Nondeficit (sufficient/surplus) Educational Attainment High School or less Some college or associate degree Bachelor's or higher Worker Status Nonworker

All Legs 11.6
11.5 11.7 11.7
12.4 9.3 12.7
11.4

NMT (Walk, Bike, Walk + Other) 9.2

Motorized* 18.9

10.1

17.5

10.3

16.4

6.5

21.0

10.9

17.1

7.4

18.2

8.8

20.8

10.7

16.7

Worker

11.7

Occupational Category (Workers Only)

Sales or service

9.8

8.2

19.3

8.2

16.3

Clerical or administrative support Blue collar

10.9

6.3

19.8

8.0

7.8

10.0

Professional, managerial, or technical

13.7

9.1

20.4

* Motorized includes public transit; transit-like (paratransit; school, intercity, private, or charter bus); POV; air; other; and multiple (not including walk or bike). A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household with a child of 515 years old. A vehicle-deficit household owns at least one vehicle, but fewer vehicles than potential drivers. Blue collar refers to manufacturing, construction, maintenance, or farming.

TRAVEL DAY WALKING AND BIKING BY GEORGIA ADULTS
This section describes travel day trips and transit access/egress legs. As discussed in the previous section of this chapter (Access and Egress Travel), access/egress legs for modes other than public transit have been recoded as trips with a purpose of "change mode of transportation."
Georgia adults produce an average of 135 nonmotorized trips and legs per year (table 170). The mean duration for a nonmotorized trip/leg is 16.4 minutes, which averages to 6.0 minutes of walking and biking per person per day.

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Table 170. Nonmotorized trips/legs per capita and duration of NMT trips/legs.

All adults ages 18+ MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO counties Urbanicity (Neighborhood Type) Rural Small town Suburban Second city Urban Sex Male Female Age Cohort Millennial and Gen Z (1836) Gen X (3752) Pre-retirement age Boomer (5364) Retirement age (65+) Caregiver Status Noncaregiver Caregiver, youngest child ages 015 Race White non-Hispanic only Black, Black multiracial & Black Hisp. Other Table continues on next page.

Nonmotorized Trips and Legs per Adult
Annually 134.9

Mean Duration of NMT Trips/Legs
(Minutes) 16.4

Average Daily Minutes of
Walking/Biking* 6.0

158.8

15.3

6.7

133.8

17.3

6.3

96.2

19.1

5.0

90.4

19.0

4.7

72.5

16.9

3.4

79.4

22.8

5.0

175.4

15.7

7.6

170.6

14.3

6.7

587.4

12.9

20.8

140.5

16.9

6.5

129.7

15.8

5.6

154.5

15.6

6.6

134.8

15.9

5.9

133.2

16.0

5.9

97.1

20.5

5.5

146.9

17.4

7.0

108.9

13.5

4.0

122.5

16.2

5.4

154.5

15.4

6.5

139.2

19.8

7.6

378

Table 170. (Continued).

Continued from previous page.

Nonmotorized Trips Mean Duration of Average Daily

and Legs per Adult NMT Trips/Legs

Minutes of

Annually

(Minutes)

Walking/Biking*

All adults ages 18+

134.9

16.4

6.0

Driver Status

Nondriver

309.8

16.1

13.7

Driver

113.2

16.4

5.1

Mobility Impairment: a "condition or handicap that makes it difficult to travel outside of the home."

Absent

132.1

16.5

6.0

Present

161.0

15.5

6.9

Annual Household Income

<$15,000

279.0

17.6

13.4

$15,000 to $49,999

109.5

15.8

4.7

$50,000 to $99,999

88.1

16.3

3.9

$100,000+

141.6

15.5

6.0

Vehicle Deficit Category of Household

Zero-vehicle

688.9

16.4

31.0

Deficit

134.8

16.5

6.1

Nondeficit (sufficient/surplus)

95.8

16.3

4.3

Worker Status

Nonworker

152.0

19.2

8.0

Worker

124.1

14.2

4.8

* Average duration x annual per capita total 365. A caregiver is defined as any adult ages 18+ in a household with a child of less than 5 years old, and any adult ages 22+

in a household with a child of 515 years old.

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than potential drivers.

Residents of urban areas make 587 NMT trips/legs per year; this is more than three times as many NMT trips/legs as second-city and suburban residents, and more than seven times as many trips/legs as residents of small towns and rural areas. The average urban resident spends more than 20 minutes per day walking and/or biking, compared to less than 8 minutes in all other neighborhood types.

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Residents of zero-vehicle households make 689 NMT trips/legs per year. Low income, nonworker and nondriver status, and younger age are all associated with increased NMT trips/legs. Mobility-impaired adults generate more NMT trips/legs per capita than other adults, though the duration of each trip/leg is somewhat shorter. This counterintuitive finding is likely related to the fact that people with mobility impairments disproportionately find themselves in groups that walk more by necessity (low-income, nondrivers, etc.). See chapter 5, Health and Disability for more discussion of the challenges facing people with mobility impairments. Table 171 shows the mode and purpose of NMT trips/legs. Walking accounts for 94.8 percent of nonmotorized travel while biking accounts for 5.2 percent. Biking is less prominent in the Atlanta MPO overall, but more prevalent in the urban neighborhoods within Atlanta, where it accounts for 9.6 percent of nonmotorized travel. Cycling also accounts for a comparatively high proportion of the nonmotorized travel by people in medium MPOs and small towns, men, members of Gen X, low-income people, people with mobility impairments, and residents of zero-vehicle and vehicle-deficit households.
380

Table 171. Mode and purpose of NMT trips/legs by demographic factors.

All adults ages 18+ MPO Tier 1. Atlanta MPO 2. Medium MPOs 3. Small MPOs 4. Non-MPO counties Urbanicity (Neighborhood Type) Rural Small town Suburban Second city Urban Sex Male Female Age Cohort Millennial and Gen Z (1836) Gen X (3752) Pre-retirement age Boomer (5364) Retirement age (65+) Caregiver Status Noncaregiver Caregiver, youngest child ages 015 Race White non-Hispanic only Black, Black multiracial & Black Hisp. Other Table continues on next page.

Annual
NMT Trips and Legs per Adult 134.9
158.8 133.8
96.2 90.4
72.5 79.4 175.4 170.6 587.4
140.5 129.7
154.5 134.8 133.2
97.1
146.9 108.9
122.5 154.5 139.2

Mode*

Purpose

Bike 5.2%
2.8% 10.5%
6.1% 10.1%
3.4% 8.7% 4.3% 3.3% 9.6%
6.8% 3.7%
5.0% 7.2% 4.9% 1.9%
5.0% 5.8%
6.7% 3.0% 5.8%

Walk 94.8%

Leisure 26.2%

Instrumental
73.8%

97.2% 89.5% 93.9% 89.9%

25.2% 22.2% 27.4% 34.6%

74.8% 77.8% 72.6% 65.4%

96.6% 91.3% 95.7% 96.7% 90.4%

34.6% 37.1% 22.8% 23.8% 17.3%

65.4% 62.9% 77.2% 76.2% 82.7%

93.2% 96.3%

27.8% 24.5%

72.2% 75.5%

95.0% 92.8% 95.1% 98.2%

21.0% 25.7% 29.2% 39.0%

79.0% 74.3% 70.8% 61.0%

95.0% 94.2%
93.3% 97.0% 94.2%

27.8% 21.5%
32.8% 15.1% 31.4%

72.2% 78.5%
67.2% 84.9% 68.6%

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Continued from previous page.

Table 171. (Continued).

Annual

Mode*

Purpose

NMT Trips and Legs per Adult

Bike

Walk

Leisure

Instrumental

All adults ages 18+

134.9

5.2%

94.8%

26.2%

73.8%

Driver Status

Nondriver

309.8

6.8%

93.2%

12.6%

87.4%

Driver

113.2

4.7%

95.3%

30.8%

69.2%

Mobility Impairment: a "condition or handicap that makes it difficult to travel outside of the home."

Absent

132.1

4.9%

95.1%

27.1%

72.9%

Present

161.0

7.6%

92.4%

19.1%

80.9%

Annual Household Income

<$15,000

279.0

8.7%

91.3%

15.8%

84.2%

$15,000 to $49,999

109.5

3.4%

96.6%

24.4%

75.6%

$50,000 to $99,999

88.1

2.6%

97.4%

35.6%

64.4%

$100,000+

141.6

Vehicle Deficit Category of Household

5.1%

94.9%

32.7%

67.3%

Zero-vehicle

688.9

8.0%

92.0%

8.3%

91.7%

Deficit

134.8

7.2%

92.8%

23.7%

76.3%

Nondeficit (sufficient/surplus)

95.8

2.8%

97.2%

36.5%

63.5%

Worker Status

Nonworker

152.0

4.7%

95.3%

28.1%

71.9%

Worker

124.1

5.6%

94.4%

24.7%

75.3%

Row percentages shown.

* Includes multimodal access/egress legs. One walk+bike egress leg is included in both the walk and bike columns. Leisure includes nonloop and loop trips with the purposes of fitness and leisure. Instrumental includes all other trip/leg

purposes.

A caregiver is defined as any adult ages 18+ in a household with a child of less than 5 years old, and any adult ages 22+ in

a household with a child of 515 years old.

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than potential drivers.

The purpose of NMT varies for different groups of people, as well. Overall, 26.2 percent of NMT trips/legs are for leisure: recreation and fitness.106 The remaining 73.8 percent of trips/legs

106 As discussed elsewhere, recreation and fitness trips are a mix of trips where the purpose of the trip itself is leisure and trips to access a leisure destination (e.g., a gym or movie theater).
382

are instrumental trips taken for the purpose of accessing a destination (work, school, shopping, public transit, etc.).

Many of the same groups that have elevated numbers of NMT trips/legs per capita also devote a higher percentage of those trips to instrumental travel. More than 90 percent of NMT trips/legs by people from zero-vehicle households are for instrumental purposes, versus just 63.5 percent of the NMT by people from nondeficit households. Nondrivers, low-income people, millennials and Gen Zers, people with mobility impairments, and residents of urban neighborhoods likewise report more NMT, with a higher share of it devoted to instrumental travel.

In contrast, men and childfree adults all report higher-than-average rates of NMT, but with a higher than usual share of it devoted to leisure.

Table 172 compares the duration of leisure and instrumental trips/legs by walking and biking. Bike trips/legs are longer than walk trips/legs on average, though the difference is larger for leisure travel than for instrumental travel.

Table 172. Mean duration in minutes of leisure and instrumental NMT trips/legs by Georgia adults.

All purposes Leisure (recreation and fitness) Instrumental (all other purposes)

All NMT 16.4 25.5 13.1

Walk 16.1 25.1 12.7

Bike 21.8 37.8 19.1

Table 173 provides a more detailed breakdown of the purposes of NMT trips and legs. Among instrumental NMT trips/legs, the most common purposes are to access/egress public transit (20.6 percent), return home (19.0 percent) and household-serving travel (11.1 percent). Compared to walking trips, a higher percentage of bike trips are for instrumental travel
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(85.6 percent versus 73.2 percent). The higher percentage of return-home trips by bicycle suggests a number of possible explanations; perhaps cyclists are not trip chaining or stopping along the way as often as pedestrians, or pedestrians are more likely to start a walking circuit from a location besides their home. The more detailed purposes of bicycle trips should be treated with caution, as some of the unweighted cell sizes are quite small (see table 174).

Table 173. Purpose and duration of NMT trips/legs.

All purposes Leisure Recreation nonloop Fitness nonloop Loop recreation or fitness Instrumental Access/egress public transit (legs) Work School Medical Household-serving Change mode of transportation

Percent of Trips/Legs (Column Percent)

All NMT

Walk*

Bike*

100%

100%

100%

26.2%

26.8%

14.4%

3.0%

2.9%

3.9%

7.2%

7.3%

6.8%

16.0%

16.7%

3.8%

73.8%

73.2%

85.6%

20.6%

21.4%

6.5%

6.3%

6.2%

7.2%

2.2%

2.3%

1.3%

0.4%

0.4%

0.1%

11.1%

10.7%

18.5%

4.1%

4.3%

0.5%

Mean Duration (Minutes) All NMT 16.4 25.5 14.2 14.6 32.5 13.1 9.2 12.4 20.9 23.8 13.3 9.3

Discretionary: dining, visit

friends/relatives

8.9%

8.7%

13.4%

12.1

Community, volunteer and religious

activities

1.0%

1.0%

0.2%

19.7

Return home

19.0%

18.0%

37.8%

17.4

Other

0.1%

0.1%

0.0%

23.7

* Includes walk + other and bike + other legs. One egress leg with the modes walk + bike is included in both the walk and bike

columns (but only counted once when combining all NMT trips and legs). Loop trips with a purpose of home or work-from home have been recoded as recreation/fitness. However, since it is not

possible to distinguish between recreation and fitness loops, loop recreation/fitness is listed as its own subcategory. Loop trips

with an instrumental purpose are included with that purpose. See chapter 7 for more details on loop trip classification. Includes buy goods and services, general errands, transport others, and accompany others.

Trips to change mode of transportation and access/egress legs for modes besides public transit (e.g., airplane, ferry, long-

distance train, etc.).

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Table 174 contains the unweighted data on which table 173 is based. Table 174. Unweighted trips/legs by mode and purpose.
Time of Day Figure 39 shows the start times of NMT trips/legs on weekdays and weekends. The figure on the left depicts the annual total in millions, and the figure on the right shows trips as a percentage of weekday or weekend trips. Table 175 and table 176 show the weighted and unweighted data on
385

which these figures are based. While there are gentle peaks around 9:00 a.m. and 5:00 p.m., trips/legs are distributed throughout the day.
Figure 39. Bar graphs. Weekday and weekend NMT by time of day. 386

Table 175. Weighted NMT trips/legs by time of day 387

Table 176. Unweighted NMT trips/legs by time of day. 388

Table 177 shows the time of day by day of week and MPO tier, collapsed into 3-hour categories (and a 6-hour overnight period). Cyclists and pedestrians get a slightly later start on the weekends.107

Table 177. Time of day of NMT trips/legs by day and MPO tier.

All

Day of Week

All NMT Trips/Legs

Weekday Weekend

Weighted Column Percentages

12:005:59 am

2.0%

1.8%

6:008:59 am

15.4%

16.7%

9:0011:59 am

20.4%

19.0%

12:002:59 pm

19.7%

20.3%

3:005:59 pm

22.0%

22.2%

6:008:59 pm

15.3%

15.3%

9:0011:59 pm

5.3%

4.7%

Unweighted Sample Sizes

All times of day

4,830

4,127

12:005:59 am

74

60

6:008:59 am

787

692

9:0011:59 am

998

833

12:002:59 pm

938

817

3:005:59 pm

1,079

924

6:008:59 pm

729

627

9:0011:59 pm

225

174

Unweighted Column Percentages

12:005:59 am

1.5%

1.5%

6:008:59 am

16.3%

16.8%

9:0011:59 am

20.7%

20.2%

12:002:59 pm

19.4%

19.8%

3:005:59 pm

22.3%

22.4%

6:008:59 pm

15.1%

15.2%

9:0011:59 pm

4.7%

4.2%

Includes all trips and legs by walking, biking, or walk + other.

2.6% 11.1% 24.9% 17.7% 21.5% 15.2%
7.1%
703 14 95
165 121 155 102
51
2.0% 13.5% 23.5% 17.2% 22.0% 14.5%
7.3%

Tier 1 Atlanta
2.3% 16.3% 19.6% 19.5% 21.1% 15.9%
5.2%
1,870 39
324 360 373 385 290
99
2.1% 17.3% 19.3% 19.9% 20.6% 15.5%
5.3%

MPO Tier

Tier 2 Medium
MPOs

Tier 3 Small MPOs

1.8% 13.3% 24.6% 19.3% 22.0% 13.6%
5.2%
1,742 24
267 387 331 394 261
78
1.4% 15.3% 22.2% 19.0% 22.6% 15.0%
4.5%

0.6% 14.3% 19.8% 22.3% 23.1% 16.4%
3.5%
840 8
131 180 156 199 136
30
1.0% 15.6% 21.4% 18.6% 23.7% 16.2%
3.6%

Tier 4 NonMPO Counties
1.4% 14.0% 19.5% 19.6% 25.7% 13.4%
6.3%
378 3
65 71 78 101 42 18
0.8% 17.2% 18.8% 20.6% 26.7% 11.1%
4.8%

107 The weekend trips/legs in the overnight period are primarily in the 12:0012:30 a.m. hour, while those on weekdays are more evenly divided between late night and early morning.
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CAPTIVE AND CHOICE NONMOTORIZED TRAVEL As with public transit, some pedestrians and cyclists choose to walk and bike, and others use these modes by necessity. This section compares the nonmotorized travel of captive and choice travelers.
We use linear regression to examine the time penalties incurred by captive pedestrians and cyclists as compared to choice pedestrians/cyclists. We model the total minutes of walking/biking on the travel day for leisure (fitness and recreation) and instrumental purposes (all other purposes) as a function of being a captive traveler and a number of control variables. Unless otherwise stated, descriptive statistics are weighted using values provided by NHTS, and models are based on unweighted data.
Defining Captive Travel We base our definition of captive travel on mode, household vehicle ownership, and income. This allows us to differentiate the needs of travelers who are carless by necessity from those who are car-free by choice (Brown 2017). We also include transit and nonmotorized trips by residents of low- and moderate-income vehicle-deficit households, or households that own at least one automobile but not enough for each potential driver in the household (Blumenberg, Brown, and Schouten 2018). In this study, a potential driver is any household resident ages 16+.108 In Georgia, these households substantially outnumber carless households, and because
108 The researchers chose to use all individuals ages 16+ because the NHTS defines drivers by asking "does this person drive?" Whether or not a person drives may be in part determined by the availability of a vehicle.
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the family car is not available for all trips, some household members will obligatorily use transit or nonmotorized modes. Table 178 shows vehicle sufficiency among Georgia households earning less than $50,000 per year and those earning $50,000 or more per year. We chose this threshold because it is the closest approximation of Georgia's median income (estimated by the American Community Survey to be $55,679 for the years 20142018) achievable with NHTS categorical income data. We conducted sensitivity analysis using different income cutoffs, and the models we present were found to be robust. Georgians who have sufficient income largely choose to have a vehicle for each driver. Among households earning at least $50,000 per year, vehicle ownership is nearly universal; 99.5 percent of households own at least one vehicle. Vehicle-deficit households are also somewhat uncommon (12.7 percent), and just 0.5 percent have chosen to be car-free. While the majority of households in the lower half of the income distribution also own cars, 38.1 percent of these households are vehicle-deficit or carless. Additionally, higher-income vehicle-deficit households are more likely to have a vehicle deficit due to the presence of a driver under the age of 18 (10.9 percent of vehicle-deficit households earning at least $50,000 versus 3.3 percent of those earning less than $50,000).
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Table 178. Vehicle sufficiency of Georgia households and adults.
In this analysis, a captive nonmotorized trip is a walking or biking trip by a traveler who lives in a captive household. A captive household meets the following criteria:
1. The household is carless or vehicle-deficit. We include vehicle-deficit households in the definition of captive travel because, while a car is available to some household members for some trips, for other trips, household members will have more limited options.
2. The household earns less than $50,000 per year. The income criterion is designed to screen out travel by people who are "car-free" by choice versus those who are financially unable to afford vehicles for every potential driver in the household.
Model Structure We model the total amount of NMT on the travel day based on the sample of people who had at least one nonmotorized trip or leg. We estimate one model for leisure travel (fitness and
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recreation) and one model for instrumental travel (all other purposes). The dependent variable for each model is the total time in minutes spent on nonmotorized travel of that type. The leisure model includes fitness and recreation trips. The instrumental model includes trips for all other purposes, as well as time spent walking/biking to and from transit stations.109
Per-person totals are modeled rather than individual trips because, unlike transit trips, nonmotorized trips are easily subdivided by travelers in ways that produce idiosyncrasies in the data. Consider, for example, two respondents who walk to work past a coffee shop. The choice pedestrian, with more disposable income, may choose to stop, while a lower-income captive pedestrian will walk past, regardless of her desire for coffee. To avoid the kind of "apples to oranges" comparisons that can result from such differences in whether respondents stop along the way, the research team chose to model the daily total duration of nonmotorized travel while controlling for purpose and number of trips.
We separate leisure travel from instrumental travel because we believe different processes guide people's choices to walk for leisure or instrumental purposes. While in most cases longer trips are considered a disutility, for some recreational and fitness trips the longer duration is part of the purpose. Separating the two forms of travel allows the models to accommodate the general tendency to try and minimize instrumental travel time but maximize leisure time (including leisure walking and biking). It also allows us to incorporate the amount of time spent walking for nonleisure purposes as an independent variable into the model, explaining the amount of time people spend walking for leisure.
109 As discussed in Travel Day Walking and Biking by Georgia Adults of this chapter, the NHTS does not report access/egress nonmotorized travel as separate "trips," so this report describes them as "legs."
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Multiple specifications were explored for each model; the effects of captive travel described here were consistent in both magnitude and significance across alternate specifications. Results As shown in table 179, captive pedestrians and cyclists make an average of 3.4 nonmotorized trips and legs per day, versus 2.3 for choice pedestrians and cyclists. Choice pedestrians and cyclists are more likely to use NMT for leisure (58 percent, versus 30 percent of captive travelers), and somewhat less likely to use NMT for instrumental purposes. Among instrumental purposes, work and school are more common destinations for choice pedestrians and cyclists than for captive travelers, who more commonly use nonmotorized travel for household-serving travel and accessing public transit.
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Table 179. Duration, quantity, and purpose of nonmotorized travel for captive and choice travelers.

All Nonmotorized Travelers

Captive* Nonmotorized Travelers

Choice Nonmotorized Travelers

At least one walk trip or leg

97.1%

95.6%

97.8%

At least one bike trip or leg Percent with any instrumental NMT

4.4% 73.2%

7.2% 84.4%

3.2% 68.2%

Percent with any leisure (fitness or recreation) NMT

49.6%

30.6%

58.1%

Mean NMT trips and legs, all purposes

2.64

3.42

2.29

Mean walk trips and legs, all purposes

2.50

3.17

2.21

Mean bike trips and legs, all purposes

0.14

0.25

0.08

Total NMT minutes

39.91

51.33

34.80

Leisure NMT minutes

15.37

9.97

17.78

Instrumental NMT minutes

24.55

41.36

17.02

Percent of Travelers using NMT for Each Purpose

Recreation nonloop

7.2%

3.7%

8.7%

Fitness nonloop

15.2%

9.3%

17.9%

Loop recreation or fitness

29.5%

19.6%

33.9%

Access/egress public transit (legs)

16.1%

29.5%

10.1%

Work

13.8%

10.4%

15.3%

School

3.7%

1.6%

4.6%

Medical Household-serving Change mode of transportation

0.8% 20.3%
5.1%

1.6% 32.8%
3.7%

0.5% 14.8%
5.7%

Discretionary: dining, visit friends/relatives

20.7%

22.0%

20.1%

Community, volunteer, and religious activities

2.5%

2.9%

2.3%

Return home

49.3%

49.3%

35.4%

* From household with fewer vehicles than potential drivers (incl. zero vehicles) and annual household income <$50,000.

Instrumental NMT includes trips for any purpose besides fitness or leisure, along with public transit access/egress legs.

Includes buy goods and services, general errands, transport others, and accompany others. Trips to change mode of transportation and access/egress legs for modes besides public transit (e.g., airplane, ferry, long-distance train, etc.).

Table 180 shows the effect of being a captive traveler on the total amount of time spent walking and/or biking for instrumental purposes over the course of the day. Being a captive traveler does not, by itself, have a significant effect on the amount of time spent walking or biking. However,
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it increases the expected duration of each expected walk trip by just over 3 minutes. While there was no significant interaction between captivity and the duration of bike trips or transit access/egress legs, the fact that captive users tend to make more NMT trips and legs for instrumental purposes results in a longer overall expected duration of instrumental NMT. Additionally, using a private auto at any point on the travel day is associated with a 6-minute reduction in the duration of nonmotorized instrumental travel. While this reduction applies to captive and choice travelers alike, choice travelers are more likely to have used a POV at some point on the travel day.
In terms of trip purpose, school travel, medical travel, and travel to return home have the longest durations. The coefficient on medical trips is not significant, but as this trip was the least common (reported by just 23 respondents), it may be worth revisiting the issue of nonmotorized medical travel with a larger or more targeted dataset.
Table 181 models the total duration of leisure (recreation and fitness) NMT. The amount of instrumental (nonleisure) NMT is negatively associated with the amount of leisure travel. This may reflect the fact that travel that does "double duty" as both instrumental and leisure will be recorded as instrumental. For example, a decision to walk to a nearby coffee shop rather than drive will be recorded by the purpose at the destination (in this case, dining). However, it likely also reflects a reduced availability of leisure time. In particular, the duration of a recreational trip to wander around a local park will be constrained by the traveler's amount of free time to, say, choose a more circuitous route or pause to admire wildlife.
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As per the descriptive statistics in table 179, the average minutes of leisure NMT is lower for captive users than for choice users.110 However, after controlling for time spent on instrumental nonmotorized travel, captivity does not have a significant effect. It seems a key reason captive nonmotorized travelers spend less time on leisure NMT is because they spend more on instrumental NMT.
110 Interactions between captivity and number, purpose, and mode of trips were also found to be insignificant and were therefore excluded from the final model.
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Table 180. Linear regression: Daily total minutes of instrumental walking and biking.

Captive traveler Used POV on travel day Used public transit on travel day Instrumental walk trip (excluding public transit access legs)
Captive* walk trip Instrumental bike trip (excluding public transit access legs)
Captive* bike trip NMT leg to access/egress public transit
Captive* NMT transit access/egress leg Purpose(s) of nonmotorized trips (1 = yes)
Work (commute or work-related business) School Medical Household-serving (transport other, buy goods/services, general errands) Change mode of transportation, including access/egress non-transit mode Discretionary: dining, visit friends/relatives Community, volunteer and religious activities Return home Female Race (reference: white non-Hispanic only) Black and Black multiracial Other Annual household income (reference: $100,000+) <$15,000 $15,000 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 Mobility impairment Age Caregiver for child ages 04 Neighborhood type (reference: rural) Small town Suburban Second city Urban Weekend Constant

Coefficient 1.12 -6.14 -5.79 6.59 3.35
11.32 -1.78 8.62 0.26
5.96 18.09 13.94
4.47 2.91 5.45 9.72 11.16 -1.72
1.34 2.11
4.88 -1.70 -5.42 -3.74 -2.24 0.88 1.30 -0.07 -5.85
3.55 3.61 4.16 0.46 2.69 3.32

P-value 0.737 0.002 *** 0.090 * 0.000 *** 0.005 *** 0.000 *** 0.587 0.000 *** 0.849
0.011 ** 0.003 *** 0.130 0.035 ** 0.312 0.016 ** 0.004 *** 0.000 *** 0.227
0.477 0.450
0.135 0.613 0.023 ** 0.080 * 0.264 0.654 0.719 0.165 0.001 ***
0.108 0.120 0.064 * 0.873 0.198 0.467

Model indicators: N = 1,288 R 2 = 0.471 Adjusted R 2 = 0.457

From household with fewer vehicles than potential drivers (incl. zero vehicles) and annual household income <$50,000.

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Table 181. Linear regression: Daily total minutes of leisure walking and biking.

Leisure Travel (New, with Loop Separated)

Captive traveler Any instrumental NMT Minutes of instrumental NMT Minutes2 of instrumental NMT Used POV on travel day Used public transit on travel day Walk recreation legs (nonloop) Bike recreation legs (nonloop) Walk fitness legs (nonloop) Bike fitness legs (nonloop) Walk loop leisure travel (fitness or recreation) Bike loop leisure travel (fitness or recreation) Female Race (reference: white non-Hispanic only)
Black and Black multiracial Other Annual household income (reference: $100,000+) <$15,000 $15,000 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 Mobility impairment Age Caregiver for child ages 04 Neighborhood type (reference: rural) Small town Suburban Second city Urban Weekend Constant

Coefficient -2.72
-15.59 0.50
-2.72E-03 -3.64 -5.52 -0.16 1.44 1.78 3.88 12.48 36.04 3.32
0.24 1.26
6.43 2.16 0.33 -2.35 0.41 4.32 -9.99 0.10 -1.14
-2.16 -1.63 -2.99 -0.69 4.14 21.06

P-value 0.394 0.000 *** 0.000 *** 0.001 *** 0.121 0.238 0.943 0.830 0.374 0.298 0.000 *** 0.001 *** 0.048 **
0.937 0.667
0.123 0.549 0.903 0.418 0.874 0.105 0.002 *** 0.050 * 0.723
0.448 0.585 0.305 0.865 0.063 * 0.000 ***

Model indicators: N = 1,059 R 2 = 0.228 Adjusted R 2 = 0.206

From household with fewer vehicles than potential drivers (incl. zero vehicles) and annual household income <$50,000.

Nonmotorized travel for a purpose other than fitness or leisure.

Since promoting walking and cycling are often pursued as public health interventions, it is also important to note that the association between health and nonmotorized travel is not the same for

399

captive and choice travelers (results not tabulated). Among choice pedestrians and cyclists who walked or biked on the travel day, 96.1 percent consider themselves to be in good, very good, or excellent health. This is slightly higher than the rate among residents of similar households who did not walk or bike (92.3 percent). However, only 76.7 percent of captive pedestrians and cyclists consider themselves to be in those categories of good health. Not only is this lower than the rate among choice pedestrians, it is also lower than the rate among residents of captive households who did not walk or bike (79.2 percent).
Captive pedestrians and cyclists spent more time traveling than their choice counterparts did, after controlling for trip quantity and purpose. Captive nonmotorized travelers also tended to make more nonmotorized trips, compounding the differences in total travel time. While increased walking and biking is broadly considered a public health goal, it is important to remember that some travelers are already walking or riding more than they would like. However, the health benefits of increasing walking and biking may not accrue equally to captive nonmotorized travelers. Not only were captive pedestrians and cyclists less likely to be in good health than choice pedestrians and cyclists, they were also less likely to be in good health than travelers from captive households who did not walk and bike. While this finding is descriptive, it suggests the need for follow-up study to examine differences in the relationship between nonmotorized travel and health among captive pedestrians and cyclists. As with transit, there may be equity issues related to access to walking and biking infrastructure and other streetscape features that are known to affect wellbeing, as well as exposure to air pollution (Maurer Braun, Read, and Ricklin 2016).
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CHILDREN'S NONMOTORIZED AND SCHOOL TRAVEL This section discusses children's nonmotorized travel, as well as their travel to and from school. Walking and biking historically accounted for a large portion of school travel, though this is less true today. The section begins with a discussion of how children's school travel has been identified in this study. Identifying School Trips In addition to children's reported "usual" modes of travel to and from school, we examine school travel on the travel day. A school trip is a trip to school at the start of the school day and a trip home from school, typically after classes and extracurricular activities have concluded. The methods used to identify school travel are closely related to the techniques for identifying work journeys described in chapter 2, in that we consider trips with the purpose of school and trips to and from the school location, some of which may not have a listed purpose of school. Most school journeys (82 percent) are simple: directly from home to school or school to home. Figure 40 provides a sample itinerary to illustrate how we categorize more complex travel patterns.
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Figure 40. Diagram. Example child's travel itinerary with trips to and from school.
This child travels from home to school. Her class then embarks on a field trip to a local museum. At the end of the field trip, the child returns to the school to change her mode of transportation. She goes to a friend's house for a few hours, and then returns home. Following the model of constructing work journeys precisely, trip 1 would be a simple school journey and trips 4 and 5 would be a complex school journey. Trips 2 and 3, from the school to a nonhome location and back, would not be part of a school journey. Because the goal is not to quantify the duration of school travel, the researchers' needs here are somewhat simpler than they are in chapter 2. For simplicity, then, in complex school journeys, the research team identifies them by the leg that is connected to the school location (the last leg of a journey to school or the first leg of a journey from school). In this example, trips 1 and 4 would be considered school travel.
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It is important to note that the definition of school travel used in this section differs from trips with the purpose of school reported elsewhere. Trip purpose is defined by purpose at destination; as a result, all trips from school to home are classified as having a purpose besides school. Ninety percent of school trips identified here have a purpose of either school or home, with the remaining 10 percent split between purposes such as running errands, visiting friends or relatives, leisure, etc.
The following steps provide a few more technical details. Some readers may wish to proceed to the next section.
Step 1: In identifying travel to and from school for children ages 517, the researchers initially included all trips that started or ended at the respondent's school location, all trips with the listed purpose of school, and all trips where the listed origin-purpose (the primary activity at the origin location) was school.
Trips to school: A total of 1,412 trips with a destination and/or purpose of school were identified. Of those, 54 trips were screened out because they were loops or because the origin and destination purposes were both school; 1,290 trips with a purpose of school that did not originate from the school location were identified as school trips; and 68 trips to the school location with a different listed purpose were flagged for manual review.
Trips from school: A total of 1,440 trips with an origin location or origin purpose of school were identified. Of those, 56 were disqualified because they were loops or the origin and destination purposes were both school. A total of 1,311 trips were classified as trips from school based on having an origin-purpose of school and nonschool destinations
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and destination-purposes. Seventy-three trips from the school location with a nonschool origin-purpose were flagged for manual review. Step 2: Flagged trips from step 1 were reviewed manually based on other trips in each child's travel itinerary; see chapter 2 for an example of the kinds of factors considered. The researchers identified one additional trip to school and six trips from school. The remainder were disqualified as school trips. Step 3: To further screen out field trips and similar travel from school, the total number of school trips for each child was calculated. Seventeen children were listed as having three or more school trips (in the single travel day measured by the diary). These children's trips were manually reviewed. As a result, six trips to school and nine trips from school were disqualified, leaving 1,285 trips to school, 1,308 trips from school, and 3,967 nonschool trips. As in the rest of this chapter, this analysis of school travel additionally screened out 8 children who did not answer questions about frequency of nonmotorized travel. Table 182 shows the mode of trips to and from school.
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Table 182. Mode of travel-day trips to and from school by children ages 517 (unweighted).

Additionally, respondents (or their parents) were asked about how they "usually get to school" (table 183). These two data sources are used for the analysis.

Table 183. Usual mode of travel to and from school by children ages 517 (unweighted).

Usual Mode.... Walk Bike POV School bus Public or paratransit Other bus (e.g., intercity, private, or charter) Vehicle for hire (taxi, ridehail, limo) Air Other Total (all modes)

To School

87

(3.8%)

9

(0.4%)

1,190

(52.5%)

958

(42.3%)

8

(0.4%)

2

(0.1%)

(0.0%)

1

(0.0%)

11

(0.5%)

2,266

From School

100

(4.4%)

9

(0.4%)

995

(43.9%)

1,122

(49.5%)

6

(0.3%)

16

(0.7%)

(0.0%)

(0.0%)

18

(0.8%)

2,266

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Due to the small sample size of school travel by bicycle, the remaining analysis combines walking and biking into the more general category of nonmotorized travel.

Children's School Travel Table 184 shows children's observed (travel day) and usual modes of travel to and from school.

Table 184. School travel: Mode split of trips and usual mode(s).

Travel Day School Trips N=2,593 Trips

Usual School Mode N=2,273 Children

To and/or

To

From

Total

To

From From*

Nonmotorized (walk or bike)

4.2%

4.9%

4.6%

3.9%

4.5%

5.4%

POV

49.5%

47.0%

48.2%

44.8%

37.6%

47.6%

School bus

45.0%

46.7%

45.8%

49.7%

55.2%

58.8%

Other

1.3%

1.4%

1.4%

1.6%

2.7%

3.0%

Weighted column percentages shown.

* Mode is reported as usual mode of transportation either to school, from school, or both. Because 14.9 percent of children have a different usual mode to and from school, this column sums to more than 100 percent.

School bus is the most commonly reported "usual" mode of transportation to and from school; 58.8 percent of caregivers reported that their child usually takes the bus to school, from school, or both. The second most common usual school travel mode is POV, which is the usual mode to and/or from school for 47.6 percent of children. In contrast, for observed (travel day) school travel, the private auto eclipses school bus, accounting for 48.2 percent of trips (versus 45.8 percent by school bus). Students' day-to-day travel diverges, to some degree, from their caregivers' description of their "usual" travel behavior.
For both observed and "usual" mode of school travel, nonmotorized travel comes in a distant third. Depending on which measure is used, nonmotorized travel accounts for slightly more or

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slightly less than 5 percent of school travel. The mode share of nonmotorized travel is higher in the afternoon (trips home from school) than in the morning; some children arrive to school by auto or bus and then walk home in the afternoon. As shown in table 184, 4.6 percent of observed school trips are by walking and biking. 5.4 percent of children are listed as usually using nonmotorized travel in at least one direction (0.9 percent only to school, 1.5 percent only from school, and 3.0 percent in both directions). Table 185 shows demographic differences in the prevalence of nonmotorized school travel.
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Table 185. Walking and biking as percentage of school travel by geography, age, driver status, sex, race, income, and household vehicles.

Travel Day School Usual School Mode Trips (To and From) (Either Direction)

N=2,593 Trips

N=2,273 Children

All children 517

4.6%

5.4%

MPO Tier

1. Atlanta MPO

4.9%

5.5%

2. Medium MPOs

7.2%

8.1%

3. Small MPOs

3.1%

4.4%

4. Non-MPO counties

2.6%

3.5%

Urbanicity (Neighborhood Type)

Rural

1.2%

0.6%

Small town

2.1%

3.5%

Suburban

7.4%

7.2%

Second city

6.6%

9.8%

Urban

18.6%

22.2%

Age and Driver Status

59 1013 1415 1617 Nondriver, ages 1617 Driver, ages 1617

5.7%

5.8%

5.7%

5.6%

1.2%

6.1%

2.8%

3.5%

3.9%

5.8%

1.3%

1.2%

Sex

Male

5.2%

5.1%

Female

3.9%

5.8%

Race

White non-Hispanic only

2.3%

3.3%

Black, Black multiracial & Black Hispanic

7.0%

7.1%

Other

4.6%

7.6%

Annual Household Income

<$15,000

4.2%

7.5%

$15,000 to $49,999

6.6%

6.6%

$50,000 to $99,999

2.5%

3.6%

$100,000+ Vehicle Deficit Category of Household

4.1%

5.4%

Zero-vehicle

8.4%

8.9%

Deficit

8.0%

8.3%

Nondeficit (sufficient/surplus)

3.0%

4.0%

Note: Trips denotes the percentage of all school trips (in either direction) that was made by walking or biking by children in the row category. Usual school mode depicts the percentage of children in each row category who "usually" walk or bike to school, from school, or both.

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than potential drivers.

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Walking and biking to/from school is most common in medium-MPO counties, where they account for 7.2 percent of observed school trips and are listed as the usual mode of school travel in at least one direction for 8.1 percent of children. It is the least common in non-MPO counties, where just 3.5 percent of children usually walk or bike to or from school and NMT accounts for just 2.6 percent of observed school trips. While nonmotorized school travel is not quite as common in the Atlanta MPO compared to smaller MPOs, it is much more prevalent in the dense urban neighborhoods only found in Atlanta; 18.6 percent of school trips in these neighborhoods are by walking or biking.
Walking and biking are less common among children who are old enough to drive (ages 1617). However, this effect mainly applies to children who are themselves drivers. Nondrivers ages 1617 are comparatively more likely to walk/bike to or from school. White children are also less likely to walk to school than children of other races.
Children in zero-vehicle households and vehicle-deficit households are more likely to walk or bike to school than children in nondeficit households. There is a dip in walking/biking among children in upper-middle income households (earning $50,000$99,999 per year) compared to both poorer and wealthier households. This may be a sign that walkability is an amenity for some high-income families.
Children's Nonmotorized Travel for All Purposes We turn now from school travel to nonmotorized travel for any purpose. Table 186 shows the purposes of nonmotorized trips made by children, as well as access/egress legs.111 Because the
111 Access/egress legs were added to the data following the methods that were described with regard to adults in Access and Egress Travel in this chapter.
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most common purposes of children's NMT differ from those of adults, rather than duplicate the adults' categories, the research team has created categories more tailored to children's observed travel. We divide children's NMT into four types of purposes. Travel for "fun" purposes like leisure and socialization (i.e., exercise, recreation, visiting friends and relatives, and dining out) account for a plurality of children's nonmotorized travel (35.4 percent). Trips with the purpose of school account for 7.0 percent of children's NMT.112 Other instrumental purposes (such as changing mode of transportation, household-serving travel, work, community, volunteer, and religious activities) account for 27.6 percent, and the remaining 30.0 percent of trips and legs have the purpose of returning home.
112 As opposed to the definition of school travel used in Children's School Travel in this chapter, here, we are returning to the standard method of defining the purpose of a trip by its destination. School trips (both to and from school) account for 14.5 percent of children's nonmotorized trips and legs.
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Table 186. Purpose of children's nonmotorized trips (weighted and unweighted).
Table 187 shows variations in the purpose of nonmotorized trips between different subpopulations of children. Unweighted sample sizes of nonmotorized trips by each group are included in the right-most column to allow the reader to cautiously interpret findings about groups with especially small sample sizes (e.g., children in urban neighborhoods, who made a total of 23 reported nonmotorized trips on the travel day). Leisure and socialization account for more than half of children's nonmotorized travel in rural and small-town neighborhoods (51.9 percent and 51.5 percent, respectively), as well as in nonMPO counties more generally (51.5 percent). Elsewhere in the state, this travel for "fun" purposes is outnumbered by other instrumental travel.
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"Fun" purposes account for a higher share of nonmotorized travel by children 1015 years old as compared to older and younger children. However, while 1617-year-old nondrivers devote just 19.8 percent of their nonmotorized trips to leisure and socialization, 60.5 percent of nonmotorized trips by their peers who drive are devoted to leisure and socialization. This is largely because children who can drive begin making fewer nonmotorized trips overall.
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Table 187. Purpose of children's nonmotorized trips by geography, age, driver status, sex, race, income, and household vehicle ownership.

Purposes of Nonmotorized Trips (Weighted Row Percentages)

Leisure and Attend School Other

Return

Socialization or Daycare Instrumental Home

All children ages 517

35.4%

7.0%

27.6%

30.0%

MPO Tier

1. Atlanta MPO

33.6%

7.3%

28.8%

30.4%

2. Medium MPOs

27.2%

10.3%

28.0%

34.5%

3. Small MPOs

36.6%

1.8%

26.4%

35.2%

4. Non-MPO counties

51.5%

5.9%

23.2%

19.3%

Urbanicity (Neighborhood Type)

Rural

51.9%

1.0%

20.8%

26.3%

Small town

51.5%

6.1%

13.8%

28.7%

Suburban

30.2%

9.3%

32.1%

28.5%

Second city

25.3%

6.2%

36.1%

32.4%

Urban

15.3%

15.2%

23.1%

46.5%

Age

59

27.5%

10.6%

30.3%

31.5%

1013

40.3%

8.6%

19.7%

31.3%

1415

43.1%

2.3%

18.4%

36.2%

1617

33.1%

2.3%

42.9%

21.7%

Driver Status

Underage, ages 515

36.0%

8.3%

23.5%

32.3%

Nondriver, ages 1617

19.8%

2.5%

57.8%

19.9%

Driver, ages 1617

60.5%

1.9%

12.3%

25.3%

Sex

Male

38.0%

6.3%

23.1%

32.6%

Female

31.5%

8.0%

34.2%

26.3%

Race

White non-Hispanic only

42.8%

3.3%

21.7%

32.1%

Black & Black multiracial

30.8%

10.2%

29.8%

29.1%

Other

30.4%

7.0%

34.9%

27.7%

Annual Household Income

<$15,000

31.5%

3.2%

38.0%

27.4%

$15,000 to $49,999

26.0%

10.6%

32.0%

31.5%

$50,000 to $99,999

43.4%

6.0%

19.0%

31.6%

$100,000+

40.6%

Vehicle Deficit Category of Household

6.7%

22.7%

30.0%

Zero-vehicle

10.0%

5.3%

58.7%

26.0%

Deficit

40.2%

6.9%

25.7%

27.2%

Nondeficit (sufficient/surplus)

38.4%

7.6%

20.5%

33.5%

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than potential drivers.

Unweighted Sample Size
602
216 231 115
40
87 145 160 187
23
202 200
84 116
486 70 46
353 249
307 200
95
93 164 135 203
66 172 364

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Leisure and socialization also account for a higher proportion of travel among wealthier children, and a greatly reduced portion of trips (just 10.0 percent) made by children from zero-vehicle households. Frequency of Nonmotorized Travel by Children Table 188 shows the mode(s) of nonmotorized transportation used by children in the past 7 days. Children are more likely to report walking and biking than adults; 78.8 percent walked or biked in the past 7 days compared to 72.5 percent of adults. Children are slightly more likely to walk than adults (75.5 percent versus 72.2 percent of adults), and much more likely to bike (30.5 percent versus 5.5 percent). Additionally, some children exhibited a mobility pattern that was virtually absent from adults. Just 0.3 percent of adults reported biking but not walking. As shown in table 188, 3.3 percent of children reported just biking.
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Table 188. Nonmotorized travel of children ages 517 (past 7 days). 415

Table 189 consolidates the data from table 188 by presenting the total percentage of children who walked (regardless of whether or not they biked) and the percentage who biked (regardless of whether or not they walked). It also shows the average number of trips in the past 7 days by users of each mode. Statewide, 78.8 percent of children ages 517 have walked and/or biked within the past 7 days. This share is higher than average in non-MPO counties, small towns, and rural areas and lower than average in second-city and urban neighborhoods. NMT usage is more common among younger children than older ones. However, while the percentage of children who walk remains relatively constant between ages 515, biking declines earlier; 44.4 percent of children ages 59 reported riding a bike, versus 34.9 percent of 1013 year olds, 15.6 percent of 1415 year olds, and just 7.4 percent of teenagers age 1617. There is not an analogous age-related decrease in the number of nonmotorized trips among those children who do walk or bike.
416

Table 189. Percentage of children who have walked or biked in the past 7 days and average number of trips among mode users.

NMT Usage Past Seven Days

Avg. Trips among Mode Users

Any NMT

Total

Walk*

Bike* (Walk and/or Bike) Walk

Bike

NMT

All children ages 517

75.5%

30.5%

78.8%

7.5

4.2

8.9

MPO Tier

1. Atlanta MPO

74.3%

24.4%

77.0%

6.9

3.5

7.8

2. Medium MPOs

75.0%

37.7%

78.2%

8.9

4.8

10.9

3. Small MPOs

70.8%

40.9%

75.7%

8.1

4.4

10.0

4. Non-MPO counties

82.6%

36.6%

86.9%

7.7

5.1

9.4

Urbanicity (Neighborhood Type)

Rural

80.1%

36.3%

84.9%

8.1

5.0

9.8

Small town

75.9%

34.3%

79.2%

7.2

4.4

8.8

Suburban

74.6%

24.0%

77.6%

7.1

3.5

7.9

Second city

72.2%

29.5%

74.6%

8.2

3.9

9.5

Urban

63.5%

13.3%

63.5%

7.9

2.2

8.4

Age

59

78.1%

44.4%

83.1%

7.2

4.3

9.1

1013

76.8%

34.9%

80.6%

6.8

3.9

8.2

1415

76.4%

15.6%

77.4%

9.0

5.2

9.9

1617

66.6%

7.4%

67.4%

8.4

4.1

8.7

Driver Status

Underage, ages 515

77.3%

35.1%

81.0%

7.4

4.3

8.9

Nondriver, ages 1617

67.0%

6.7%

67.0%

10.7

5.7

11.3

Driver, ages 1617

66.2%

8.1%

67.8%

5.8

2.6

6.0

Sex

Male

78.2%

33.1%

81.0%

7.7

4.6

9.3

Female

72.8%

27.8%

76.6%

7.3

3.8

8.4

Race

White non-Hispanic only

78.9%

32.0%

82.3%

7.4

4.3

8.7

Black & Black multiracial

72.2%

27.4%

74.8%

7.7

4.3

9.0

Other

73.9%

34.1%

78.5%

7.5

4.2

8.9

Annual Household Income

<$15,000

78.6%

38.3%

80.1%

8.8

4.2

10.7

$15,000 to $49,999

74.8%

29.3%

78.2%

9.0

5.4

10.6

$50,000 to $99,999

77.4%

29.1%

79.9%

6.6

3.7

7.7

$100,000+

75.8%

Vehicle Deficit Category of Household

30.3%

80.6%

5.8

3.4

6.8

Zero-vehicle

81.1%

39.1%

81.1%

6.5

3.4

8.2

Deficit

74.3%

22.7%

76.0%

8.1

5.0

9.4

Nondeficit (sufficient/surplus)

75.6%

33.2%

79.8%

7.4

4.1

8.7

* Percentage of row group who have walked or biked, regardless of their usage of the other mode (i.e., walk includes walk only

and both walk and bike; bike includes bike only and both walk and bike).

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than potential drivers.

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We turn now to trips observed on the travel day itself. As shown in table 190, 9.7 percent of trips by children ages 517 were by walking and 1.0 percent were by biking, for a total of 10.6 percent of all children's trips. The percentage of children who made at least one nonmotorized trip on the travel day (12.9 percent) is slightly higher than the percentage of the trips themselves that are nonmotorized.

Table 190. Nonmotorized travel on the travel day: Percent of trips/legs and percent of children.

Travel Day Trips/Legs by Children Ages 517

Number of

Percent of Trips

Trips

(Weighted) (Unweighted)

Unweighted Percent

Walk*

9.7%

540

8.2%

Bike

1.0%

62

0.9%

All NMT (walk + bike)

10.6%

602

9.2%

All other modes

89.4%

5956

90.8%

Total

6558

Modes Used on Travel Day by Each Child Ages 517

Percent of

Number of

Children

Children

Unweighted

(Weighted) (Unweighted)

Percent

1+ walk trips/legs

11.8%

276

11.3%

1+ bike trips/legs

1.4%

33

1.3%

1+ nonmotorized trip/leg (walk and/or bike)

12.9%

306

12.5%

No nonmotorized travel

87.1%

2,145

87.5%

Total

2,451

* Includes two legs of walk + other. Three children made both walk and bike trips on the travel day.

As shown in table 191, a higher percentage of children walk and/or bike in large and medium MPOs than in small MPOs and non-MPO counties. In urban neighborhoods of Atlanta, more than a quarter of children walked or biked on the travel day compared to less than 10 percent in rural and small-town neighborhoods.

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Table 191. Percent of children who walked/biked on the travel day and minutes of nonmotorized travel.

Percent of Children who

Total Minutes of Nonmotorized

Walked and/or Biked on the

Travel on Travel Day (among

Travel Day

children with 1+ minutes of NMT)

All children ages 515

12.9%

29.3

MPO Tier

1. Atlanta MPO

13.7%

31.3

2. Medium MPOs

13.8%

22.0

3. Small MPOs

10.9%

23.4

4. Non-MPO counties

11.1%

33.7

Urbanicity (Neighborhood Type)

Rural

7.9%

31.4

Small town

9.9%

22.0

Suburban

15.3%

39.5

Second city

18.7%

21.7

Urban

26.1%

27.6

Age

59

11.8%

27.2

1013

14.4%

27.7

1415

10.6%

28.4

1617

14.6%

36.6

Driver Status

Underage, ages 515

12.6%

27.6

Nondriver, age 1617

18.4%

39.2

Driver, age 1617

10.5%

31.8

Sex

Male

14.9%

28.1

Female

10.9%

31.0

Race

White non-Hispanic only

11.9%

21.4

Black & Black multiracial

13.8%

38.8

Other

13.8%

25.7

Annual Household Income

<$15,000

16.4%

51.8

$15,000 to $49,999

14.9%

26.4

$50,000 to $99,999

8.6%

21.7

$100,000+

12.8%

26.3

Vehicle Deficit Category of Household

Zero-vehicle

23.4%

36.7

Deficit

17.2%

32.4

Nondeficit (sufficient/surplus)

10.2%

25.6

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than potential drivers.

There is not a clear trend in the percentage of children who walk/bike by age. However, among teenagers old enough to drive, nondrivers are more likely to walk/bike than drivers. Additionally,
419

nondrivers who walk or bike do so for an average of 39.2 minutes compared to 31.8 minutes among pedestrians/cyclists who are able to drive. Boys are more likely to report walking/biking than girls, but female pedestrians/cyclists spend slightly longer on nonmotorized travel than do male ones. Compared to white non-Hispanic children, children of all other races were more likely to walk/bike on the travel day. However, Black children who walk/bike do so for an average of 38.8 minutes, while children of other races average 25.7 minutes (white children average 21.4 minutes). The lowest-income children (<$15,000 per year) are somewhat more likely to walk/bike than their wealthier peers. However, the average number of minutes spent on NMT by pedestrians/cyclists from these households (51.8 minutes), is nearly double the amount of time spent by more affluent pedestrians and cyclists. As with adults, there is evidence that some children are walking and biking by necessity.
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CHAPTER 7. TRAVEL FOR ITS OWN SAKE
CHAPTER 7 SUMMARY
This chapter explores the positive utility of travel by focusing on travel undertaken for its own sake.
Overview and Methods defines and reviews examples of travel for its own sake (TFIOS). We discuss the methodological challenges of measuring TFIOS in general and with regard to NHTS data. We focus on loop trips (trips with the same start and end location) as an easily identifiable form of TFIOS, and discuss the NHTS's methods of soliciting information about loop trips. The purpose of a loop trip generally differs from the activities conducted at the origin and destination. This section discusses how the researchers have reclassified the purposes of loop trips (this classification has also been used throughout chapter 1 to chapter 6).
Loop Trips in the 2017 Georgia NHTS Subsample reviews empirical findings on loop trips in the state of Georgia and compares them to nonloop trips. While nonloop travel is dominated by the private auto, the overwhelming majority of loop trips are on foot. Likewise, while recreation and fitness account for 85 percent of loop trips, only 6 percent of nonloop trips are for the purpose of recreation or fitness.
OVERVIEW AND METHODS
Studies of transportation typically frame travel as a disutility: a cost that must be paid to access a desired destination. However, a number of studies have pointed to the intrinsic value of travel,
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beyond its utilitarian purpose of getting people from A to B (Mokhtarian 2019, Mokhtarian et al. 2015). Travel can provide positive utility via several avenues. First, utility can come from enjoyment of the trip itself, independent of destination (for example, a walk through one's own neighborhood to experience fresh air and exercise113). Travel can also provide positive utility as an enjoyable activity on the way to a desired destination (e.g., an enjoyable bike ride to meet a friend at a coffee shop).114 Finally, travel can provide positive utility through the opportunity it affords to conduct additional productive or pleasant activities during the trip; this has been the focus of some research on mode choice between public transit and private autos and the emerging literature on autonomous vehicles (Frei, Hyland, and Mahmassani 2017; Frei, Mahmassani, and Frei 2015; Malokin, Circella, and Mokhtarian 2019).
For this study's purposes, TFIOS comprises trips that afford positive utility in their own right, instead of or in addition to the utility of the destination accessed. TFIOS can include:
A trip with the same origin and destination (loop trip), made for fitness, exercise, leisure, to enjoy riding a motorcycle (or train, boat, or beloved car), to see the sights, or simply to "get out of the house."
Travel where the nominal destination is the excuse for the trip rather than the other way around. In other words, the destination was generated by the trip and would not have been visited otherwise.
Travel that was intended to be a loop trip but included an impulsive stop along the way.
113 As will be discussed in the section on Loop Trips in the 2017 Georgia NHTS Subsample in this chapter, such trips are often categorized (or, as these researchers argue, miscategorized) as trips to return home.
114 The difference between these two cases is that in the second case, the positive utility is a purpose in addition to the recorded purpose at the destination while in the first, the purpose at destination (e.g., to go home) is not an accurate reflection of the trip itself.
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A sightseeing tour with stop(s) along the way. A trip where the main purpose is the destination accessed, but the traveler chooses a
longer route or slower mode for pleasure. This would include choosing a circuitous, deliberately longer route on a bike trip or enjoying a drive along one of Georgia's Scenic Byways115 when a more direct route is available.
Challenges of Identifying and Measuring TFIOS Many studies address the positive utility of travel in a general sense, with survey questions about a liking for travel in general, by various modes, and for various purposes (e.g., Ory and Mokhtarian 2005). Research that investigates a particular trip often focuses on directly measuring travelers' subjective well-being, direct satisfaction with the travel, and perceptions about whether the travel time itself was well spent or wasted (Ettema et al. 2011, Friman et al. 2012). Other studies focus on qualitative aspects of the trip themselves, including pleasant or unpleasant experiences during travel and activities conducted during travel (Abou-Zeid et al. 2012, Gripsrud and Hjorthol 2012, Susilo et al. 2012, Lin 2012).116 Some studies use the thought experiment of teleportation ("If you could instantly teleport yourself to the destination, would you do so?") to assess the extent to which the trip itself is desirable (Russell and Mokhtarian 2015).
115 See http://www.dot.ga.gov/DS/Travel/Scenic for a list of the 15 corridors (as of this writing) that have been designated as Georgia Scenic Byways by the Georgia DOT.
116 However, there is not perfect overlap between studies of the positive utility of travel and TFIOS; travel can have incidental benefits even if it is undertaken primarily for a separate purpose.
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NHTS does not contain data about travelers' internal experiences, well-being, satisfaction, or motives for making a trip.117 Without these direct data, identifying and measuring TFIOS poses a number of challenges:
No category of purpose at destination can unambiguously indicate that a trip ought to be considered TFIOS. A trip with the purpose of "leisure," for example, would include going to a local park for a stroll (TFIOS) or going to a movie theater to sit and watch a film (not TFIOS).
For loop trips, the purpose at destination will never capture the true purpose of the trip. For example, as discussed in Loop Trips in the 2017 Georgia NHTS Subsample later in this chapter, 72 percent of Georgia's loop trips nominally have the destination purpose of "regular home activities."
For nonloop trips, it is difficult to distinguish between (a) a nominal destination generated by a trip and (b) a standard destination that generated a trip. Further, the intent of such a trip may vary between members of the same party. A parent may coax their child to go for a walk in the park by promising them ice cream. For the parent, the purpose of the trip might be to get out of the house and enjoy the greenery (TFIOS). For the child, the point is definitely the ice cream (not TFIOS).
Using loop trips as a proxy for TFIOS will result in many false negatives (i.e., failing to identify nonloop trips that would more accurately be considered TFIOS). Classifying all loop trips as TFIOS results in a small but nonzero number of false positives, such as a drive to charge up a car battery or walking the family dog in inclement weather.
117 Trip purpose in NHTS is defined by the primary activity conducted at the destination, rather than reasons for conducting that activity or for making the trip at all.
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Additionally, some loop trips may actually be a series of nonloop trips for which the respondent neglected to report interim stops. Unless the reason for mode choice is asked--which it is not for the NHTS--no given mode can be assumed to be TFIOS. Even a stereotypically pleasurable mode such as the bicycle may be only reluctantly "chosen" by the traveler who owns a car and would prefer to drive but whose car is in the shop. Conversely, for the traveler who just bought a new car, even a trip to the grocery store may be TFIOS, invented as an excuse to drive. With walking for its own sake, it is unclear if and how walking within a destination such as a botanical garden or zoo is reflected in survey data and may vary based on how detailoriented the traveler filling out the survey is. While using a service such as a Google API is likely more accurate than self-reported distance for many trips, it will not detect trips where a circuitous route is chosen and will underestimate the distance of such trips. Because trip duration is self-reported, the duration will still be accurate. However, an unexpectedly long duration, particularly for a trip by private auto, is more likely to be a result of congestion than of TFIOS. NHTS does not include travel outside of the country, which will miss trips taken while on vacation abroad.
With the caveats listed above, in the NHTS data set, loop trips are the most reliable proxy for TFIOS. While using loop trips as a proxy will neglect to include nonloop TFIOS, a high proportion of loop trips can safely be considered TFIOS.
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Measurement of Loop Trips and TFIOS in the 2017 and 2009 NHTS Figure 41 shows an excerpt from the 2009 NHTS instructions for completing the travel diary.118 A trip was defined as "whenever you travel from one address to another." While this instruction would seem to exclude loop trips, respondents were given the clarification "if you started and ended in the same place, list the farthest point you reached and record a return trip."
Figure 41. Instructions. Excerpt from 2009 NHTS travel diary instructions.
118 Page 13 of the instructions sent to respondents, reproduced as p. 124 of the 2009 "Questionnaire and Field Documents" file available for download on the NHTS site: https://nhts.ornl.gov/documentation.shtml.
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The 2017 NHTS, in contrast, prompted respondents to include loop trips without adding a destination in the middle (figure 42). Additionally, compared to 2009, loop trips figured more prominently in the instructions; both of the instructional graphics shown to respondents included a clearly marked loop.
Figure 42. Instructions. Excerpt from 2017 NHTS travel diary instructions. When 2017 NHTS respondents reported a loop trip, they were asked to report the total distance traveled (as opposed to nonloop trips, where the Google API was used to calculate the shortest path distance). Misra (2017), studying data from the Cycle Atlanta mobile app, which recorded location traces of users' bicycle trips, found that the majority of trips deviated from the shortest distance path, with a mean deviation of 20 percent and a median of 2 percent (p. 161). The probability and size of the deviation was influenced by built environment characteristics (e.g.,
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traffic volume and speed, availability of bike facilities, slope) and rider demographics; women and older adults were more likely to choose a longer route (p. 168). Self-reported distances are therefore potentially more accurate, depending on the precision of respondents' estimates of their own travel distance. Trip Purpose for Loop Trips With nonloop trips, the primary activity at the destination (destination purpose) is a workable proxy for the trip purpose; a nonloop trip to a location where the primary activity is working for pay can reasonably be considered a trip with the purpose of work, and a nonloop trip where the primary activity at the destination is regular home activities can reasonably be considered a trip to return home. However, this logic breaks down with loop trips. Table 192 shows destination purposes for the 1,294 loop trips reported by 928 Georgians ages 5 and up; 4.6 percent of Georgians (weighted) made at least one loop trip on the travel day.
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Table 192. Destination purpose of loop trips made by Georgians ages 5+ (unweighted).

Destination Purpose*

Number of Loop Unweighted

Trips

Percent

All purposes Home

1294 956

100.0% 73.9%

Regular home activities (chores, sleep)

933

72.1%

Work from home (paid) Recreation and Fitness

23

1.8%

181

14.0%

Recreational activities (visit parks, movies, bars, museums)

24

1.9%

Exercise (go for a jog, walk, walk the dog, go to the gym)

157

12.1%

Work, School, and Daycare

91

7.0%

Work

65

5.0%

Work-related meeting / trip Attend school as a student

6

0.5%

19

1.5%

Attend adult care

1

0.1%

Other

66

5.1%

Volunteer activities (not paid)

2

0.2%

Drop off / pick up someone

8

0.6%

Change type of transportation

5

0.4%

Buy goods (groceries, clothes, appliances, gas)

8

0.6%

Buy services (dry cleaners, banking, service a car, pet care)

2

0.2%

Buy meals (go out for a meal, snack, carry-out)

13

1.0%

Other general errands (post office, library)

3

0.2%

Visit friends or relatives

19

1.5%

Health care visit (medical, dental, therapy)

1

0.1%

Religious or other community activities

3

0.2%

Unknown

2

0.2%

* Primary activity at the end location of the loop trip. This table shows original trip destination purposes as defined by NHTS. For the rest of this analysis, home loop trips ("regular home activities" and "work from home") are reclassified as recreation/fitness trips. We also

reclassify some trips with the destination purpose of "attend school as a student." Five trips with the purpose of "attend school" and a location of the respondent's school were reclassified as recreation/fitness; school trips at

nonschool locations were left with their original classification (14). In subsequent analysis, the researchers combine recreational activities and exercise into a single recreation/fitness category because, absent further data, it is difficult to distinguish recreational purposes (e.g.,

visiting a park) from exercise purposes (e.g., walking the dog), or ambiguous cases such as walking a dog at the park.

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Seventy-four percent of these trips (unweighted) have a destination purpose of home (regular home activities or work from home). In fact, the majority of loop trips (58.8 percent unweighted) are walking trips where the primary activity at both the origin and destination is home (not tabulated). In other words, the respondent is engaging in home activities, perhaps reading the news or doing housework. She leaves the house to take a walk, returns home, and resumes household activities (perhaps making dinner or watching television). In this sequence of events, it is not logical to conclude that the purpose of the trip was home activities. The loop trip, rather, was a break from those home activities.
The true purpose of these trips could be characterized more accurately as exercise ("go for a jog, walk, walk the dog, go to the gym") or recreation ("visit parks, movies, bars, museums"). Absent further data,119 it is difficult to distinguish between the two, and it is easy to think of trips for which the classification would be ambiguous (e.g., a walk through a local park to exercise while walking the dog). Therefore, throughout this report, the research team has reclassified loop trips with a destination purpose of home and placed them in a combined category of fitness/recreation, along with loop and nonloop trips with the destination purposes of exercise or recreation. We have likewise reclassified five loops with the purpose of attending school as fitness/recreation.120
Some of the remaining loop trips are also likely misclassified, either because the destination purpose does not reflect the purpose of the trip itself or because the respondent neglected to
119 NHTS technical documentation indicates that respondents who reported making a loop trip were prompted as to whether it was for fitness or "something else." However, the variable this question would have generated, "TPURP_LOOP," was not included with either the public or private versions of the dataset. The reasons for this omission are, at the time of writing, unknown.
120 The researchers did not reclassify school trips at nonschool locations on the grounds that the travel might be part of an educational activity. We likewise did not classify work-based loops on the grounds that they might be work errands.
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report interim stops. Loop trips with the destination purposes of buying goods/services/meals or running errands readily support the interpretation that some of these loops are simply trip chains with missing interim stops. However, these trips are relatively few in number. To be conservative, the research team has not reclassified the purposes of these other types of loops. We do, however, include them in this chapter's analysis of travel for its own sake, presented in the next section. LOOP TRIPS IN THE 2017 GEORGIA NHTS SUBSAMPLE In 2017, Georgians ages 5 and older made 233,173,400 loop trips, or 2.1 percent of all trips. This figure varies considerably by mode: loops account for 22.5 percent of all walk trips but only 5.4 percent of bike trips and just 0.3 percent of POV trips (table 193). There are likewise variations by purpose. Loop trips account for 24.5 percent of recreation and fitness trips versus 0.3 percent of trips for all other purposes (not tabulated).
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Table 193. Loop trips as a percent of total trips by mode and purpose (Georgians ages 5+).

All modes and purposes Mode Nonmotorized (walk, wheelchair, bike)
Walk or wheelchair Bike Motorized POV (including rental car) Other ground or water Purpose Recreation and fitness* Work, school, and daycare Other Return home* Note: Weighted row percentages shown.

Loop Trips 2.1%

Nonloop Trips 97.9%

21.3% 22.5%
5.4% 0.3% 0.3% 0.5%

78.7% 77.5% 94.6% 99.7% 99.7% 99.5%

24.5% 1.2% 0.3% 0.0%

75.5% 98.8% 99.7% 100.0%

As shown in table 194, Georgians made 24.4 loop trips per capita in 2017, of which 21.2 were nonmotorized. The number of loop trips per capita is highest in Atlanta and non-MPO counties, and lower in small and medium MPOs. Similarly, the number of loop trips per capita is highest at the lowest and highest ends of the income spectrum. Older adults make more loop trips than younger adults, and those with disabilities make more loop trips than those without. One plausible explanation for this is availability of free time and/or limited access to destinations that are farther afield. Working Georgians make fewer loop trips than nonworkers, but drivers make more loop trips than nondrivers.
Black Georgians make fewer loop trips than white Georgians and those of other races. Young children make the fewest loop trips of any group, likely due to their limited autonomy compared to adults.

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Table 194. Loop trips per capita in 2017 (ages 5+).

Nonmotorized*

(Walk and Bike) All Other Modes

All Georgians ages 5+

21.2

3.2

MPO Tier

1. Atlanta MPO

23.8

2.9

2. Medium MPOs

14.8

3.1

3. Small MPOs

15.2

1.5

4. Non-MPO counties

22.6

4.9

Sex

Male

23.5

3.1

Female

19.2

3.2

Age Cohort

Minor ages 517

8.3

2.8

Millennial and Gen Z (1836)

20.9

3.2

Gen X (3752)

20.4

3.8

Pre-retirement age Boomer (5364)

29.3

3.7

Retirement age (65+)

32.4

1.7

Race

White non-Hispanic only

24.6

2.9

Black, Black multiracial & Black Hispanic

13.4

4.0

Other

27.1

2.0

Mobility Impairment: a "condition or handicap that makes it difficult to travel outside of the home."

Absent

20.9

3.2

Present

24.8

2.3

Annual Household Income

<$15,000

26.2

4.5

$15,000 to $49,999

16.4

3.1

$50,000 to $99,999

19.3

2.0

$100,000+

26.7

3.6

Vehicle Deficit Category of Household

Zero-vehicle

26.7

5.9

Deficit

23.1

2.1

Nondeficit (sufficient/surplus)

20.1

3.4

Driver Status (Ages 16+ Only)

Nondriver

21.9

2.0

Driver

24.1

3.4

Worker Status (Ages 16+ Only)

Nonworker

30.4

2.5

Worker

19.4

3.8

* Walk, bike, and wheelchair. A vehicle-deficit household owns at least one vehicle, but fewer vehicles than potential drivers.

Total 24.4
26.7 18.0 16.7 27.4
26.6 22.3
11.1 24.1 24.2 33.0 34.1
27.5 17.4 29.1
24.2 27.1
30.7 19.5 21.2 30.3
32.5 25.2 23.5
23.9 27.5
32.9 23.1

Table 195 shows the mode and purpose of Georgians' loop trips. Of loop trips, 87.1 percent were nonmotorized, primarily by walk/wheelchair (85.5 percent), with biking a distant second

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(1.6 percent). The remaining 12.9 percent were by other motorized modes: POV (11.7 percent) and other ground or water (1.2 percent). No loop trips by air were reported.

Table 195. Mode and purpose of loop trips by Georgians ages 5+.

Weighted

Unweighted

Column Number of Column

Percent

Trips

Percent

All loop trips

1,294

Mode*

Nonmotorized

87.1%

1,147

88.6%

Walk or wheelchair

85.5%

1,122

86.7%

Bike

1.6%

25

1.9%

Motorized

12.9%

147

11.4%

POV (including rental car) Other ground or water

11.7% 1.2%

132

10.2%

15

1.2%

Purpose: Loop Trips by All Modes (N=1,294) *

Recreation and fitness

84.7%

Work, school, and daycare

8.9%

1,142 86

88.3% 6.6%

Other

6.3%

Purpose: Nonmotorized Loop Trips (N=1,147)

Recreation and fitness

88.6%

Work, school, and daycare

6.8%

66
1,060 50

5.1%
92.4% 4.4%

Other

4.5%

Purpose: Motorized Loop Trips (N=147)

Recreation and fitness

58.6%

Work, school, and daycare

23.0%

37

3.2%

82

55.8%

36

24.5%

Other

18.4%

29

19.7%

* Column percentages shown are based on total sample of 1,294 loop trips. Includes school bus (3), public transit (3), other bus (1), taxi/ridehail/limo (1), golf cart/Segway (3), boat/ferry/water taxi (2), and unspecified (1). Includes original destination purposes of recreation, exercise, home, and school trips at the school location. Includes work, work-related meeting/trip, attend school as a student, and attend daycare or adult daycare. Column percentages shown are based on subtotal of loop trips by each mode category (nonmotorized or motorized).

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Recreation/fitness was the dominant purpose of loop trips. These purposes accounted for 84.7 percent of all loop trips and 88.6 percent of nonmotorized trips (after the reclassification described previously in Trip Purpose for Loop Trips). Recreation/fitness was still the most common purpose of motorized loops, but by a narrower margin (58.6 percent). Table 196 compares the characteristics of loop and nonloop trips. Walking accounted for 85.5 percent of loop trips versus just 6.3 percent of nonloop trips. Private autos accounted for 87.1 percent of nonloop trips versus 11.7 percent of loop trips. While recreation and fitness are the dominant purpose of loop trips, these trips make up just 5.6 percent of nonloop trips.
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Table 196. Mode, purpose, distance, and duration of loop and nonloop trips.

Mode (Column Percent) Walk or wheelchair Bike POV (including rental car) Other ground or water Air Purpose (Column Percent) Recreation and fitness* Work, school, and daycare Other Return home* Mean Distance (Miles) All modes Walk or wheelchair Bike POV (including rental car) Other ground or water Mean Duration (Minutes) All modes Walk or wheelchair Bike POV (including rental car) Other ground or water

Loop Trips 85.5% 1.6% 11.7% 1.2% 0.0%
Loop Trips 84.7% 8.9% 6.3% n/a
Loop Trips 4.2 1.0 4.7
26.6 0.9
Loop Trips 46.5 31.3 53.6
160.4 93.0

Nonloop Trips 6.3% 0.6%
87.1% 5.8% 0.2%
Nonloop Trips 5.6%
16.4% 43.7% 34.3% Nonloop Trips
12.0 0.7 2.2
10.9 9.0
Nonloop Trips 23.6 13.9 18.1 23.0 38.5

Note: All statistics are weighted.

* For loop trips, home trips (regular home activities, work from home) are reclassified as recreation/fitness. School loop trips at the school location are also reclassified as recreation/fitness. Nonloop trips with these purposes are unchanged.

Includes work, work-related meeting/trip, attend school as a student, and attend daycare or adult daycare.

Because so many loop trips are nonmotorized, their average distance is much shorter than that of nonloop trips (4.2 miles versus 12.0 miles). However, when comparing individual modes, loop trips by walking/wheelchair, biking, and POV are all longer, on average, than nonloop trips by these same modes. In terms of duration, loop trips are longer than nonloop trips in the aggregate and across every mode.

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Table 197 shows these same statistics but narrowed to the sample of only fitness and recreation trips. The differences between loop and nonloop fitness/recreation trips are similar to those between loop and nonloop trips in general. This may suggest that differences in trip purpose between loop and nonloop trips do not explain differences in distance and duration, but it is also likely that a much higher fraction of nonloop trips in the fitness and recreation category represent trips to a sedentary recreation activity (such as a movie) than for loop trips. In other words, it is likely that the nature of this composite category (created for reasons explained in Trip Purpose for Loop Trips in this chapter) varies substantially between loop and nonloop trips.
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Table 197. Distance and duration of loop and nonloop trips for fitness and recreation.

Percentage of Trips that are for Fitness or Recreation (by Mode) *

Loop Trips Nonloop Trips

All modes

84.7%

5.6%

Walk or wheelchair

88.5%

16.7%

Bike

96.5%

14.0%

POV (including rental car) Other ground or water

56.3%

4.8%

81.3%

4.9%

Mean Distance of Fitness/Recreation

Trips (Miles)

Loop Trips

Nonloop Trips

All modes

3.7

10.8

Walk or wheelchair

1.1

0.7

Bike

4.9

2.4

POV (including rental car)

33.2

13.0

Other ground or water

0.9

18.5

Mean Duration of Fitness/Recreation Trips (Minutes)

Loop Trips Nonloop Trips

All modes

42.9

25.5

Walk or wheelchair

32.2

13.9

Bike

55.3

25.7

POV (including rental car)

162.5

26.7

Other ground or water

103.1

50.2

Note: All statistics are weighted. * For loop trips, home trips (regular home activities, work from home) are reclassified as recreation/fitness. School loop trips at the school location are also reclassified as recreation/fitness. Nonloop trips with these purposes are unchanged.

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APPENDIX: SAMPLE SIZE TABLES
439

This appendix contains detailed sample breakdowns of the different populations and subpopulations analyzed for this report. As shown in table 198, NHTS provides data on (A) households, (B) vehicles owned by each household, (C) the people that live in those households, and (D) trips made by the people living in each household. NHTS is a national dataset. Unless otherwise stated, all analysis in this report is based specifically on the samples of Georgia households, residents, vehicles, and trips. For our analysis, we used information in the trip files to additionally derive samples of (E) work journeys (see chapter 2), (F) legs of travel to access/egress another mode of transportation (chapter 6), and (G) total nonmotorized travel (pooling nonmotorized trips and nonmotorized access/egress legs).
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Table 198. Overview of Georgia NHTS population types and samples used in this report.
All analyses in this report are based on one of these seven samples, or a subsample thereof (for example, workers are a subset of persons). The remainder of this appendix provides more detailed information on each sample and key subsamples.121 Each section begins with a list of which tables in the body of the report use the relevant samples and subsamples. The tables also note populations for which sample tables can be found in the main body of the report.
121 Tables in chapter 1 through chapter 7 may exclude additional observations from their sample based on missing data. Unless otherwise stated, missing data are omitted from relevant rows/columns rather than the entire table. Where missing data occur, it represents a small fraction of observations.
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HOUSEHOLD AND VEHICLE SAMPLE TABLES

Table 199. Overview of household and vehicle subsamples.

Population

Subpopulation

Households n/a (all households)

Households

Online shoppers (1+ orders in past month)

Sample Size* Tables Using

(Unweighted) Subpopulation

8,611

1.37, 1.16,

1.25, 1.26, 4.14,

4.15, 6.22

5,714

4.14, 4.15

Detailed Sample Table(s)
A3
n/a

Vehicles

n/a (all vehicles)

16,947 1.36, 1.2729

A4

Vehicles

Seldom-used

832

1.30, 1.31

n/a

Vehicles

Newly purchased (past

2,369

1.26

n/a

12 months)

Vehicles

Recent (model year 20042017)

11,588

4.3, 4.5, 4.6

n/a

Vehicles

Alternative-fuel vehicles

313

4.16

4.2

Vehicles

Conventional-fuel

16,606

4.2, 4.4

4.2

vehicles

* Additional exclusion criteria may be listed in individual tables. Unless otherwise stated, missing data are

omitted from relevant rows/columns rather than the entire table.

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Table 200. Household sample table.

Weighted
Percent (Nonmissing Observations)

Unweighted Sample Size, N

Percent (All
Observations)

All Georgia households

-

8,611

-

MPO Tier

1. Atlanta MPO counties

53.6%

2,532

29.4%

2. Medium MPO counties

16.3%

3,144

36.5%

3. Small MPO counties

10.2%

2,004

23.3%

4. Non-MPO counties

19.9%

931

Addresses are classified by whether the county is part of an MPO. Medium MPOs have an MPO population of

10.8%

200,0011,000,000 people; small MPOs have a population of 200,000 or less. See chapter 1, Geographic Divisions for

Analysis for more details.

MPO

Albany

1.2%

229

2.7%

Athens

2.5%

502

5.8%

Atlanta

53.6%

2,532

29.4%

Augusta

3.8%

746

8.7%

Brunswick

1.1%

245

2.9%

Cartersville

0.8%

167

1.9%

Chattanooga

1.1%

70

0.8%

Columbus

2.7%

503

5.8%

Dalton

1.1%

201

2.3%

Gainesville

2.3%

512

6.0%

Hinesville

0.7%

115

1.3%

Macon

1.9%

350

4.1%

Rome

0.8%

167

1.9%

Savannah

3.8%

811

9.4%

Valdosta

1.1%

202

2.4%

Warner Robins

1.6%

328

3.8%

Non-MPO

19.9%

931

10.8%

Neighborhood Type (Urbanicity)

Rural

22.3%

2,050

23.8%

Small town

26.9%

2,725

31.7%

Suburban

27.8%

1,751

20.3%

Second city

20.5%

1,945

22.6%

Urban

2.7%

140

1.6%

Urbanicity is a measure of built environment type. See chapter 1, Geographic Divisions for Analysis for detailed definitions.

Table continues on next page.

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Continued from previous page: Household sample table.

Weighted

Percent (Nonmissing Observations)

All Georgia households

-

Annual Household Income

<$15,000

16.8%

$15,000 to $24,999

10.4%

$25,000 to $34,999

11.0%

$35,000 to $49,999

12.1%

$50,000 to $74,999

16.4%

$75,000 to $99,999

10.9%

$100,000+

22.4%

Missing

Household Composition

One adult, no children

18.5%

2+ adults, no children

20.8%

One adult, youngest child 05

2.1%

2+ adults, youngest child 05

11.0%

One adult, youngest child 615

4.0%

2+ adults, youngest child 615

12.3%

One adult, youngest child 1621

1.4%

2+ adults, youngest child 1621

5.2%

One adult, retired, no children

8.4%

2+ adults, retired, no children

16.3%

Number of Workers

0

25.5%

1

41.2%

2+

33.3%

Table continues on next page.

Unweighted Sample Size, N
8,611

Percent (All
Observations)
-

1,119 818 865
1,072 1,487 1,048 1,904
298

13.0% 9.5%
10.1% 12.5% 17.3% 12.2% 22.1%
3.5%

1,635 1,793
93 657 225 710
87 290 1,129 1,992

19.0% 20.8%
1.1% 7.6% 2.6% 8.3% 1.0% 3.4% 13.1% 23.1%

2,972 3,262 2,377

34.5% 37.9% 27.6%

444

Continued from previous page: Household sample table.

Weighted

Percent (Nonmissing Observations)

All Georgia households

-

Unweighted

Sample Size, N Percent (All
Observations)

8,611

-

Race of Household Member(s) White non-Hispanic only

54.0%

5,680

66.0%

Black and Black multiracial only (incl. Black Hisp.)

29.5%

1,949

22.6%

Other race only

7.1%

382

4.4%

Multiracial household (multiple races)

9.5%

600

7.0%

This definition differs from NHTS' measure of household race, which is based only on the race of the primary respondent.

Female-headed Household Not female-headed

71.6%

6,206

72.1%

Female-headed

28.4%

2,405

27.9%

A female-headed household is one with no adult males, or with adult males between the ages of 1821 who are the child or

other dependent of an older adult woman.

Mobility impairment Absent

82.4%

7,111

82.6%

Present

17.6%

1,496

17.4%

Missing

4

0.1%

A mobility impairment is defined as a "condition or handicap that makes it difficult to travel outside of the home."

Vehicle Deficit Category of Household

Zero-vehicle

6.9%

444

5.2%

Deficit (hard or soft)

18.9%

1,055

12.3%

Nondeficit (sufficient/surplus)

74.1%

7,112

82.6%

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than household members ages 16+ (i.e., potential

drivers). See chapter 1, Vehicle Ownership for more details.

Vehicle Deficit Category by Household Size Nondeficit, single potential driver

26.4%

2,685

31.2%

Nondeficit, 2+ potential drivers

47.7%

4,427

51.4%

Deficit

18.9%

1,055

12.3%

Zero-vehicle

6.9%

444

5.2%

Table continues on next page.

445

Continued from previous page: Household sample table.

Weighted
Percent (Nonmissing Observations)

Unweighted Sample Size, N

Percent (All
Observations)

All Georgia households

-

8,611

-

Household Vehicle Count

0

6.9%

444

5.2%

1

33.6%

2,737

31.8%

2

34.6%

3,281

38.1%

3+

24.9%

2,149

25.0%

Household Purchased 1+ vehicles in past 12 months

No

73.9%

6,532

75.9%

Yes

26.1%

2,079

24.1%

Newly purchased vehicles can be new or used so long as the household acquired them within the past 12 months.

Transit Funding Status of County of Residence

No transit funding

10.5%

948

11.0%

Rural (on-demand)

29.4%

2,528

29.4%

Urban (whole county)

46.0%

3,031

35.2%

Urban & rural

12.7%

2,039

23.7%

City only

1.4%

65

0.8%

County-level transit funding information provided by Garrow et al. (2019). For more information, see chapter 1, Transit

Availability and Use.

County-level Transit Access

None

4.5%

228

2.7%

Partial*

35.4%

3,248

37.7%

Full

60.1%

5,135

59.6%

* Partial transit access includes counties with no fixed-route transit service that have: (a) on-demand rural transit service, (b)

access to fixed-route transit in a different county in the MPO, or (c) both. See chapter 1, Transit Availability and Use for

more information.

Transit Funding Category of County of Residence

No transit funding

10.5%

948

11.0%

Rural (on-demand) only

29.4%

2,528

29.4%

Transit funding

60.1%

5,135

59.6%

446

Table 201. Sample table: Vehicles owned by Georgia households.

All vehicles MPO Tier 1. Atlanta MPO counties 2. Medium MPO counties 3. Small MPO counties 4. Non-MPO counties

Weighted
Percent (Nonmissing Observations)
-

Unweighted Sample Size, N
16,947

Percent (All
Observations) -

52.8% 16.0% 10.1% 21.1%

4,866 6,197 3,878 2,006

28.7% 36.6% 22.9% 11.8%

Addresses are classified by whether the county is part of an MPO. Medium MPOs have an MPO population of

200,0011,000,000 people; small MPOs have a population of 200,000 or less. See chapter 1, Geographic Divisions for

Analysis for more details.

MPO by County

Albany

1.0%

392

2.3%

Athens

2.5%

1,006

5.9%

Atlanta

52.8%

4,866

28.7%

Augusta

3.4%

1,398

8.3%

Brunswick

1.1%

472

2.8%

Cartersville

1.0%

352

2.1%

Chattanooga

1.2%

149

0.9%

Columbus

2.5%

934

5.5%

Dalton

1.1%

415

2.5%

Gainesville

2.7%

1,162

6.9%

Hinesville

0.7%

232

1.4%

Macon

1.8%

650

3.8%

Rome

0.8%

338

2.0%

Savannah

3.7%

1,548

9.1%

Valdosta

1.1%

381

2.3%

Warner Robins

1.6%

646

3.8%

Non-MPO

21.1%

2,006

11.8%

Vehicle Type

Car/wagon

49.5%

7,988

47.1%

Van

5.4%

840

5.0%

SUV

23.6%

3,978

23.5%

Pickup

17.5%

3,369

19.9%

Other truck

0.6%

97

0.6%

RV

0.7%

113

0.7%

Motorcycle

2.4%

477

2.8%

Something else

0.3%

54

0.3%

Missing

31

0.2%

Table continues on next page.

447

Continued from previous page: Sample of vehicles owned by Georgia households.

All vehicles

Weighted
Percent (Nonmissing Observations)
-

Unweighted Sample Size, N
16,947

Percent (All
Observations) -

Newly Purchased (past 12 months)

Not newly purchased

84.2%

14,500

85.6%

Newly purchased

15.8%

2,369

14.0%

Missing

78

0.5%

Newly purchased vehicles can be new or used so long as the household acquired them within the past 12 months.

Newly Purchased (past 12 months)

Not newly purchased

84.2%

14,500

85.6%

Newly purchased

15.8%

2,369

14.0%

Missing

78

0.5%

Newly purchased vehicles can be new or used so long as the household acquired them within the past 12 months.

Vehicle Age Cohort Pre-LEV (pre-1993)

4.6%

882

5.2%

LEV1 (19932003)

26.7%

4,346

25.6%

LEV2 (20042014)

56.3%

9,534

56.3%

New vehicles (20152017)

12.3%

2,054

12.1%

Missing

131

0.8%

Vehicle Mileage 049,999 mi

28.6%

4,240

25.0%

50,00099,999 mi

24.1%

3,598

21.2%

100149,999 mi

20.6%

2,763

16.3%

150199,999 mi

14.4%

1,863

11.0%

200,000+ mi

12.3%

1,481

8.7%

Missing

3,002

17.7%

Table continues on next page.

448

Continued from previous page: Sample of vehicles owned by Georgia households.

Weighted

Unweighted

Percent

Sample Size, N Percent

(Nonmissing

(All

Observations)

Observations)

All vehicles

-

16,947

-

Fuel Type

Gas

96.0%

16,229

95.8%

Diesel

2.1%

367

2.2%

Hybrid, electric, or other alternative fuel

1.8%

307

1.8%

Some other fuel

0.1%

18

0.1%

Missing

26

0.2%

Seldom Used

No

95.9%

15,823

93.4%

Yes

4.1%

832

4.9%

Missing

292

1.7%

A seldom-used vehicle is defined as a vehicle with fewer than 1,050 annual miles that is driven less than half as many miles

as would be expected given the household annual miles driven and number of vehicles. See chapter 1, Vehicle Fleet

Characteristics.

Annual Household Income

<$15,000

8.4%

1,154

6.8%

$15,000 to $24,999

7.6%

1,162

6.9%

$25,000 to $34,999

9.3%

1,488

8.8%

$35,000 to $49,999

12.0%

2,073

12.2%

$50,000 to $74,999

18.4%

3,187

18.8%

$75,000 to $99,999

13.7%

2,478

14.6%

$100,000+

30.6%

4,852

28.6%

Missing

553

3.3%

Vehicle Deficit Category of Household

Deficit (hard or soft)

14.4%

1,452

8.6%

Nondeficit (sufficient/surplus)

85.6%

15,495

91.4%

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than household members ages 16+ (i.e., potential

drivers). See chapter 1, Vehicle Ownership for more details.

449

PERSON SAMPLE TABLES

Table 202. Overview of person subsamples.

Unweighted

Detailed

Sample

Tables Using Sample

Population

Subpopulation

Size*

Subpopulation Table(s)

Persons Ages 5+

All persons ages 5+ n/a (all)

17,681 1.36, 1.8, 1.17,

A6

Drivers ages 5+

n/a (all)

1.18, 6.7, 7.3

14,292 1.3, 1.8

n/a

Adults Ages 18+

All adults 18+

n/a (all)

15,222 2.1, 2.4, 3.2,

A7, 5.17

5.1.5, 5.1721, 6.1,

6.3, 6.4, 6.14, 6.22

Workers ages 18+ n/a (all)

8,293 2.1, 2.2, 2.4, 2.7,

A8-9

2.20, 2.22, 2.24,

2.31, 3.1, 3.2, 3.46,

3.1113, 3.23,

3.2629, 3.31

Workers ages 18+ College-educated

4,202 2.3, 3.7, 3.9

n/a

Workers ages 18+ Noncollege-educated

4,081 2.3, 3.8, 3.10

n/a

Workers ages 18+ Telecommute-eligible

1,079 3.1722, 3.24, 3.25 n/a

Adults 18+

Active commuters (on travel day)

5,039 2.5, 2.6, 2.8, 2.9,

A10

2.27, 2.28, 2.36,

Adults 18+

Active workers (on travel day)

5,720 3.2, 3.3

n/a

Adults 18+

Active telecommuters (on travel day)

713 3.2

n/a

Adults 18+

With mobility impairment

1,632 5.2235

5.30

Adults 18+

Without mobility impairment

13,582 5.225.27

5.30

Adults 18+

Recent pedestrian or cyclist (past 7 days)

11,111 6.2, 6.2325

n/a

Adults 18+

Travel-day pedestrian or cyclist

2,019 6.236.25

n/a

Persons Ages 16+

All persons ages 16+ n/a (all)

15,605 4.8, 4.10, 4.11, 4.14, A11

4.1719

Persons ages 16+ Ridehailing users

1,176 4.7, 4.1113

4.7, 4.12

Persons ages 16+ Carsharing users

104 4.74.9

4.7, 4.8

Persons ages 16+ Bikesharing users

51 4.74.9

4.7, 4.8

Persons ages 16+ Other recent cyclists (past 7 days)

842 4.8

4.8

Persons ages 16+ Workers

8,363 1.3, 1.4, 1.3436

A12

Persons ages 16+ Drivers

14,236 1.3, 1.4, 5.1015

5.11

Other Age

Ages 1617

Teen drivers in vehicle-owning households

212 5.16

n/a

Children 517

n/a (all)

2,459 5.29, 6.3235

5.29

Children 517

Enrolled in public or private school

2,275 6.276.29

6.27

Older adults ages 80+ Without mobility impairment

534 5.28

n/a

* Additional exclusion criteria may be listed in individual tables. Unless otherwise stated, missing data are omitted from

relevant rows/columns rather than the entire table.

450

Table 203. Person sample table: All persons ages 5+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All persons ages 5+ MPO Tier 1. Atlanta MPO counties 2. Medium MPO counties 3. Small MPO counties 4. Non-MPO counties

-
54.2% 16.0% 10.2% 19.6%

17,681
5,284 6,459 4,035 1,903

-
29.9% 36.5% 22.8% 10.8%

Addresses are classified by whether the county is part of an MPO. Medium MPOs have an MPO population of

200,0011,000,000 people; small MPOs have a population of 200,000 or less. See chapter 1, Geographic Divisions for

Analysis for more details. MPO Albany Athens Atlanta Augusta Brunswick Cartersville Chattanooga Columbus Dalton Gainesville Hinesville Macon Rome Savannah Valdosta Warner Robins Non-MPO Age Child 515 Teen 1617 Adult 1864 Senior 6579 Elderly 80+ NHTS imputed age for 50 people. Age Cohort (Adults Only) Millennial and Gen Z (1836) Gen X (3752) Pre-retirement age Boomer (5364) Retirement age (65+) {Children & teens} Table continues on next page.

1.1% 2.4% 54.2% 3.8% 1.0% 0.8% 0.9% 2.7% 1.2% 2.4% 0.8% 1.9% 0.7% 3.8% 1.1% 1.6% 19.6%
16.2% 3.2%
67.0% 11.1%
2.5%
34.2% 28.7% 20.3% 16.9%

436 987 5,284 1,567 473 326 129 1,035 421 1,089 257 702 338 1,652 400 682 1,903
2,076 383
10,771 3,609 842
3,244 3,571 3,956 4,451 2,459

2.5% 5.6% 29.9% 8.9% 2.7% 1.8% 0.7% 5.9% 2.4% 6.2% 1.5% 4.0% 1.9% 9.3% 2.3% 3.9% 10.8%
11.7% 2.2%
60.9% 20.4%
4.8%
18.4% 20.2% 22.4% 25.2% 13.9%

451

Continued from previous page: Sample of persons ages 5+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All persons ages 5+ Driver Status by Age Nondriver ages 16+ Driver ages 16+

-
10.5% 73.3%

17,681
1,368 14,236

-
7.7% 80.5%

Underage driver (Excluded from analysis unless

otherwise stated)

0.4%

56

0.3%

Underage nondriver

15.8%

2020

11.4%

Missing

1

0.0%

NHTS does not ask about drivers' licensing; rather, they ask "do you/does this person drive?" Some children under 16 were

reported as drivers, perhaps due to learner's permits. Unless otherwise stated, drivers in the report refers only to drivers ages

16+.

Worker Status by Age

Nonworker ages 18+

31.1%

6,929

39.2%

Worker ages 18+

49.6%

8,293

46.9%

Nonworker ages 1617

2.7%

313

1.8%

Worker ages 1617

0.4%

70

0.4%

Child under 16

2076

11.7%

NHTS defines a worker as someone who worked for pay or profit, or was temporarily absent from paid employment, in the

week before completing the travel survey ("last week"). Unless otherwise specified, all references to "workers" in this report

refer to NHTS-defined workers ages 18+. Sex Male Female NHTS imputed sex for 16 people. Race (Detailed) White non-Hispanic only Black non-Hispanic only Latino (white Hispanic) only Asian/Pacific Islander only Native American only Other (single race) Black multiracial (incl. Black Hispanic)

48.5% 51.5%
53.4% 30.8%
7.6% 3.9% 0.2% 0.2% 2.3%

8,142 9,539
12,057 3,847 625 521 49 15 256

46.1% 54.0%
68.2% 21.8%
3.5% 3.0% 0.3% 0.1% 1.5%

Other multiracial (not including Black or Black

Hispanic)

1.6%

311

1.8%

NHTS imputed race and/or Hispanic status for 74 people. See chapter 1, Assorted Definitions and Notes for more details on

how race is categorized in this report. Race (Categories) White non-Hispanic only Black and Black multiracial (incl. Black Hispanic) Other Table continues on next page.

53.4% 33.0% 13.6%

12,057 4,103 1,521

68.2% 23.2%
8.6%

452

Continued from previous page: Sample of persons ages 5+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All persons ages 5+ Race/Ethnicity (used in Chapter 5: Equity)

-

17,681

-

White non-Hispanic only

53.4%

12,057

68.2%

Black and Black multiracial (excl. Black Hispanic)

31.9%

4,027

22.8%

Hispanic (any race)

9.0%

724

4.1%

Asian or other Mobility Impairment Absent

5.6% 91.9%

873 15,998

4.9% 90.5%

Present

8.1%

1,674

9.5%

Missing

9

0.1%

A mobility impairment is defined as a "condition or handicap that makes it difficult to travel outside of the home."

Annual Household Income

<$15,000

14.3%

1,846

10.4%

$15,000 to $24,999

9.8%

1,475

8.3%

$25,000 to $34,999

10.2%

1,622

9.2%

$35,000 to $49,999

11.8%

2,143

12.1%

$50,000 to $74,999

16.2%

3,034

17.2%

$75,000 to $99,999

11.8%

2,346

13.3%

$100,000+

25.9%

4,673

26.4%

Missing Education Level

542

3.1%

<HS graduate

13.3%

1,547

8.8%

HS or GED

23.4%

3,422

19.4%

Some college or associate degree

28.4%

4,470

25.3%

Bachelor's degree

19.6%

3,408

19.3%

Graduate or professional degree

15.4%

3,132

17.7%

Missing

27

0.2%

Age <14 College-educated

1,674

9.5%

No 4-year degree

65.0%

9,439

53.4%

Bachelor's or higher

35.0%

6,540

37.0%

Missing

1,701

9.6%

Age <14 Immigrant Nonimmigrant (born in U.S.)

89.5%

1,674 16,322

9.5% 92.3%

Immigrant (born elsewhere)

10.5%

1,350

7.6%

Missing

9

0.1%

Table continues on next page.

453

Continued from previous page: Sample of persons ages 5+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All persons ages 5+ Immigrant by Education Level

-

17,681

-

US HS or less

33.1%

4,644

26.3%

US some college/assoc.

26.0%

4,185

23.7%

US bachelor's+

29.5%

5,852

33.1%

Imm. HS or less

3.6%

323

1.8%

Imm. some college/assoc.

2.4%

284

1.6%

Imm. bachelor's+

5.4%

685

3.9%

Missing Caregiver Status Noncaregiver

74.4%

1,708 14,418

9.7% 81.6%

Caregiver, youngest child ages 015

25.6%

3,263

18.5%

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a

household with a child of 515 years old. Caregiver Status by Gender Male noncaregiver

36.9%

6,725

38.0%

Female noncaregiver

11.6%

1,417

8.0%

Male caregiver

37.5%

7,693

43.5%

Female caregiver Caregiver Status by Age of Youngest Child

14.0%

1,846

10.4%

Noncaregiver

74.4%

14,418

81.6%

Youngest ages 04

12.5%

1,520

8.6%

Youngest ages 515 Caregiver Status by Household Type Male noncaregiver

13.1% 36.9%

1,743 6,725

9.9% 38.0%

Female noncaregiver

37.5%

7,693

43.5%

Male co-caregiver

11.3%

1,375

7.8%

Female co-caregiver

11.6%

1,564

8.9%

Male single caregiver

0.4%

42

0.2%

Female single caregiver Vehicle Deficit Category of Household Zero-vehicle

2.3% 5.0%

282

1.6%

653

3.7%

Deficit (hard or soft)

26.4%

3,030

17.1%

Nondeficit (sufficient/surplus)

68.6%

13,998

79.2%

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than household members ages 16+ (i.e., potential

drivers). See chapter 1, Vehicle Ownership for more details.

Vehicle Deficit Category by Household Size Nondeficit, single potential driver

13.0%

2,991

16.9%

Nondeficit, 2+ potential drivers

55.6%

11,007

62.3%

Deficit

26.4%

3,030

17.1%

Zero-vehicle

5.0%

653

3.7%

Table continues on next page.

454

Continued from previous page: Sample of persons ages 5+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All persons ages 5+ Household Vehicle Count 0 1 2

-
5.0% 25.2% 37.5%

17,681
653 4,054 7,280

-
3.7% 22.9% 41.2%

3+

32.3%

Household Purchased 1+ vehicles in past 12 months

No

69.0%

5,694 12,749

32.2% 72.1%

Yes

31.0%

4,932

27.9%

Newly purchased vehicles can be new or used so long as the household acquired them within the past 12 months.

Transit Use, Past 30 Days

No

87.3%

16,008

90.5%

Yes

12.7%

1,656

9.4%

Missing

17

0.1%

Days of Transit Use, Past 30 Days

No days

87.3%

16,008

90.5%

15 days

2.0%

291

1.7%

6+ days Missing Transit Funding Status of County of Residence No transit funding Rural (on-demand) Urban (whole county) Urban & rural City only

10.6%
11.0% 29.8% 45.6% 12.4%
1.1%

1,365 17
2,036 5,392 6,068 4,061
124

7.7% 0.1%
11.5% 30.5% 34.3% 23.0%
0.7%

County-level transit funding information provided by Garrow et al (2019). For more information, see chapter 1, Transit

Availability and Use. Transit Funding Category of County of Residence No transit funding Rural (on-demand) only Transit funding Table continues on next page.

11.0% 29.8% 59.2%

2,036 5,392 10,253

11.5% 30.5% 58.0%

455

Continued from previous page: Sample of persons ages 5+.
Weighted
Percent (Nonmissing Observations)

All persons ages 5+ Walking, Past 30 Days No Yes Missing Biking, Past 30 Days No Yes Missing Number of Walk Trips, Past 30 Days 0 14 59 1019 20+ Missing Number of Bike Trips, Past 30 Days 0 14 5+ Missing

-
27.2% 72.8%
89.7% 10.3%
27.2% 30.0% 27.9%
7.9% 6.9%
89.7% 7.6% 2.7%

Unweighted

Sample Size, Percent

N

(All

Observations)

17,681

-

4,734 12,877
70

26.8% 72.8%
0.4%

15,997 1,674 10

90.5% 9.5% 0.1%

4,734 5,302 4,906 1,468 1,201
70

26.8% 30.0% 27.8%
8.3% 6.8% 0.4%

15,997 1,273 401 10

90.5% 7.2% 2.3% 0.1%

456

Table 204. Person sample table: All adults ages 18+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All adults ages 18+ MPO Tier 1. Atlanta MPO counties 2. Medium MPO counties 3. Small MPO counties 4. Non-MPO counties

-
54.1% 15.8% 10.1% 20.0%

15,222
4,496 5,549 3,505 1,672

29.5% 36.5% 23.0% 11.0%

Addresses are classified by whether the county is part of an MPO. Medium MPOs have an MPO population of

200,0011,000,000 people; small MPOs have a population of 200,000 or less. See chapter 1, Geographic Divisions for

Analysis for more details. MPO Albany Athens Atlanta Augusta Brunswick

1.1% 2.4% 54.1% 3.6% 1.0%

387 870 4,496 1,313 417

2.5% 5.7% 29.5% 8.6% 2.7%

Cartersville Chattanooga

0.8% 1.0%

291

1.9%

120

0.8%

Columbus Dalton Gainesville

2.6% 1.1% 2.3%

870

5.7%

361

2.4%

939

6.2%

Hinesville Macon Rome

0.7% 2.0% 0.7%

206

1.4%

621

4.1%

290

1.9%

Savannah Valdosta Warner Robins

3.9% 1.1% 1.6%

1,437 348 584

9.4% 2.3% 3.8%

Non-MPO Age Adult 1864 Senior 6579 Elderly 80+

20.0%
83.1% 13.7%
3.1%

1,672
10,771 3,609 842

11.0%
70.8% 23.7%
5.5%

NHTS imputed age for 50 people. Age Cohort (Adults Only) Millennial and Gen Z (1836) Gen X (3752) Pre-retirement age Boomer (5364) Retirement age (65+) Table continues on next page.

34.2% 28.7% 20.3% 16.9%

3,244 3,571 3,956 4,451

21.3% 23.5% 26.0% 29.2%

457

Continued from previous page: Sample of adults ages 18+.

Weighted

Percent (Nonmissing

Observations)

All adults ages 18+

-

Driver Status by Age

Nondriver ages 16+

11.0%

Driver ages 16+

89.0%

Missing

Unweighted

Sample Size, Percent

N

(All

Observations)

15,222

1,199 14,022
1

7.9% 92.1%
0.0%

NHTS does not ask about drivers' licensing; rather, they ask "do you/does this person drive?" Some

children under 16 were reported as drivers, perhaps due to learner's permits. Unless otherwise stated,

drivers in the report refers only to drivers ages 16+.

Worker Status by Age

Nonworker ages 18+

38.5%

6,929

45.5%

Worker ages 18+

61.5%

8,293

54.5%

NHTS defines a worker as someone who worked for pay or profit, or was temporarily absent from paid employment, in the week before completing the travel survey ("last week"). Unless otherwise specified, all references to "workers" in this report refer to NHTS-defined workers ages 18+.

Sex

Male

47.9%

6,845

45.0%

Female

52.1%

8,377

55.0%

NHTS imputed sex for 16 people. Race (Detailed) White non-Hispanic only

55.2%

10,635

69.9%

Black non-Hispanic only

30.0%

3,233

21.2%

Latino (white Hispanic) only

7.2%

481

3.2%

Asian/Pacific Islander only

3.9%

426

2.8%

Native American only

0.3%

41

0.3%

Other (single race)

0.2%

15

0.1%

Black and Black multiracial (incl. Black Hispanic)

1.6%

149

1.0%

Other multiracial (not including Black or Black Hispanic)

1.5%

242

1.6%

NHTS imputed race and/or Hispanic status for 74 people. See chapter 1, Assorted Definitions and Notes for more details on how race is categorized in this report.

Race (Categories)

White non-Hispanic only

55.2%

10,635

69.9%

Black and Black multiracial (incl. Black Hispanic)

31.6%

3,382

22.2%

Other

13.1%

1,205

7.9%

Table continues on next page.

458

Continued from previous page: Sample of adults ages 18+.
Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All adults ages 18+ Race/Ethnicity (used in Chapter 5: Equity) White non-Hispanic only Black and Black multiracial (excl. Black Hisp.) Hispanic (any race) Asian or other Mobility Impairment Absent Present Missing

-
55.2% 30.8%
8.5% 5.5%
90.5% 9.5%

15,222
10,635 3,335 548 704
13,582 1,632 8

69.9% 21.9%
3.6% 4.6%
89.2% 10.7%
0.1%

A mobility impairment is defined as a "condition or handicap that makes it difficult to travel outside of the

home."

Annual Household Income

<$15,000

14.6%

1,612

10.6%

$15,000 to $24,999

9.4%

1,277

8.4%

$25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000+ Missing Education Level <HS graduate HS or GED Some college or associate degree Bachelor's degree Graduate or professional degree Missing

10.1% 12.0% 16.6% 11.8% 25.5%
7.0% 24.7% 30.5% 21.1% 16.6%

1,419 1,864 2,661 2,013 3,872
504
811 3,381 4,467 3,408 3,132
23

9.3% 12.3% 17.5% 13.2% 25.4%
3.3%
5.3% 22.2% 29.4% 22.4% 20.6%
0.2%

College-educated No 4-year degree Bachelor's or higher Missing

62.3% 37.7%

8,659 6,540
23

56.9% 43.0%
0.2%

Immigrant Nonimmigrant (born in US) Immigrant (born elsewhere) Missing Table continues on next page.

88.0% 12.0%

13,953 1,262 7

91.7% 8.3% 0.1%

459

Continued from previous page: Sample of adults ages 18+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All adults ages 18+ Immigrant by Education Level US HS or less US some college/assoc. US bachelor's+ Imm. HS or less Imm. some college/assoc. Imm. bachelor's+ Missing Caregiver Status Noncaregiver Caregiver, youngest child ages 015

-
28.2% 28.0% 31.8%
3.5% 2.6% 5.9%
68.2% 31.8%

15,222
3,902 4,182 5,851
288 284 685
30
11,959 3,263

25.6% 27.5% 38.4%
1.9% 1.9% 4.5% 0.2%
78.6% 21.4%

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any

adult age 22+ in a household with a child of 515 years old.

Caregiver Status by Gender

Male noncaregiver

33.5%

5,428

35.7%

Female noncaregiver

14.4%

1,417

9.3%

Male caregiver

34.7%

6,531

42.9%

Female caregiver

17.3%

1,846

12.1%

Caregiver Status by Age of Youngest Child

Noncaregiver

68.2%

11,959

78.6%

Youngest ages 04

15.6%

1,520

10.0%

Youngest ages 515

16.2%

1,743

11.5%

Caregiver Status by Household Type

Male noncaregiver

33.5%

5,428

35.7%

Female noncaregiver

34.7%

6,531

42.9%

Male co-caregiver

14.0%

1,375

9.0%

Female co-caregiver

14.4%

1,564

10.3%

Male single caregiver

0.5%

42

0.3%

Female single caregiver

2.9%

282

1.9%

Vehicle Deficit Category of Household

Zero-vehicle

4.9%

572

3.8%

Deficit (hard or soft)

25.9%

2,513

16.5%

Nondeficit (sufficient/surplus)

69.2%

12,137

79.7%

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than household members ages

16+ (i.e., potential drivers). See chapter 1, Vehicle Ownership for more details.

Vehicle Deficit Category by Household Size

Nondeficit, single potential driver

13.3%

2,685

17.6%

Nondeficit, 2+ potential drivers

55.9%

9,452

62.1%

Deficit

25.9%

2,513

16.5%

Zero-vehicle

4.9%

572

3.8%

Table continues on next page.

460

Continued from previous page: Sample of adults ages 18+.
Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All adults ages 18+

-

15,222

Household Vehicle Count

0

4.9%

572

3.8%

1

24.9%

3,535

23.2%

2

37.1%

6,234

41.0%

3+

33.1%

4,881

32.1%

Household Purchased 1+ Vehicles in Past 12 Months

No

69.2%

11,079

72.8%

Yes

30.8%

4,143

27.2%

Newly purchased vehicles can be new or used so long as the household acquired them within the past 12 months.

Transit Use, Past 30 Days

No

89.2%

14,017

92.1%

Yes

10.8%

1,190

7.8%

Missing

15

0.1%

Days of Transit Use, Past 30 Days

No days

89.2%

14,017

92.1%

15 days

2.3%

268

1.8%

6+ days

8.5%

922

6.1%

Missing

15

0.1%

Transit Funding Status of County of Residence

No transit funding

10.4%

1,717

11.3%

Rural (on-demand)

30.0%

4,638

30.5%

Urban (whole county)

45.9%

5,225

34.3%

Urban & rural

12.4%

3,529

23.2%

City only

1.3%

113

0.7%

County-level transit funding information provided by Garrow et al. (2019). For more information, see chapter 1, Transit

Availability and Use. Transit Funding Category of County of Residence No transit funding Rural (on-demand) only Transit funding Table continues on next page.

10.4% 30.0% 59.6%

1,717 4,638 8,867

11.3% 30.5% 58.3%

461

Continued from previous page: Sample of adults ages 18+.
Weighted
Percent (Nonmissing Observations)

All adults ages 18+ Walking, Past 30 Days No Yes Missing Biking, Past 30 Days No Yes Missing Number of Walk Trips, Past 30 Days 0 14 59 1019 20+ Missing Number of Bike Trips, Past 30 Days 0 14 5+ Missing

-
27.8% 72.2%
94.5% 5.5%
27.8% 30.5% 26.5%
8.1% 7.1%
94.5% 4.5% 1.0%

Unweighted

Sample Size, Percent

N

(All

Observations)

15,222

4,127 11,033
62

27.1% 72.5%
0.4%

14,355 861 6

94.3% 5.7% 0.0%

4,127 4,598 4,102 1,271 1,062
62

27.1% 30.2% 27.0%
8.4% 7.0% 0.4%

14,355 713 148 6

94.3% 4.7% 1.0% 0.0%

462

Table 205. Person sample table: All workers ages 18+ (demographic characteristics).

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All workers ages 18+ MPO Tier 1. Atlanta MPO counties 2. Medium MPO counties 3. Small MPO counties 4. Non-MPO counties

57.8% 15.7% 10.0% 16.6%

8,293
2,789 3,001 1,779
724

33.6% 36.2% 21.5%
8.7%

Addresses are classified by whether the county is part of an MPO. Medium MPOs have an MPO population of

200,0011,000,000 people; small MPOs have a population of 200,000 or less. See chapter 1, Geographic Divisions

for Analysis for more details. MPO Albany Athens Atlanta Augusta

1.0% 2.5% 57.8% 3.4%

186 506 2,789 695

2.2% 6.1% 33.6% 8.4%

Brunswick Cartersville Chattanooga

1.0% 0.9% 1.0%

188

2.3%

156

1.9%

70

0.8%

Columbus Dalton Gainesville

2.6% 1.1% 2.3%

474

5.7%

180

2.2%

467

5.6%

Hinesville Macon Rome

0.8% 1.9% 0.6%

125

1.5%

326

3.9%

128

1.5%

Savannah Valdosta Warner Robins

3.9% 1.2% 1.5%

789

9.5%

181

2.2%

309

3.7%

Non-MPO Age Adult 1864 Senior 6579 Elderly 80+

16.6%
95.0% 4.8% 0.2%

724
7,556 701 36

8.7%
91.1% 8.5% 0.4%

NHTS imputed age for 50 people. Age Cohort (Adults Only) Millennial and Gen Z (1836) Gen X (3752) Pre-retirement age Boomer (5364) Retirement age (65+) Table continues on next page.

38.1% 36.8% 20.1%
5.0%

2,361 2,843 2,352
737

28.5% 34.3% 28.4%
8.9%

463

Continued from previous page: Sample of workers ages 18+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All workers ages 18+ Driver Status by Age Nondriver ages 16+ Driver ages 16+ Missing

4.9% 95.1%

8,293
240 8,052
1

2.9% 97.1%
0.0%

NHTS does not ask about drivers' licensing; rather, they ask "do you/does this person drive?" Some children under 16

were reported as drivers, perhaps due to learner's permits. Unless otherwise stated, drivers in the report refers only to

drivers ages 16+.

Worker Status by Age

Nonworker ages 18+

Worker ages 18+

8,293

100.0%

NHTS defines a worker as someone who worked for pay or profit, or was temporarily absent from paid employment, in

the week before completing the travel survey ("last week"). Unless otherwise specified, all references to "workers" in this

report refer to NHTS-defined workers ages 18+.

Sex

Male

53.8%

4,169

50.3%

Female

46.2%

4,124

49.7%

NHTS imputed sex for 16 people. Race (Detailed)

White non-Hispanic only

56.1%

5,745

69.3%

Black non-Hispanic only

28.8%

1,752

21.1%

Latino (white Hispanic) only

7.9%

313

3.8%

Asian/Pacific Islander only

3.4%

227

2.7%

Native American only

0.3%

23

0.3%

Other (single race)

0.2%

8

0.1%

Black and Black multiracial (incl. Black Hispanic)

1.8%

93

1.1%

Other multiracial (not including Black or Black

Hispanic)

1.4%

132

1.6%

NHTS imputed race and/or Hispanic status for 74 people. See chapter 1, Assorted Definitions and Notes for more

details on how race is categorized in this report. Race (Categories) White non-Hispanic only

56.1%

5,745

69.3%

Black and Black multiracial (incl. Black Hispanic) Other Table continues on next page.

30.6% 13.2%

1,845 703

22.3% 8.5%

464

Continued from previous page: Sample of workers ages 18+.
Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All workers ages 18+ Race/Ethnicity (used in chapter 5, Equity)

8,293

White non-Hispanic only

56.1%

5,745

69.3%

Black and Black multiracial (excl. Black Hisp.)

29.6%

1,812

21.9%

Hispanic (any race)

9.3%

360

4.3%

Asian or other

4.9%

376

4.5%

Mobility Impairment

Absent

98.1%

8,127

98.0%

Present

1.9%

163

2.0%

Missing

3

0.0%

A mobility impairment is defined as a "condition or handicap that makes it difficult to travel outside of the home."

Annual Household Income

<$15,000

8.1%

473

5.7%

$15,000 to $24,999

7.7%

476

5.7%

$25,000 to $34,999

10.1%

714

8.6%

$35,000 to $49,999

12.3%

979

11.8%

$50,000 to $74,999

17.7%

1,493

18.0%

$75,000 to $99,999

13.2%

1,243

15.0%

$100,000+

30.9%

2,721

32.8%

Missing

194

2.3%

Education Level

<HS graduate

3.4%

201

2.4%

HS or GED

21.1%

1,458

17.6%

Some college or associate degree

30.9%

2,422

29.2%

Bachelor's degree

25.0%

2,188

26.4%

Graduate or professional degree

19.7%

2,014

24.3%

Missing

10

0.1%

College-educated No 4-year degree Bachelor's or higher Missing

55.3% 44.7%

4,081 4,202
10

49.2% 50.7%
0.1%

Immigrant Nonimmigrant (born in US) Immigrant (born elsewhere) Missing Table continues on next page.

87.0% 13.0%

7,536 753 4

90.9% 9.1% 0.1%

465

Continued from previous page: Sample of workers ages 18+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All workers ages 18+

8,293

Immigrant by Education Level

US HS or less

21.1%

1,521

18.3%

US some college/assoc.

28.1%

2,258

27.2%

US bachelor's+

37.8%

3,750

45.2%

Imm. HS or less

3.4%

137

1.7%

Imm. some college/assoc.

2.7%

163

2.0%

Imm. bachelor's+

6.8%

450

5.4%

Missing

14

0.2%

Caregiver Status

Noncaregiver

62.6%

5,883

70.9%

Caregiver, youngest child ages 015

37.4%

2,410

29.1%

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in

a household with a child of 515 years old.

Caregiver Status by Gender

Male noncaregiver

33.6%

2,932

35.4%

Female noncaregiver

20.2%

1,237

14.9%

Male caregiver

29.0%

2,951

35.6%

Female caregiver

17.2%

1,173

14.1%

Caregiver Status by Age of Youngest Child

Noncaregiver

62.6%

5,883

70.9%

Youngest ages 04

17.8%

1,105

13.3%

Youngest ages 515

19.5%

1,305

15.7%

Caregiver Status by Household Type

Male noncaregiver

33.6%

2,932

35.4%

Female noncaregiver

29.0%

2,951

35.6%

Male co-caregiver

19.7%

1,203

14.5%

Female co-caregiver

13.8%

969

11.7%

Male single caregiver

0.5%

34

0.4%

Female single caregiver

3.4%

204

2.5%

Vehicle Deficit Category of Household

Zero-vehicle

2.5%

141

1.7%

Deficit (hard or soft)

20.9%

1,063

12.8%

Nondeficit (sufficient/surplus)

76.6%

7,089

85.5%

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than household members ages 16+ (i.e. potential

drivers). See chapter 1, Vehicle Ownership for more details. Vehicle Deficit Category by Household Size Nondeficit, single potential driver Nondeficit, 2+ potential drivers Deficit Zero-vehicle Table continues on next page.

14.1% 62.5% 20.9%
2.5%

1,420 5,669 1,063
141

17.1% 68.4% 12.8%
1.7%

466

Continued from previous page: Sample of workers ages 18+.
Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All workers ages 18+

8,293

Household Vehicle Count

0

2.5%

141

1.7%

1

22.1%

1,592

19.2%

2

39.7%

3,562

43.0%

3+

35.8%

2,998

36.2%

Household Purchased 1+ Vehicles in Past 12 Months

No

67.1%

5,756

69.4%

Yes

32.9%

2,537

30.6%

Newly purchased vehicles can be new or used so long as the household acquired them within the past 12 months.

Transit Use, Past 30 Days

No

88.3%

7,563

91.2%

Yes

11.7%

724

8.7%

Missing

6

0.1%

Days of Transit Use, Past 30 Days

No days

88.3%

7,563

91.2%

15 days

2.8%

173

2.1%

6+ days

8.9%

551

6.6%

Missing

6

0.1%

Transit Funding Status of County of Residence

No transit funding

9.3%

894

10.8%

Rural (on-demand)

27.4%

2,340

28.2%

Urban (whole county)

49.7%

3,163

38.1%

Urban & rural

12.6%

1,845

22.3%

City only

1.1%

51

0.6%

County-level transit funding information provided by Garrow et al. (2019). For more information, see chapter 1, Transit

Availability and Use.

Transit Funding Category of County of Residence

No transit funding

9.3%

Rural (on-demand) only

27.4%

Transit funding

63.3%

Table continues on next page.

894 2,340 5,059

10.8% 28.2% 61.0%

467

Continued from previous page: Sample of workers ages 18+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All workers ages 18+ Walking, Past 30 Days No Yes Missing Biking, Past 30 Days No Yes Missing Number of Walk Trips, Past 30 Days 0 14 59 1019 20+ Missing Number of Bike Trips, Past 30 Days 0 14 5+ Missing

27.5% 72.5%
93.9% 6.1%
27.5% 32.2% 25.7%
8.0% 6.6%
93.9% 5.0% 1.1%

8,293
2,150 6,123
20
7,743 548 2
2,150 2,696 2,159
705 563
20
7,743 454 94 2

25.9% 73.8%
0.2%
93.4% 6.6% 0.0%
25.9% 32.5% 26.0%
8.5% 6.8% 0.2%
93.4% 5.5% 1.1% 0.0%

468

Table 206. Person sample table: All workers ages 18+ (job and commute characteristics).

Weighted Unweighted

Percent (Nonmissing Observations)

Sample Size, Percent

N

(All

Observations)

All workers ages 18+

8,293

Usual Commute Mode (Detailed)

Walk

1.7%

88

1.1%

Bicycle

0.6%

28

0.3%

Car

63.8%

4,345

52.4%

SUV

14.5%

1,047

12.6%

Van

2.3%

161

1.9%

Pickup truck

12.0%

1,003

12.1%

Golf cart / Segway

0.0%

1

0.0%

Motorcycle/moped

0.5%

31

0.4%

RV (motor home, ATV, snowmobile)

0.0%

2

0.0%

School bus

0.0%

5

0.1%

Public or commuter bus

1.2%

53

0.6%

Paratransit/dial-a-ride

0.1%

3

0.0%

Private/charter/tour/shuttle bus

0.1%

8

0.1%

Intercity bus (megabus, Greyhound)

0.1%

5

0.1%

Amtrak/commuter rail

0.4%

12

0.1%

Subway/light rail/ streetcar

1.1%

43

0.5%

Taxi/ridehailing/limo

0.8%

29

0.4%

Rental car (incl Zipcar etc)

0.0%

4

0.1%

Airplane

0.3%

19

0.2%

Other or unknown motorized

0.4%

19

0.2%

Missing

1,387

16.7%

"How did you usually get to your (primary)] job last week? If you used more than one mode of transportation, please

select the one used for most of the distance." This may differ from the commute mode used on the travel day.

Category of Usual Commute Mode

POV incl rental car

93.2%

6598

79.6%

Nonmotorized

2.3%

116

1.4%

Public transit or other bus

3.8%

155

1.9%

Other ground or water

0.4%

18

0.2%

Air

0.3%

19

0.2%

Missing

1,387

16.7%

Distance to Work (Miles)

5 mi

24.7%

1766

21.3%

510 mi

20.6%

1,659

20.0%

1020 mi

27.8%

1,921

23.2%

2045 mi

21.4%

1,227

14.8%

>45 mi

5.5%

339

4.1%

Missing

1,381

16.7%

Road network distance, in miles, between respondent's home location and work location, as calculated by NHTS.

Table continues on next page.

469

Continued from previous page: Sample of workers ages 18+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All workers ages 18+

Occupational Category

Sales or service

26.9%

Clerical or administrative support

9.3%

Blue collar*

17.7%

Professional, managerial, or technical

46.0%

Other

0.1%

Missing

* Blue collar refers to manufacturing, construction, maintenance, or farming.

Worker Type

Full-time (35+ hours per week)

19.6%

Part-time

80.4%

Missing

Work Flexibility Type

Schedule and location

22.3%

Location only

4.9%

Schedule only

18.7%

Neither schedule nor location

54.2%

Missing

8,293
1,930 799
1,143 4,112
10 299
1,553 6,446
294
1,839 317
1,690 4,142
305

23.3% 9.6%
13.8% 49.6%
0.1% 3.6%
18.7% 77.7%
3.6%
22.2% 3.8%
20.4% 50.0%
3.7%

Schedule refers to flextime. Location refers to telecommute-eligible workers and home-based workers. See Flextime and

Telework Eligibility below for definitions, and chapter 3, Overview for more details.

Flextime

Ineligible

59.1%

Eligible

40.9%

Missing

"At your (primary) job, do you have the ability to set or change your own start time?"

Telework Eligibility

Telecommute-ineligible worker

73.8%

Telecommute-eligible worker

13.0%

Home-based worker

13.2%

4,459 3,529
305
6,129 1,079 1,085

53.8% 42.6%
3.7%
73.9% 13.0% 13.1%

A home-based worker "usually work(s) from home." A telecommute-eligible worker does not usually work from home,

"have the option of working from home or an alternate location instead of going into your/their primary workplace." See

chapter 3, Worker Telework Eligibility Categories for details. Travel Day Teleworking Did not work Telework (exclusive) Telework (mixed) Conventional commute

37.2% 5.5% 2.3%
54.9%

2,573 501 212
5,007

31.0% 6.0% 2.6%
60.4%

A respondent teleworked on the travel day if they reported working from home for pay. They engaged in a conventional

commute if they reported a trip with the purpose of working for pay (not at home). Mixed teleworkers reported both

teleworking and a conventional commute. See chapter 3, Travel Day Work and Telework Categories for more details.

470

Table 207. Person sample table: Active commuters ages 18+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All active commuters ages 18+

5,113

-

An active commuter is a person who made one or more work journeys on the travel day (see chapter 2).

NHTS-defined Worker Status

Nonworker ages 18+

1.3%

74

1.4%

Worker ages 18+

98.7%

5,039

98.6%

NHTS defines a worker as someone who worked for pay or profit, or was temporarily absent from paid employment, in the

week before completing the travel survey ("last week"). A small number of people who were not NHTS-designated workers

nevertheless reported work travel on their travel day, perhaps reflecting irregular employment situations.

MPO Tier

1. Atlanta MPO counties

57.2%

1,650

32.3%

2. Medium MPO counties

14.9%

1,858

36.3%

3. Small MPO counties

9.8%

1,131

22.1%

4. Non-MPO counties

18.2%

474

9.3%

Addresses are classified by whether the county is part of an MPO. Medium MPOs have an MPO population of

200,0011,000,000 people; small MPOs have a population of 200,000 or less. See chapter 1, Geographic Divisions for

Analysis for more details.

Age Cohort (Adults Only)

Millennial and Gen Z (1836)

40.2%

1,517

29.7%

Gen X (3752)

35.9%

1,752

34.3%

Pre-retirement age Boomer (5364)

19.7%

1,456

28.5%

Retirement age (65+)

4.2%

388

7.6%

Sex

Male

56.2%

2,650

51.8%

Female

43.8%

2,463

48.2%

Annual Household Income

<$35,000

27.9%

1,048

20.5%

$35,000 to $49,999

12.8%

646

12.6%

$50,000 to $74,999

18.3%

951

18.6%

$75,000 to $99,999

14.3%

806

15.8%

$100,000+

26.7%

1,556

30.4%

Missing

106

2.1%

Table continues on next page.

471

Continued from previous page: Sample of active commuters ages 18+.

Weighted

Unweighted

Percent

Sample Size, Percent

(Nonmissing

N

(All

Observations)

Observations)

All active commuters ages 18+

5,113

-

An active commuter is a person who made one or more work journeys on the travel day (see chapter 2).

Race (Categories)

White non-Hispanic only

53.4%

3,493

68.3%

Black and Black multiracial (incl. Black Hispanic)

32.7%

1,182

23.1%

Other

13.9%

438

8.6%

NHTS imputed race and/or Hispanic status for 74 people. See chapter 1, Assorted Definitions and Notes for more details on how race is categorized in this report.

Occupational Category

Sales or service

27.2%

1,159

22.7%

Clerical or administrative support

9.1%

499

9.8%

Blue collar*

19.9%

795

15.5%

Professional, managerial, or technical

43.6%

2,553

49.9%

Other

0.1%

5

0.1%

Not an NHTS-defined worker

74

1.4%

Missing

28

0.5%

* Blue collar refers to manufacturing, construction, maintenance, or farming.

Worker Type

Part-time (<35 hours per week)

15.4%

708

13.8%

Full-time (35+ hours per week)

84.6%

4,305

84.2%

Not an NHTS-defined worker Unknown College-educated No 4-year degree Bachelor's or higher Missing

59.4% 40.6%

78 22
2,654 2,453
6

1.5% 0.4%
51.9% 48.0%
0.1%

472

Table 208. Person sample table: Persons ages 16+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All persons ages 16+

17,618

-

MPO Tier

1. Atlanta MPO counties

54.4%

4,627

26.3%

2. Medium MPO counties

15.8%

5,683

32.3%

3. Small MPO counties

10.1%

3,592

20.4%

4. Non-MPO counties

19.8%

1,703

9.7%

Addresses are classified by whether the county is part of an MPO. Medium MPOs have an MPO population of

200,0011,000,000 people; small MPOs have a population of 200,000 or less. See chapter 1, Geographic Divisions for

Analysis for more details.

Age Cohort

Teenager (1617)

3.8%

383

2.2%

Millennial and Gen Z (1836)

32.9%

3,244

18.4%

Gen X (3752)

27.6%

3,571

20.3%

Pre-retirement age Boomer (5364)

19.5%

3,956

22.5%

Seniors (6579)

13.2%

3,609

20.5%

Elderly (80+)

3.0%

842

4.8%

Sex

Male

47.9%

7,038

39.9%

Female

52.1%

8,567

48.6%

Annual Household Income

<$15,000

14.5%

1,650

9.4%

$15,000 to $24,999

9.5%

1,303

7.4%

$25,000 to $34,999

10.1%

1,447

8.2%

$35,000 to $49,999

11.9%

1,904

10.8%

$50,000 to $74,999

16.6%

2,731

15.5%

$75,000 to $99,999

11.7%

2,061

11.7%

$100,000+

25.7%

3,995

22.7%

Missing

514

2.9%

Table continues on next page.

473

Continued from previous page: Sample of persons ages 16+.
Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All persons ages 16+

17,618

-

Race (Categories)

White non-Hispanic only

54.8%

10,852

61.6%

Black and Black multiracial (incl. Black Hispanic)

32.1%

3,507

19.9%

Other

13.1%

1,246

7.1%

NHTS imputed race and/or Hispanic status for 74 people. See chapter 1, Assorted Definitions and Notes for more details

on how race is categorized in this report.

Mobility Impairment

Absent

90.7%

13,957

79.2%

Present

9.3%

1,640

9.3%

Missing

8

0.0%

A mobility impairment is defined as a "condition or handicap that makes it difficult to travel outside of the home."

Driver Status

Nondriver ages 16+

12.6%

1,368

7.8%

Driver ages 16+

87.4%

14,236

80.8%

Missing

1

0.0%

Vehicle Deficit Category of Household

Zero-vehicle

5.0%

589

3.3%

Deficit (hard or soft)

27.0%

2,697

15.3%

Nondeficit (sufficient/surplus)

68.0%

12,319

69.9%

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than household members ages 16+ (i.e., potential

drivers). See chapter 1, Vehicle Ownership for more details.

Transit Use, Past 30 Days

No

88.9%

14,344

81.4%

Yes

11.1%

1,246

7.1%

Missing

15

0.1%

Walking, Past 30 Days

No

28.0%

4,257

24.2%

Yes

72.0%

11,285

64.1%

Missing

63

0.4%

474

Table 209. Person sample table: Workers ages 16+.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All workers ages 16+

8,363

-

MPO Tier

1. Atlanta MPO counties

57.7%

2,808

33.6%

2. Medium MPO counties

15.7%

3,033

36.3%

3. Small MPO counties

10.0%

1,794

21.5%

4. Non-MPO counties

16.5%

728

8.7%

Addresses are classified by whether the county is part of an MPO. Medium MPOs have an MPO population of

200,0011,000,000 people; small MPOs have a population of 200,000 or less. See chapter 1, Geographic Divisions for

Analysis for more details.

Age Cohort

Teenager (1617)

0.9%

70

0.8%

Millennial and Gen Z (1836)

37.8%

2,361

28.2%

Gen X (3752)

36.5%

2,843

34.0%

Pre-retirement age Boomer (5364)

19.9%

2,352

28.1%

Seniors (6579)

4.7%

701

8.4%

Elderly (80+)

0.2%

36

0.4%

Sex

Male

53.7%

4,203

50.3%

Female

46.3%

4,160

49.7%

Annual Household Income

<$15,000

8.2%

481

5.8%

$15,000 to $24,999

7.7%

478

5.7%

$25,000 to $34,999

10.1%

721

8.6%

$35,000 to $49,999

12.2%

986

11.8%

$50,000 to $74,999

17.7%

1,500

17.9%

$75,000 to $99,999

13.2%

1,260

15.1%

$100,000+

30.9%

2,743

32.8%

Missing Table continues on next page.

194

2.3%

475

Continued from previous page: Sample of workers ages 16+.

All workers ages 16+

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

8,363

-

Race (Categories)

White non-Hispanic only

56.1%

5,789

69.2%

Black and Black multiracial (incl. Black Hispanic)

30.7%

1,862

22.3%

Other

13.2%

712

8.5%

NHTS imputed race and/or Hispanic status for 74 people. See chapter 1, Assorted Definitions and Notes for more details

on how race is categorized in this report.

Mobility Impairment

Absent

98.1%

8,197

98.0%

Present

1.9%

163

1.9%

Missing

3

0.0%

A mobility impairment is defined as a "condition or handicap that makes it difficult to travel outside of the home."

Driver Status

Nondriver ages 16+

5.2%

257

3.1%

Driver ages 16+

94.8%

8,105

96.9%

Missing

1

0.0%

Vehicle Deficit Category of Household

Zero-vehicle

2.5%

143

1.7%

Deficit (hard or soft)

21.1%

1,086

13.0%

Nondeficit (sufficient/surplus)

76.4%

7,134

85.3%

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than household members ages 16+ (i.e., potential

drivers). See Section 1.6.1 for more details.

Transit Use, Past 30 Days

No

88.3%

7,628

91.2%

Yes

11.7%

729

8.7%

Missing

6

0.1%

Walking, Past 30 Days

No

27.7%

2,179

26.1%

Yes

72.3%

6,164

73.7%

Missing

20

0.2%

476

TRIP, WORK JOURNEY, AND ACCESS/EGRESS TRAVEL SAMPLE TABLES Samples of trips and trip-derived variables (WJs, access/egress legs) are broken down by who is doing the traveling and what kind of travel is being discussed (person trips, vehicle trips, work journeys, access/egress legs).122 Additional subsamples are based on these primary divisions (e.g., nonmotorized person trips made by adults ages 18+). To clarify these relationships, table 210 summarizes the sample sizes of traveler populations and instances of travel by members of those populations. Table 211 provides a more detailed overview of specific subsamples, where these subsamples are used in the report, and the location of more detailed subpopulation sample tables. In addition to tables focusing on trips or other instances of travel, travel behavior is frequently included in tables in a normalized fashion (e.g., trips per capita). In recognition of this reality, we have included sample tables for person trips and vehicle trips by adults ages 18+. These samples were not the independent basis of tables in the report, but were frequently used to calculate figures found in other tables.
122 See Key Terms in chapter 1 for more discussion of the difference between person trips and vehicle trips. Discussions of the methods for deriving work journeys and access/egress legs can be found in chapter 2 and chapter 6.
477

Table 210. Summary of traveler populations and instances of travel.

Traveler Group

Group A. Number of Persons in
Group

Trips B. Total Trips Made by Group

C. VehicleTrips

D. Nonmotorized
Trips

Work
E. Work Journeys

Access/Egress Legs

NMT

F. Non-

H. Non-

motorized G. Motorized motorized

Legs

Legs Travel (D+F)

All Georgians ages 5+

17,681

59,706

40,635

4,480

10,490

952

367

5,432

Ages 16+

15,605

54,271

40,606

4,023

10,490

923

355

4,946

Adults ages 18+

15,222

53,203

40,196

3,927

10,490

903

351

4,830

Children ages 517

2,459

6,503

439

553

-

49

16

602

Workers ages 16+

8,363

31,623

25,484

2,041

10,340

466

218

2,507

Workers ages 18+

8,293

31,356

25,333

2,017

10,340

466

218

2,483

Drivers, all ages

14,292

51,597

40,606

3,394

10,199

586

244

3,980

Drivers ages 16+

14,236

51,597

40,606

3,394

10,199

586

244

3,980

Drivers ages 18+

14,022

50,924

40,196

3,351

10,199

583

243

3,934

* Derived from trips as described in chapters 2 and 6.

Also described as person-trips. Vehicle trips are private auto or rental car trips where the respondent is driving the vehicle. Total motorized trips (all modes except walking and biking) can be calculated by subtracting column D from column B.

478

Table 211. Overview of trip, work journey, and access/egress travel subsamples.

Trip or Instance Category

Traveler Type

Trip or Instance Type

Unweighted Sample Size*

Tables Using Population

Detailed Sample Table(s)

Person-trips (all trips by all modes)

Person-trip

Persons ages 5+

All

59,706 1.5, 1.6, 1.912, 1.14, 1.2024, 7.2, A1518

7.5, 7.6

Person-trip

Persons ages 5+

Loop

1,294 7.1, 7.4

7.1, 7.4

Person-trip

Adults ages 18+

All

53,203 n/a

A2225

Person-trip

Adults ages 18+

Trips entirely within Georgia

50,270 5.65.9

5.6

Person-trip

Children ages 517

School trips

2,593 6.26, 6.2829

6.26

Person-trip

Persons ages 16+

Vehicle-for-hire

205 4.12, 4.13

4.12

Vehicle-trips (motorized trips where respondent is driver)

Vehicle-trip

Persons ages 5+

All

40,635 1.5, 1.6, 1.9, 1.15

A1921

Vehicle-trip

Adults ages 18+

All

40,196 n/a

A2629

Access/Egress Legs (legs to access or egress another mode of transportation, included in data as part of the trip by the parent mode. See chapter 6.)

Access/egress legs

Persons ages 5+

All

1,319 6.8, 6.9

6.8

Access/egress legs

Adults ages 18+

Legs to access/egress transit

949 6.116.13

6.8

Access/egress legs

Adults ages 18+

All

1,254 6.10

6.8

Nonmotorized Trips and Legs (total of nonmotorized trips + nonmotorized access/egress legs)

Person-trips + legs

Persons ages 5+

Nonmotorized

5,432 6.5, 6.6

n/a

Person-trips + legs

Adults ages 18+

Nonmotorized

4,830 6.1521

6.18, 6.20

Person-trips + legs

Children ages 517

Nonmotorized

602 6.30, 6.31, 6.34

6.30, 6.31, 6.34

Work Journey/Commute (note: WJs and commutes differ in distance and duration, but are synonymous for sample size.)

WJ/commute

Adults ages 18+

All (incl. supercommutes >100 mi)

10,463 2.1013, 2.15, 2.20, 2.21, 2.23, 2.30 A3032

WJ/commute

Adults ages 18+

WJ excl. supercommutes >100 mi

10,378 2.1619, 2.25, 2.26, 2.29, 2.3235 n/a

WJ/commute

Adults ages 18+

Complex WJs

6,218 2.16

2.16

WJ/commute

Adults ages 18+

Matched Complex WJs

1,585 2.17

2.16

* Sample size given is for instances of travel (trips, legs, or WJs) rather than the number of individuals in the traveler population (e.g., adults, children). Additional exclusion criteria may be

listed in individual tables. Unless otherwise stated, missing data are omitted from relevant rows/columns rather than the entire table. In addition to tables listed here, trip data are often the denominator in tables of normalized data (e.g., trips per capita, WJ per worker, etc.).

479

Table 212. Trip sample table: All trips by all persons ages 5+ by location.

All trips by all persons ages 5+

Weighted Percent (Nonmissing Observations)
-

Unweighted

Sample Size, N Percent

(All

Observations)

59,706

-

MPO Tier (Residence of Traveler) 1. Atlanta MPO counties

55.7%

17,958 30.1%

2. Medium MPO counties

15.8%

22,160 37.1%

3. Small MPO counties

10.1%

13,563 22.7%

4. Non-MPO counties

18.4%

6,025 10.1%

Addresses are classified by whether the county is part of an MPO. Medium MPOs have an MPO population of

200,0011,000,000 people; small MPOs have a population of 200,000 or less. See chapter 1, Geographic Divisions

for Analysis for more details.

MPO (Residence of Traveler) Albany

1.0%

1,396

2.3%

Athens

2.4%

3,622

6.1%

Atlanta

55.7%

17,958 30.1%

Augusta

3.9%

5,446

9.1%

Brunswick

1.3%

1,756

2.9%

Cartersville

0.8%

1,146

1.9%

Chattanooga

0.7%

404

0.7%

Columbus

2.6%

3,432

5.7%

Dalton

1.1%

1,419

2.4%

Gainesville

2.4%

3,741

6.3%

Hinesville

0.7%

770

1.3%

Macon

1.8%

2,302

3.9%

Rome

0.7%

1,271

2.1%

Savannah

3.8%

5,515

9.2%

Valdosta

1.1%

1,334

2.2%

Warner Robins

1.5%

2,169

3.6%

Non-MPO Table continues on next page.

18.4%

6,025 10.1%

480

Continued from previous page: Sample of trips by all persons ages 5+.

Weighted Percent (Nonmissing Observations)

All trips by all persons ages 5+

-

Trip Location (Based on Origin and Destination)

In Georgia

95.8%

Partly in Georgia

1.5%

Not in Georgia

2.7%

MPO Tier (Trip Origin)

1. Atlanta MPO

55.9%

2. Medium MPOs

15.8%

3. Small MPOs

10.5%

4. Non-MPO

17.8%

Out of state

MPO (Trip Origin)

Albany

1.1%

Athens

2.6%

Atlanta

54.0%

Augusta

3.7%

Brunswick

1.4%

Cartersville

0.8%

Chattanooga

0.5%

Columbus

2.5%

Dalton

1.0%

Gainesville

2.3%

Hinesville Macon

0.7% 1.8%

Rome

0.7%

Savannah

3.8%

Valdosta

1.1%

Warner Robins

1.5%

Non-MPO

17.2%

Out of state

3.5%

Unweighted

Sample Size, N Percent

(All

Observations)

59,706

-

57,007 1,000 1,699

95.5% 1.7% 2.8%

18,085 20,525 12,830
6,077 2,189

30.3% 34.4% 21.5% 10.2%
3.7%

1,337 3,477 18,085 5,110 1,644 1,000
297 3,246 1,285 3,134
725 2,316 1,175 5,261 1,309 2,039 6,077 2,189

2.2% 5.8% 30.3% 8.6% 2.8% 1.7% 0.5% 5.4% 2.2% 5.2% 1.2% 3.9% 2.0% 8.8% 2.2% 3.4% 10.2% 3.7%

481

Table 213. Trip sample table: All trips by all persons ages 5+ by purpose.

All trips by all persons ages 5+ Purpose Home Work commute Other work-related travel Attend school or daycare Transport someone Shopping or errands Medical/dental services Social/recreational or fitness Dining (restaurant or carryout) Community, religious, and volunteer Other Missing Purpose Type Mandatory Household-serving Discretionary Return home Other Missing

Weighted
Percent (Nonmissing Observations)
-

Unweighted

Sample Size, Percent

N

(All

Observations)

59,706

-

33.6% 11.1%
1.0% 4.1% 6.7% 18.7% 1.4% 11.1% 7.9% 3.0% 1.3%

19,520 6,770 698 1,888 3,643
12,092 1,187 6,552 4,954 1,658 722 22

32.7% 11.3%
1.2% 3.2% 6.1% 20.3% 2.0% 11.0% 8.3% 2.8% 1.2% 0.0%

16.3% 26.8% 22.1% 33.6%
1.3%

9,356 16,922 13,164 19,520
722 22

15.7% 28.3% 22.0% 32.7%
1.2% 0.0%

482

Table 214. Trip sample table: All trips by all persons ages 5+ by mode.

Weighted
Percent (Nonmissing Observations)

Unweighted

Sample Size, Percent

N

(All

Observations)

All trips by all persons ages 5+

-

59,706

Mode

Pedestrian (walk/wheelchair)

8.0%

4,201

Bike

0.6%

287

POV, including rental car

85.5%

52,675

School bus

2.9%

1,213

Public transit

1.4%

515

Paratransit

0.1%

56

Other bus

0.2%

141

Taxi/ridehail/limo

0.6%

222

Air

0.2%

105

Other

0.4%

289

Missing

2

Mode Category

POV, including rental car

85.5%

52,675

Nonmotorized (walk, bike, wheelchair)

8.6%

4,490

Public transit or other bus/train

4.6%

1,869

Other ground or water

1.2%

565

Air

0.2%

105

Missing

2

Vehicle or Person Trip

Person trip only

37.9%

19,071

Vehicle trip and person trip

62.1%

40,635

See chapter 1, Key Terms for more explanation of the relationship between person trips and vehicle trips.

-
7.0% 0.5% 88.2% 2.0% 0.9% 0.1% 0.2% 0.4% 0.2% 0.5% 0.0%
88.2% 7.5% 3.1% 0.9% 0.2% 0.0%
31.9% 68.1%

483

Table 215. Trip sample table: All trips by all persons ages 5+ by demographic factors.

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N Percent
(All Observations)

All trips by all persons ages 5+

-

59,706

-

Sex

Male

48.4%

27,472 46.0%

Female

51.6%

32,234 54.0%

NHTS imputed sex for 16 people.

Age

Child 515

12.5%

5,435

9.1%

Teen 1617

2.5%

1,068

1.8%

Adult 1864

72.6%

38,862 65.1%

Senior 6579

10.7%

12,333 20.7%

Elderly 80+

1.6%

2,008

3.4%

Age by Sex

Male: child 515

6.7%

2,966

5.0%

Male: teen 1617

1.2%

524

0.9%

Male: adult 1864

34.6%

17,067 28.6%

Male: senior 6579

5.2%

5,958 10.0%

Male: elderly 80+

0.7%

957

1.6%

Female: child 515

5.8%

2,469

4.1%

Female: teen 1617

1.3%

544

0.9%

Female: adult 1864

38.0%

21,795 36.5%

Female: senior 6579

5.6%

6,375 10.7%

Female: elderly 80+

0.9%

1,051

1.8%

Age Cohort (Adults Only)

Millennial and Gen Z (1836)

32.8%

11,175 18.7%

Gen X (3752)

31.3%

13,382 22.4%

Pre-retirement age Boomer (5364)

21.3%

14,305 24.0%

Retirement age (65+)

14.6%

14,341 24.0%

{Children & teens}

6,503 10.9%

Driver Status by Age

Driver ages 18+

79.1%

50,924 85.3%

Nondriver ages 18+

5.9%

2,279

3.8%

Driver ages 1617

1.4%

673

1.1%

Nondriver ages 1617

1.2%

395

0.7%

Child ages 515

12.5%

5,435

9.1%

NHTS does not ask about drivers' licensing; rather, they ask "do you/does this person drive?" Some children under 16

were reported as drivers, perhaps due to learner's permits. Unless otherwise stated, drivers in the report refers only to

drivers ages 16+.

Table continues on next page.

484

Continued from previous page: Sample of trips by all persons ages 5+.

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N Percent
(All Observations)

All trips by all persons ages 5+

-

59,706

-

Worker Status by Age

Nonworker ages 18+

28.0%

21,847 36.6%

Worker ages 18+

57.0%

31,356 52.5%

Nonworker ages 1617

2.0%

801

1.3%

Worker ages 1617

0.5%

267

0.4%

Child under 16

12.5%

5,435

9.1%

Race (Categories)

White non-Hispanic only

54.7%

41,603 69.7%

Black and Black multiracial (incl. Black Hisp.)

32.2%

13,330 22.3%

Other

13.0%

4,773

8.0%

NHTS imputed race and/or Hispanic status for 74 people. See chapter 1, Assorted Definitions and Notes for more

details on how race is categorized in this report.

Race/Ethnicity (used in chapter 5, Equity)

White non-Hispanic only

54.7%

41,603 69.7%

Black and Black multiracial (excl. Black Hisp.)

31.1%

13,101 21.9%

Hispanic (any race)

9.1%

2,296

3.8%

Asian or other

5.1%

2,706

4.5%

Mobility Impairment

Absent

94.2%

55,664 93.2%

Present

5.8%

4,019

6.7%

Missing

23

0.0%

Annual Household Income

<$15,000

12.8%

5,587

9.4%

$15,000 to $24,999

8.7%

4,428

7.4%

$25,000 to $34,999

10.0%

5,182

8.7%

$35,000 to $49,999

12.3%

7,281 12.2%

$50,000 to $74,999

16.3%

10,536 17.6%

$75,000 to $99,999

12.2%

8,236 13.8%

$100,000+

27.7%

16,907 28.3%

Missing

1,549

2.6%

Education Level

High school or less

30.9%

14,333 24.0%

Some college or associate degree

28.5%

15,289 25.6%

Bachelor's degree

21.9%

12,999 21.8%

Graduate or professional degree

18.7%

12,568 21.0%

Missing

4,517

7.6%

Age <14

0.0%

Table continues on next page.

485

Continued from previous page: Sample of trips by all persons ages 5+.

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N Percent
(All Observations)

All trips by all persons ages 5+

-

59,706

-

College-educated

No 4-year degree

59.4%

29,622 49.6%

Bachelor's or higher

40.6%

25,567 42.8%

Missing

4,517

7.6%

Age <14

4,450

7.5%

Immigrant

Nonimmigrant (born in US)

89.8%

55,487 92.9%

Immigrant (born outside of US)

10.2%

4,206

7.0%

Missing

13

0.0%

Immigrant by Education Level

US HS or less

27.9%

13,487 22.6%

US some college/assoc.

26.5%

14,540 24.4%

US bachelor's+

34.7%

23,225 38.9%

Imm. HS or less

2.9%

846

1.4%

Imm. some college/assoc.

2.3%

876

1.5%

Imm. bachelor's+

5.7%

2,342

3.9%

N/A (age <14) or missing

4,390

7.4%

Caregiver Status

Noncaregiver

65.4%

40,602 68.0%

Caregiver, youngest child ages 015

34.6%

12,601 21.1%

N/A (child under 18)

6,503 10.9%

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age

22+ in a household with a child of 515 years old.

Caregiver Status by Gender

Male noncaregiver

32.3%

18,754 31.4%

Female noncaregiver

15.3%

5,228

8.8%

Male caregiver

33.1%

21,848 36.6%

Female caregiver

19.3%

7,373 12.3%

Missing

6,503 10.9%

Caregiver Status by Age of Youngest Child

Noncaregiver

70.6%

47,105 78.9%

Youngest ages 04

14.1%

5,687

9.5%

Youngest ages 515 Table continues on next page.

15.3%

6,914 11.6%

486

Continued from previous page: Sample of trips by all persons ages 5+

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N Percent
(All Observations)

All trips by all persons ages 5+

-

59,706

-

Vehicle Deficit Category of Household

Zero-vehicle

4.3%

1,804

3.0%

Deficit (hard or soft)

23.9%

8,730 14.6%

Nondeficit (sufficient/surplus)

71.8%

49,172 82.4%

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than household members ages 16+ (i.e.,

potential drivers). See chapter 1, Vehicle Ownership for more details.

Vehicle Deficit Category by Household Size

Nondeficit, single potential driver

15.5%

11,675 19.6%

Nondeficit, 2+ potential drivers

56.4%

37,497 62.8%

Deficit

23.9%

8,730 14.6%

Zero-vehicle

4.3%

1,804

3.0%

Transit Use, Past 30 Days

No

86.1%

53,866 90.2%

Yes

13.9%

5,796

9.7%

Missing

44

0.1%

Walking, Past 30 Days No

22.9%

13,826 23.2%

Yes

77.1%

45,688 76.5%

Missing

192

0.3%

Biking, Past 30 Days No

89.7%

53,934 90.3%

Yes

10.3%

5,751

9.6%

Missing

21

0.0%

487

Table 216. Trip sample table: All vehicle trips by all persons ages 5+ by location.

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N Percent
(All Observations)

All vehicle trips by all persons ages 5+

40,635

-

MPO Tier (Residence of Traveler) 1. Atlanta MPO counties

55.9%

11,937

29.4%

2. Medium MPO counties

15.3%

15,095

37.1%

3. Small MPO counties

10.3%

9,460

23.3%

4. Non-MPO counties

18.6%

4,143

10.2%

Addresses are classified by whether the county is part of an MPO. Medium MPOs have an MPO population of

200,0011,000,000 people; small MPOs have a population of 200,000 or less. See chapter 1, Geographic Divisions

for Analysis for more details.

MPO (Residence of Traveler) Albany

1.1%

1,001

2.5%

Athens

2.2%

2,336

5.7%

Atlanta

55.9%

11,937

29.4%

Augusta

3.7%

3,720

9.2%

Brunswick

1.4%

1,219

3.0%

Cartersville

0.9%

845

2.1%

Chattanooga

0.8%

319

0.8%

Columbus

2.6%

2,443

6.0%

Dalton

1.0%

972

2.4%

Gainesville

2.4%

2,577

6.3%

Hinesville

0.8%

567

1.4%

Macon

1.7%

1,579

3.9%

Rome

0.7%

855

2.1%

Savannah

3.5%

3,700

9.1%

Valdosta

1.2%

920

2.3%

Warner Robins

1.5%

1,502

3.7%

Non-MPO Table continues on next page.

18.6%

4,143

10.2%

488

Continued from previous page: Sample of vehicle trips by all persons ages 5+.

Weighted

Unweighted

Percent (Nonmissing

Sample Size, N

Percent (All

Observations)

Observations)

All vehicle trips by all persons ages 5+

40,635

-

Trip Location (Based on Origin and Destination)

In Georgia

97.2%

39,348

96.8%

Partly in Georgia

1.2%

641

1.6%

Not in Georgia

1.6%

646

1.6%

MPO Tier (Trip Origin)

1. Atlanta MPO

56.2%

12,287

30.2%

2. Medium MPOs

15.3%

14,151

34.8%

3. Small MPOs

10.8%

9,094

22.4%

4. Non-MPO

17.6%

4,143

10.2%

Out of state

960

2.4%

MPO (Trip Origin)

Albany

1.2%

970

2.4%

Athens

2.4%

2,310

5.7%

Atlanta

55.0%

12,287

30.2%

Augusta

3.6%

3,537

8.7%

Brunswick

1.5%

1,173

2.9%

Cartersville

0.8%

723

1.8%

Chattanooga

0.6%

233

0.6%

Columbus

2.5%

2,324

5.7%

Dalton

0.9%

889

2.2%

Gainesville

2.4%

2,187

5.4%

Hinesville

0.8%

537

1.3%

Macon

1.8%

1,626

4.0%

Rome

0.7%

801

2.0%

Savannah

3.5%

3,560

8.8%

Valdosta

1.3%

930

2.3%

Warner Robins

1.6%

1,445

3.6%

Non-MPO

17.2%

4,143

10.2%

Out of state

2.2%

960

2.4%

489

Table 217. Trip sample table: All vehicle trips by all persons ages 5+ by purpose.

All vehicle trips by all persons ages 5+ Purpose Home Work commute Other work-related travel Attend school or daycare Transport someone Shopping or errands Medical/dental services Social/recreational or fitness Dining (restaurant or carryout) Community, religious, and volunteer Other Missing Purpose Type Mandatory Household-serving Discretionary Return home Other Missing

Weighted Percent (Nonmissing Observations)

Unweighted

Sample Size, N Percent

(All

Observations)

40,635

-

33.8% 14.9%
1.2% 1.3% 8.4% 20.5% 1.4% 7.8% 7.5% 2.7% 0.6%

13,463 5,900 530 401 2,987 9,008 801 3,123 3,113 1,060 235 14

33.1% 14.5%
1.3% 1.0% 7.4% 22.2% 2.0% 7.7% 7.7% 2.6% 0.6% 0.0%

17.4% 30.3% 17.9% 33.8%
0.6%

6,831 12,796
7,296 13,463
235 14

16.8% 31.5% 18.0% 33.1%
0.6% 0.0%

490

Table 218. Trip sample table: All vehicle trips by all persons ages 5+ by demographic factors.

All vehicle trips by all persons ages 5+

Weighted Percent (Nonmissing Observations)

Unweighted

Sample Size, N Percent

(All

Observations)

40,635

-

Sex

Male

49.7%

19,343

47.6%

Female NHTS imputed sex for 16 people.

50.3%

21,292

52.4%

Age Child 515

0.1%

29

0.1%

Teen 1617

1.2%

410

1.0%

Adult 1864

84.6%

29,696

73.1%

Senior 6579

12.6%

9,214

22.7%

Elderly 80+

1.5%

1,286

3.2%

Age by Sex Male: child 515

0.1%

13

0.0%

Male: teen 1617

0.5%

182

0.4%

Male: adult 1864

41.6%

13,553

33.4%

Male: senior 6579

6.6%

4,902

12.1%

Male: elderly 80+

0.9%

693

1.7%

Female: child 515

0.0%

16

0.0%

Female: teen 1617

0.7%

228

0.6%

Female: adult 1864

43.0%

16,143

39.7%

Female: senior 6579

6.0%

4,312

10.6%

Female: elderly 80+

0.6%

593

1.5%

Age Cohort (Adults Only)

Millennial and Gen Z (1836)

30.8%

8,110

20.0%

Gen X (3752)

33.4%

10,576

26.0%

Pre-retirement age Boomer (5364)

21.5%

11,010

27.1%

Retirement age (65+)

14.3%

10,500

25.8%

{Children & teens}

439

1.1%

Driver Status by Age

Driver ages 18+

98.7%

40,196

98.9%

Nondriver ages 18+

0.0%

0.0%

Driver ages 1617

1.2%

410

1.0%

Nondriver ages 1617

0.0%

0.0%

Child ages 515

0.1%

29

0.1%

NHTS does not ask about drivers' licensing; rather, they ask "do you/does this person drive?" Some children under 16

were reported as drivers, perhaps due to learner's permits. Unless otherwise stated, drivers in the report refers only to

drivers ages 16+.

Table continues on next page.

491

Continued from previous page: Sample of vehicle trips by all persons ages 5+.

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N Percent
(All Observations)

All vehicle trips by all persons ages 5+

40,635

-

College-educated

No 4-year degree

55.9%

20,785

51.2%

Bachelor's or higher

44.1%

19,825

48.8%

Missing

25

0.1%

Age <14

0.0%

Immigrant

Nonimmigrant (born in US)

88.7%

37,682

92.7%

Immigrant (born outside of US)

11.3%

2,944

7.2%

Missing

9

0.0%

Immigrant by Education Level

US HS or less

22.1%

8,292

20.4%

US some college/assoc.

28.6%

11,305

27.8%

US bachelor's+

38.0%

18,076

44.5%

Imm. HS or less

2.3%

510

1.3%

Imm. some college/assoc.

2.8%

678

1.7%

Imm. bachelor's+

6.1%

1,749

4.3%

N/A (age <14) or missing

25

0.1%

Caregiver Status

Noncaregiver

62.5%

30,005

73.8%

Caregiver, youngest child ages 015

37.5%

10,191

25.1%

N/A (child under 18)

439

1.1%

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age

22+ in a household with a child of 515 years old.

Caregiver Status by Gender

Male noncaregiver

32.5%

14,767

36.3%

Female noncaregiver

17.3%

4,381

10.8%

Male caregiver

30.0%

15,238

37.5%

Female caregiver

20.2%

5,810

14.3%

Missing

439

1.1%

Caregiver Status by Age of Youngest Child

Noncaregiver

63.0%

30,444

74.9%

Youngest ages 04

17.8%

4,583

11.3%

Youngest ages 515 Table continues on next page.

19.2%

5,608

13.8%

492

Continued from previous page: Sample of vehicle trips by all persons ages 5+.

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N Percent
(All Observations)

All vehicle trips by all persons ages 5+

40,635

-

Worker Status by Age

Nonworker ages 18+

27.6%

14,863

36.6%

Worker ages 18+

71.1%

25,333

62.3%

Nonworker ages 1617

0.8%

259

0.6%

Worker ages 1617

0.4%

151

0.4%

Child under 16

0.1%

29

0.1%

Race (Categories)

White non-Hispanic only

56.8%

29,034

71.5%

Black and Black multiracial (incl. Black Hisp.)

31.4%

8,781

21.6%

Other

11.8%

2,820

6.9%

NHTS imputed race and/or Hispanic status for 74 people. See chapter 1, Assorted Definitions and Notes for more

details on how race is categorized in this report.

Race/Ethnicity (used in Chapter 5: Equity)

White non-Hispanic only

56.8%

29,034

71.5%

Black and Black multiracial (excl. Black Hisp.)

30.3%

8,650

21.3%

Hispanic (any race)

8.2%

1,322

3.3%

Asian or other

4.7%

1,629

4.0%

Mobility Impairment

Absent

95.5%

38,412

94.5%

Present

4.5%

2,205

5.4%

Missing

18

0.0%

Annual Household Income

<$15,000

9.1%

2,944

7.2%

$15,000 to $24,999

8.2%

2,970

7.3%

$25,000 to $34,999

10.5%

3,636

8.9%

$35,000 to $49,999

13.1%

5,222

12.9%

$50,000 to $74,999

18.1%

7,585

18.7%

$75,000 to $99,999

13.0%

5,797

14.3%

$100,000+

28.0%

11,350

27.9%

Missing

1,131

2.8%

Education Level

High school or less

24.4%

8,802

21.7%

Some college or associate degree

31.5%

11,983

29.5%

Bachelor's degree

23.8%

10,192

25.1%

Graduate or professional degree

20.3%

9,633

23.7%

Missing

25

0.1%

Age <14 Table continues on next page.

0

0.0%

493

Continued from previous page: Sample of vehicle trips by all persons ages 5+.

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N Percent
(All Observations)

All vehicle trips by all persons ages 5+

40,635

-

College-educated

No 4-year degree

55.9%

20,785

51.2%

Bachelor's or higher

44.1%

19,825

48.8%

Missing

25

0.1%

Age <14

0.0%

Immigrant

Nonimmigrant (born in US)

88.7%

37,682

92.7%

Immigrant (born outside of US)

11.3%

2,944

7.2%

Missing

9

0.0%

Immigrant by Education Level

US HS or less

22.1%

8,292

20.4%

US some college/assoc.

28.6%

11,305

27.8%

US bachelor's+

38.0%

18,076

44.5%

Imm. HS or less

2.3%

510

1.3%

Imm. some college/assoc.

2.8%

678

1.7%

Imm. bachelor's+

6.1%

1,749

4.3%

N/A (age <14) or missing

25

0.1%

Caregiver Status

Noncaregiver

62.5%

30,005

73.8%

Caregiver, youngest child ages 015

37.5%

10,191

25.1%

N/A (child under 18)

439

1.1%

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age

22+ in a household with a child of 515 years old.

Caregiver Status by Gender

Male noncaregiver

32.5%

14,767

36.3%

Female noncaregiver

17.3%

4,381

10.8%

Male caregiver

30.0%

15,238

37.5%

Female caregiver

20.2%

5,810

14.3%

Missing

439

1.1%

Caregiver Status by Age of Youngest Child

Noncaregiver

63.0%

30,444

74.9%

Youngest ages 04

17.8%

4,583

11.3%

Youngest ages 515 Table continues on next page.

19.2%

5,608

13.8%

494

Continued from previous page: Sample of vehicle trips by all persons ages 5+.

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N Percent
(All Observations)

All vehicle trips by all persons ages 5+

40,635

-

Vehicle Deficit Category of Household

Zero-vehicle

0.4%

115

0.3%

Deficit (hard or soft)

20.1%

4,888

12.0%

Nondeficit (sufficient/surplus)

79.5%

35,632

87.7%

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than household members ages 16+ (i.e.,

potential drivers). See chapter 1, Vehicle Ownership for more details.

Vehicle Deficit Category by Household Size

Nondeficit, single potential driver

18.7%

9,386

23.1%

Nondeficit, 2+ potential drivers

60.8%

26,246

64.6%

Deficit

20.1%

4,888

12.0%

Zero-vehicle

0.4%

115

0.3%

Transit Use, Past 30 Days

No

91.7%

38,348

94.4%

Yes

8.3%

2,263

5.6%

Missing

24

0.1%

Walking, Past 30 Days No

25.6%

10,058

24.8%

Yes

74.4%

30,458

75.0%

Missing

119

0.3%

Biking, Past 30 Days No

94.2%

38,158

93.9%

Yes

5.8%

2,471

6.1%

Missing

.

6

0.0%

495

Table 219. Trip sample table: All trips by all persons ages 18+ by location.

All trips by adults ages 18+

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N
53,203

Percent (All
Observations)
-

MPO Tier (Residence of Traveler) 1. Atlanta MPO counties

55.8%

15,850

29.8%

2. Medium MPO counties

15.6%

19,661

37.0%

3. Small MPO counties

10.1%

12,227

23.0%

4. Non-MPO counties

18.5%

5,465

10.3%

Addresses are classified by whether the county is part of an MPO. Medium MPOs have an MPO population of

200,0011,000,000 people; small MPOs have a population of 200,000 or less. See chapter 1, Geography Divisions for

Analysis for more details.

MPO (Residence of Traveler) Albany

1.1%

1,285

2.4%

Athens

2.4%

3,292

6.2%

Atlanta

55.8%

15,850

29.8%

Augusta

3.7%

4,747

8.9%

Brunswick

1.3%

1,606

3.0%

Cartersville

0.9%

1,059

2.0%

Chattanooga

0.8%

375

0.7%

Columbus

2.5%

3,031

5.7%

Dalton

1.0%

1,250

2.3%

Gainesville

2.4%

3,291

6.2%

Hinesville

0.7%

679

1.3%

Macon

1.7%

2,070

3.9%

Rome

0.8%

1,135

2.1%

Savannah

3.8%

4,925

9.3%

Valdosta

1.1%

1,202

2.3%

Warner Robins

1.5%

1,941

3.6%

Non-MPO Table continues on next page.

18.5%

5,465

10.3%

496

Continued from previous page: Sample of trips by all adults ages 18+.

Weighted

Percent (Nonmissing

Observations)

All trips by adults ages 18+

Trip Location (Based on Origin and Destination)

In Georgia

95.7%

Partly in Georgia

1.6%

Not in Georgia

2.8%

MPO Tier (Trip Origin)

1. Atlanta MPO

55.9%

2. Medium MPOs

15.8%

3. Small MPOs

10.5%

4. Non-MPO

17.8%

Out of state

MPO (Trip Origin)

Albany

2.7%

Athens

53.9%

Atlanta

3.6%

Augusta

1.3%

Brunswick

0.8%

Cartersville

0.6%

Chattanooga

2.4%

Columbus

1.0%

Dalton

2.2%

Gainesville

0.6%

Hinesville

1.8%

Macon

0.7%

Rome

3.7%

Savannah

1.2%

Valdosta

1.5%

Warner Robins

17.2%

Non-MPO

3.6%

Out of state

Unweighted Sample Size, N
53,203

Percent (All
Observations)
-

50,724 930
1,549

95.3% 1.7% 2.9%

15,992 18,171 11,532
5,507 2,001

30.1% 34.2% 21.7% 10.4%
3.8%

1,229 3,175 15,992 4,432 1,493
920 280 2,862 1,121 2,739 633 2,086 1,044 4,683 1,188 1,818 5,507 2,001

2.3% 6.0% 30.1% 8.3% 2.8% 1.7% 0.5% 5.4% 2.1% 5.1% 1.2% 3.9% 2.0% 8.8% 2.2% 3.4% 10.4% 3.8%

497

Table 220. Trip sample table: All trips by all persons ages 18+ by purpose.

All trips by adults ages 18+ Purpose Home Work commute Other work-related travel Attend school or daycare Transport someone Shopping or errands Medical/dental services Social/recreational or fitness Dining (restaurant or carryout) Community, religious, and volunteer Other Missing Purpose Type Mandatory Household-serving Discretionary Return home Other Missing

Weighted Percent (Nonmissing Observations)
32.8% 13.0%
1.2% 1.4% 7.1% 19.9% 1.6% 10.8% 8.2% 3.0% 1.1%
15.5% 28.6% 22.0% 32.8%
1.1%

Unweighted Sample Size, N
53,203

Percent (All
Observations)
-

17,050 6,715 693 455 3,371
11,465 1,130 5,723 4,541 1,480 558 22

32.0% 12.6%
1.3% 0.9% 6.3% 21.5% 2.1% 10.8% 8.5% 2.8% 1.0% 0.0%

7,863 15,966 11,744 17,050
558 22

14.8% 30.0% 22.1% 32.0%
1.0% 0.0%

498

Table 221. Trip sample table: All trips by all persons ages 18+ by mode.

Weighted

Unweighted

Percent (Nonmissing

Sample Size, N

Percent (All

Observations)

Observations)

All trips by adults ages 18+

53,203

-

Mode

Pedestrian (walk/wheelchair)

7.9%

3,710

7.0%

Bike

0.6%

225

0.4%

POV, including rental car

88.0%

47,929

90.1%

School bus

0.4%

111

0.2%

Public transit

1.6%

495

0.9%

Paratransit

0.1%

53

0.1%

Other bus

0.2%

121

0.2%

Taxi/ridehail/limo

0.6%

203

0.4%

Air

0.2%

105

0.2%

Other

0.4%

249

0.5%

Missing

2

0.0%

Mode Category

POV, including rental car

88.0%

47,929

90.1%

Nonmotorized (walk, bike, wheelchair)

8.4%

3,937

7.4%

Public transit or other bus/train

2.2%

727

1.4%

Other ground or water

1.2%

503

0.9%

Air

0.2%

105

0.2%

Missing

2

0.0%

Vehicle or Person Trip

Person trip only

27.8%

13,007

24.4%

Vehicle trip and person trip

72.2%

40,196

75.6%

See chapter 1, Key Terms for more explanation of the relationship between person trips and vehicle trips.

499

Table 222. Trip sample table: All trips by all persons ages 18+ by demographic factors.

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N

Percent (All
Observations)

All trips by adults ages 18+

53,203

-

Sex

Male

47.6%

23,982

45.1%

Female

52.4%

29,221

54.9%

NHTS imputed sex for 16 people.

Age

Child 515

Teen 1617

Adult 1864

85.4%

38,862

73.0%

Senior 6579

12.6%

12,333

23.2%

Elderly 80+

1.9%

2,008

3.8%

Age by Sex

Male: adult 1864

40.7%

17,067

32.1%

Male: senior 6579

6.1%

5,958

11.2%

Male: elderly 80+

0.8%

957

1.8%

Female: adult 1864

44.7%

21,795

41.0%

Female: senior 6579

6.5%

6,375

12.0%

Female: elderly 80+

1.1%

1,051

2.0%

Age Cohort (Adults Only)

Millennial and Gen Z (1836)

32.8%

11,175

21.0%

Gen X (3752)

31.3%

13,382

25.2%

Pre-retirement age Boomer (5364)

21.3%

14,305

26.9%

Retirement age (65+)

14.6%

14,341

27.0%

Driver Status by Age

Driver ages 18+

93.1%

50,924

95.7%

Nondriver ages 18+

6.9%

2,279

4.3%

NHTS does not ask about drivers' licensing; rather, they ask "do you/does this person drive?" Some children under 16

were reported as drivers, perhaps due to learner's permits. Unless otherwise stated, drivers in the report refers only to

drivers ages 16+.

Table continues on next page.

500

Continued from previous page: Sample of trips by all adults ages 18+.

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N

Percent (All
Observations)

All trips by adults ages 18+

53,203

-

Worker Status by Age

Nonworker ages 18+

32.9%

21,847

41.1%

Worker ages 18+

67.1%

31,356

58.9%

Race (Categories)

White non-Hispanic only

55.8%

37,628

70.7%

Black and Black multiracial (incl. Black Hisp.)

31.7%

11,625

21.9%

Other

12.5%

3,950

7.4%

NHTS imputed race and/or Hispanic status for 74 people. See chapter 1, Assorted Definitions and Notes for more details

on how race is categorized in this report.

Race/Ethnicity (used in chapter 5, Equity)

White non-Hispanic only

55.8%

37,628

70.7%

Black and Black multiracial (excl. Black Hisp.)

30.8%

11,474

21.6%

Hispanic (any race)

8.6%

1,822

3.4%

Asian or other

4.9%

2,279

4.3%

Mobility Impairment

Absent

93.4%

49,257

92.6%

Present

6.6%

3,923

7.4%

Missing

23

Annual Household Income

<$15,000

12.7%

4,988

9.4%

$15,000 to $24,999

8.5%

3,990

7.5%

$25,000 to $34,999

10.1%

4,739

8.9%

$35,000 to $49,999

12.7%

6,597

12.4%

$50,000 to $74,999

16.6%

9,595

18.0%

$75,000 to $99,999

12.5%

7,340

13.8%

$100,000+

26.9%

14,467

27.2%

Missing

1,487

2.8%

Education Level

High school or less

27.0%

12,295

23.1%

Some college or associate degree

30.1%

15,283

28.7%

Bachelor's degree

23.2%

12,999

24.4%

Graduate or professional degree

19.7%

12,566

23.6%

Missing Table continues on next page.

60

0.1%

501

Continued from previous page: Sample of trips by all adults ages 18+.

Weighted

Percent

(Nonmissing

Observations)

All trips by adults ages 18+

College-educated

No 4-year degree

57.1%

Unweighted Sample Size, N
53,203

Percent (All
Observations)
-

27,578

51.8%

Bachelor's or higher Immigrant Nonimmigrant (born in US)

42.9% 88.6%

25,565 49,199

48.1% 92.5%

Immigrant (born outside of US) Missing Immigrant by Education Level US HS or less US some college/assoc. US bachelor's+

11.4%
24.2% 27.6% 36.8%

3,993 11
11,535 14,407 23,223

7.5% 0.0%
21.7% 27.1% 43.6%

Imm. HS or less Imm. some college/assoc. Imm. bachelor's+

2.8% 2.5% 6.1%

760 876 2,342

1.4% 1.6% 4.4%

N/A (age <14) or missing Caregiver Status Noncaregiver

65.4%

60 40,602

0.1% 76.3%

Caregiver, youngest child ages 015

34.6%

12,601

23.7%

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a household with a child of 515 years old.

Caregiver Status by Gender Male noncaregiver Female noncaregiver

32.3% 15.3%

18,754 5,228

35.2% 9.8%

Male caregiver Female caregiver Missing

33.1% 19.3%

21,848 7,373

41.1% 13.9%

Caregiver Status by Age of Youngest Child Noncaregiver Youngest ages 04

65.4% 16.6%

40,602 5,687

76.3% 10.7%

Youngest ages 515 Table continues on next page.

18.0%

6,914

13.0%

502

Continued from previous page: Sample of trips by all adults ages 18+.

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size, N

Percent (All
Observations)

All trips by adults ages 18+

53,203

-

Vehicle Deficit Category of Household

Zero-vehicle

4.1%

1,603

3.0%

Deficit (hard or soft)

23.4%

7,517

14.1%

Nondeficit (sufficient/surplus)

72.5%

44,083

82.9%

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than household members ages 16+ (i.e., potential

drivers). See chapter 1, Vehicle Ownership for more details.

Vehicle Deficit Category by Household Size

Nondeficit, single potential driver

16.2%

10,870

20.4%

Nondeficit, 2+ potential drivers

56.3%

33,213

62.4%

Deficit

23.4%

7,517

14.1%

Zero-vehicle

4.1%

1,603

3.0%

Transit Use, Past 30 Days

No

87.5%

48,610

91.4%

Yes

12.5%

4,549

8.6%

Missing

44

0.1%

Walking, Past 30 Days No

23.4%

12,378

23.3%

Yes

76.6%

40,645

76.4%

Missing

180

0.3%

Biking, Past 30 Days No

93.3%

49,577

93.2%

Yes

6.7%

3,605

6.8%

Missing

21

0.0%

503

Table 223. Trip sample table: All vehicle trips by all persons ages 18+ by location.

All vehicle trips by adults ages 18+

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size,
N
40,196

Percent (All
Observations)

MPO Tier (Residence of Traveler) 1. Atlanta MPO counties

55.8%

11,790

29.3%

2. Medium MPO counties

15.3%

14,926

37.1%

3. Small MPO counties

10.3%

9,358

23.3%

4. Non-MPO counties

18.7%

4,122

10.3%

Addresses are classified by whether the county is part of an MPO. Medium MPOs have an MPO population of

200,0011,000,000 people; small MPOs have a population of 200,000 or less. See chapter 1, Geographic

Divisions for Analysis for more details.

MPO (Residence of Traveler) Albany

1.1%

989

2.5%

Athens

2.2%

2,290

5.7%

Atlanta

55.8%

11,790

29.3%

Augusta

3.8%

3,692

9.2%

Brunswick

1.3%

1,204

3.0%

Cartersville

0.9%

841

2.1%

Chattanooga

0.8%

311

0.8%

Columbus

2.6%

2,430

6.0%

Dalton

1.0%

959

2.4%

Gainesville

2.4%

2528

6.3%

Hinesville

0.8%

567

1.4%

Macon

1.7%

1,554

3.9%

Rome

0.7%

846

2.1%

Savannah

3.5%

3,675

9.1%

Valdosta

1.2%

907

2.3%

Warner Robins

1.5%

1,491

3.7%

Non-MPO

18.7%

4,122

10.3%

Table continues on next page.

504

Continued from previous page: Sample of vehicle trips by all adults ages 18+

All vehicle trips by adults ages 18+

Trip Location (Based on Origin and Destination)

In Georgia

97.2%

Partly in Georgia

1.2%

Not in Georgia

1.6%

MPO Tier (Trip Origin)

1. Atlanta MPO

56.1%

2. Medium MPOs

15.4%

3. Small MPOs

10.8%

4. Non-MPO

17.7%

Out of state

MPO (Trip Origin)

Albany

1.2%

Athens

2.4%

Atlanta

54.9%

Augusta

3.6%

Brunswick

1.4%

Cartersville

0.8%

Chattanooga

0.6%

Columbus

2.5%

Dalton

0.9%

Gainesville

2.3%

Hinesville

0.8%

Macon

1.8%

Rome

0.7%

Savannah

3.5%

Valdosta

1.3%

Warner Robins

1.7%

Non-MPO

17.3%

Out of state

2.2%

40,196
38,920 634 642
12,140 13,988
8,994 4,120
954
958 2,264 12,140 3,508 1,158
720 228 2,311 877 2,142 537 1,600 791 3,535 917 1,436 4,120 954

-
96.8% 1.6% 1.6%
30.2% 34.8% 22.4% 10.2%
2.4%
2.4% 5.6% 30.2% 8.7% 2.9% 1.8% 0.6% 5.7% 2.2% 5.3% 1.3% 4.0% 2.0% 8.8% 2.3% 3.6% 10.2% 2.4%

505

Table 224. Trip sample table: All vehicle trips by all persons ages 18+ by purpose.

All vehicle trips by adults ages 18+ Purpose Home Work commute Other work-related travel Attend school or daycare Transport someone Shopping or errands Medical/dental services Social/recreational or fitness Dining (restaurant or carryout) Community, religious, and volunteer Other Missing Purpose Type Mandatory Household-serving Discretionary Return home Other Missing

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size,
N
40,196

Percent (All
Observations)
-

33.7% 15.0%
1.3% 1.1% 8.4% 20.6% 1.5% 7.7% 7.4% 2.7% 0.6%

13,291 5,868 528 311 2,967 8,969 801 3,081 3,082 1,052 232 14

33.1% 14.6%
1.3% 0.8% 7.4% 22.3% 2.0% 7.7% 7.7% 2.6% 0.6% 0.0%

17.4% 30.5% 17.9% 33.7%
0.6%

6,707 12,737
7,215 13,291
232 14

16.7% 31.7% 17.9% 33.1%
0.6% 0.0%

506

Table 225. Trip sample table: All vehicle trips by all persons ages 18+ by demographic factors.

Weighted Percent (Nonmissing Observations)

Unweighted Sample Size,
N

Percent (All
Observations)

All vehicle trips by adults ages 18+

40,196

-

Sex

Male

49.8%

19,148

47.6%

Female NHTS imputed sex for 16 people.

50.2%

21,048

52.4%

Age

Child 515

0.0%

Teen 1617

0.0%

Adult 1864

85.7%

29,696

73.9%

Senior 6579

12.8%

9,214

22.9%

Elderly 80+

1.5%

1,286

3.2%

Age by Sex

Male: adult 1864

42.2%

13,553

33.7%

Male: senior 6579

6.7%

4,902

12.2%

Male: elderly 80+

0.9%

693

1.7%

Female: adult 1864

43.5%

16,143

40.2%

Female: senior 6579

6.1%

4,312

10.7%

Female: elderly 80+

0.6%

593

1.5%

Age Cohort (Adults Only)

Millennial and Gen Z (1836)

30.8%

8,110

20.2%

Gen X (3752)

33.4%

10,576

26.3%

Pre-retirement age Boomer (5364)

21.5%

11,010

27.4%

Retirement age (65+)

14.3%

10,500

26.1%

Driver Status by Age

Driver ages 18+

Nondriver ages 18+ NHTS does not ask about drivers' licensing; rather, they ask "do you/does this person drive?" Some children

under 16 were reported as drivers, perhaps due to learner's permits. Unless otherwise stated, drivers in the

report refers only to drivers ages 16+.

Table continues on next page.

507

Continued from previous page: Sample of weekday trips by all adults ages 5+.

Weighted Unweighted

Percent

Sample Size, Percent

(Nonmissing

N

(All

Observations)

Observations)

All vehicle trips by adults ages 18+

40,196

-

Worker Status by Age

Nonworker ages 18+

28.0%

14,863

37.0%

Worker ages 18+

72.0%

25,333

63.0%

Race (Categories)

White non-Hispanic only

56.5%

28,685

71.4%

Black and Black multiracial (incl. Black Hisp.) 31.6%

8,716

21.7%

Other

11.9%

2,795

7.0%

NHTS imputed race and/or Hispanic status for 74 people. See chapter 1, Assorted Definitions and Notes for

more details on how race is categorized in this report.

Race/Ethnicity (used in chapter 5, Equity)

White non-Hispanic only

56.5%

28,685

71.4%

Black and Black multiracial (excl. Black Hisp.) 30.5%

8,591

21.4%

Hispanic (any race)

8.2%

1,301

3.2%

Asian or other

4.7%

1,619

4.0%

Mobility Impairment

Absent

95.5%

37,973

94.5%

Present

4.5%

2,205

5.5%

Missing

18

0.0%

Annual Household Income

<$15,000

9.1%

2,919

7.3%

$15,000 to $24,999

8.3%

2,968

7.4%

$25,000 to $34,999

10.5%

3,614

9.0%

$35,000 to $49,999

13.2%

5,183

12.9%

$50,000 to $74,999

18.2%

7,514

18.7%

$75,000 to $99,999

13.1%

5,717

14.2%

$100,000+

27.7%

11,158

27.8%

Missing

1,123

2.8%

Education Level

High school or less

23.5%

8,370

20.8%

Some college or associate degree

31.9%

11,979

29.8%

Bachelor's degree

24.1%

10,192

25.4%

Graduate or professional degree

20.6%

9,631

24.0%

Missing

24

0.1%

Table continues on next page.

508

Continued from previous page: Sample of weekday trips by all adults ages 5+.

Weighted Unweighted

Percent

Sample Size, Percent

(Nonmissing

N

(All

Observations)

Observations)

All vehicle trips by adults ages 18+

40,196

-

College-educated

No 4-year degree

55.4%

20,349

50.6%

Bachelor's or higher Immigrant Nonimmigrant (born in US)

44.6% 88.6%

19,823 37,275

49.3% 92.7%

Immigrant (born outside of US) Missing Immigrant by Education Level US HS or less US some college/assoc. US bachelor's+ Imm. HS or less Imm. some college/assoc. Imm. bachelor's+ N/A (age <14) or missing Caregiver Status Noncaregiver

11.4%
21.2% 29.0% 38.5%
2.3% 2.9% 6.1%
62.5%

2,912 9
7,892 11,301 18,074
478 678 1,749
24
30,005

7.2% 0.0%
19.6% 28.1% 45.0%
1.2% 1.7% 4.4% 0.1%
74.6%

Caregiver, youngest child ages 015

37.5%

10,191

25.4%

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult

age 22+ in a household with a child of 515 years old.

Caregiver Status by Gender Male noncaregiver Female noncaregiver Male caregiver Female caregiver Missing Caregiver Status by Age of Youngest Child Noncaregiver Youngest ages 04 Youngest ages 515 Table continues on next page.

32.5% 17.3% 30.0% 20.2%
62.5% 18.0% 19.5%

14,767 4,381
15,238 5,810

36.7% 10.9% 37.9% 14.5%

30,005 4,583 5,608

74.6% 11.4% 14.0%

509

Continued from previous page: Sample of weekday trips by all adults ages 5+.

Weighted Unweighted

Percent Sample Size, Percent

(Nonmissing

N

(All

Observations)

Observations)

All vehicle trips by adults ages 18+

40,196

-

Vehicle Deficit Category of Household

Zero-vehicle

0.4%

112

0.3%

Deficit (hard or soft)

20.1%

4,817

12.0%

Nondeficit (sufficient/surplus)

79.5%

35,267

87.7%

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than household members ages 16+

(i.e., potential drivers). See chapter 1, Vehicle Ownership for more details.

Vehicle Deficit Category by Household Size

Nondeficit, single potential driver

18.9%

9,383

23.3%

Nondeficit, 2+ potential drivers

60.5%

25,884

64.4%

Deficit

20.1%

4,817

12.0%

Zero-vehicle

0.4%

112

0.3%

Transit Use, Past 30 Days

No

91.7%

37,940

94.4%

Yes

8.3%

2,232

5.6%

Missing

24

0.1%

Walking, Past 30 Days

No

25.5%

9,894

24.6%

Yes

74.5%

30,183

75.1%

Missing

119

0.3%

Biking, Past 30 Days No

94.3%

37,744

93.9%

Yes

5.7%

2,446

6.1%

Missing

6

0.0%

510

Table 226. Work journey/commute sample table: Location, distance, and mode.

Weighted

Unweighted

Percent (Nonmissing

Sample Size, N

Percent (All

All work journeys by adults ages 18+

Observations)

Observations) 10,490

MPO Tier (Residence of Traveler)

1. Atlanta MPO counties

56.9%

3,334

31.8%

2. Medium MPO counties

15.2%

3,848

36.7%

3. Small MPO counties

9.9%

2,330

22.2%

4. Non-MPO counties

18.0%

978

9.3%

MPO (Residence of Traveler)

Albany

1.2%

274

2.6%

Athens

2.1%

605

5.8%

Atlanta

56.9%

3,334

31.8%

Augusta

3.9%

990

9.4%

Brunswick

1.1%

242

2.3%

Cartersville

0.8%

195

1.9%

Chattanooga

0.9%

88

0.8%

Columbus

2.4%

620

5.9%

Dalton

1.1%

243

2.3%

Gainesville

1.7%

523

5.0%

Hinesville

1.0%

186

1.8%

Macon

1.9%

429

4.1%

Rome

0.5%

137

1.3%

Savannah

4.1%

1,022

9.7%

Valdosta

1.0%

244

2.3%

Warner Robins

1.2%

380

3.6%

Non-MPO

18.0%

978

9.3%

WJ Type

Simple (no stops)

75.8%

7,872

75.0%

Complex (one or more stops)

24.2%

2,618

25.0%

Supercommute (WJ > 100 mi)

Not a supercommute (WJ PMT <100 mi)

99.2%

10,378

98.9%

Supercommute (WJ >100 mi)

0.8%

85

0.8%

Missing

27

0.3%

Mode

POV

92.8%

9,994

95.3%

Nonmotorized (walk, bike wheelchair)

2.3%

171

1.6%

Public transit or other bus/train

2.6%

149

1.4%

Other ground or water

0.9%

50

0.5%

Air or air multimodal

0.1%

6

0.1%

Multimodal with POV

0.8%

98

0.9%

Multimodal, no POV

0.5%

20

0.2%

Missing

2

0.0%

Note: Work journeys and commutes are the same in terms of sample size; the two differ in terms of distance and duration.

See chapter 2 for more details.

511

Table 227. Work journey/commute sample table: Stops, driving, and time of day.

Weighted

Unweighted

All work journeys by adults ages 18+

Percent (Nonmissing Observations)

Sample Size, N
10,490

Percent (All
Observations)

WJ Type

Simple (no stops)

75.8%

7,872

75.0%

Complex (one or more stops)

24.2%

2,618

25.0%

Number and Duration of WJ Stops (Complex WJ Only)

0.0%

Direct (no stops)

75.8%

7,872

75.0%

Single short stop (<30 min)

13.3%

1,345

12.8%

Single long stop (30+ min)

5.0%

616

5.9%

Multiple short

2.5%

265

2.5%

Short + long or multiple long

3.5%

392

3.7%

Purpose of Stop(s) (Complex WJ Only)

0.0%

Shopping

10.7%

1,162

11.1%

Transport others

6.9%

673

6.4%

Dining

5.0%

566

5.4%

Social, recreational or fitness

2.6%

324

3.1%

Other work

0.7%

110

1.0%

Other nonwork

3.3%

329

3.1%

Note: Categories are not mutually exclusive. WJ may contain stops for more than one purpose.

Drive Alone for Entire POV

No

31.5%

2,723

26.0%

Yes

68.5%

7,767

74.0%

Driver Status for WJ

Drive alone all POV legs

69.1%

7,833

74.7%

Family sharing: drive with household passenger for 1+ legs

6.7%

648

6.2%

Carpool driver: drive with nonhousehold passenger for 1+ legs

9.1%

963

9.2%

Both carpool and family sharing

1.7%

157

1.5%

Drive alone + ride as pax

0.2%

33

0.3%

All POV legs as passenger

6.7%

458

4.4%

No POV legs

6.4%

398

3.8%

Peak Category

AM peak (6am9:59am)

35.2%

3,873

36.9%

Midday (10am2:59 pm)

15.8%

1,589

15.1%

PM peak (3pm6:59 pm)

35.1%

3,846

36.7%

Overnight (7pm6:59am)

13.9%

1,182

11.3%

Note: Work journeys and commutes are the same in terms of sample size; the two differ in terms of distance and duration. See

chapter 2 for more details.

512

Table 228. Work journey/commute sample table: Traveler characteristics.

Weighted

Unweighted

All work journeys by adults ages 18+

Percent (Nonmissing Observations)

Sample Size, Percent

N

(All

Observations)

10,490

Worker Status of Commuter

Not an NHTS-defined worker

1.2%

150

1.4%

NHTS-defined worker

98.8%

10,340

98.6%

NHTS defines a worker as someone who worked for pay or profit, or was temporarily absent from paid employment, in the

week before completing the travel survey ("last week"). A small number of people who were not NHTS-designated workers

nevertheless reported work travel on their travel day, perhaps reflecting irregular employment situations.

Occupational Category

Sales or service

27.2%

2,365

22.5%

Clerical or administrative support

9.0%

1,045

10.0%

Blue collar*

19.8%

1,603

15.3%

Professional, managerial, or technical

43.9%

5,260

50.1%

Other

0.2%

11

0.1%

Missing

206

2.0%

* Blue collar refers to manufacturing, construction, maintenance, or farming.

Worker Type

Full-time

16.4%

1,608

15.3%

Part-time

83.6%

8,830

84.2%

Missing

52

0.5%

Sex

Male

56.3%

5,437

51.8%

Female

43.7%

5,053

48.2%

Age Cohort (Adults Only)

Millennial and Gen Z (1836)

40.5%

3,145

30.0%

Gen X (3752)

35.3%

3,543

33.8%

Pre-retirement age Boomer (5364)

19.9%

2,992

28.5%

Retirement age (65+)

4.3%

810

7.7%

Driver Status

Nondriver

5.6%

291

2.8%

Driver

94.4%

10,199

97.2%

Race (categories)

White non-Hispanic only

53.4%

7,199

68.6%

Black and black multiracial (incl. black Hispanic)

32.3%

2,394

22.8%

Other

14.3%

897

8.6%

See chapter 1, Assorted Definitions and Notes for more details on how race is categorized in this report.

Table continues on next page.

513

Continued from previous page: Sample of work journeys by adults ages 18+.

Weighted

Unweighted

Percent

Sample Size, Percent

(Nonmissing

N

(All

Observations)

Observations)

All work journeys by adults ages 18+

10,490

Mobility impairment

Absent

98.7%

10,336

98.5%

Present

1.3%

150

1.4%

Missing

4

0.0%

A mobility impairment is defined as a "condition or handicap that makes it difficult to travel outside of the home."

Annual Household Income

<$35,000

28.0%

2,148

20.5%

$35,000 to $49,999

13.2%

1,345

12.8%

$50,000 to $74,999

18.0%

1,965

18.7%

$75,000 to $99,999

14.4%

1,657

15.8%

$100,000+

26.3%

3,161

30.1%

Missing

214

2.0%

Education Level

High school or less

27.6%

2,226

21.2%

Some college or associate degree

31.4%

3,152

30.0%

Bachelor's degree

23.3%

2,641

25.2%

Graduate or professional degree

17.7%

2,460

23.5%

Missing

11

0.1%

College-educated

No 4-year degree

59.0%

5,378

51.3%

Bachelor's or higher

41.0%

5,101

48.6%

Missing

11

0.1%

Caregiver Status

Noncaregiver

63.8%

7,520

71.7%

Caregiver, youngest child ages 015

36.2%

2,970

28.3%

A caregiver is defined as any adult age 18+ in a household with a child of less than 5 years old, and any adult age 22+ in a

household with a child of 515 years old.

Caregiver Status by Gender

Male noncaregiver

36.3%

3,850

36.7%

Female noncaregiver

20.0%

1,587

15.1%

Male caregiver

27.5%

3,670

35.0%

Female caregiver

16.2%

1,383

13.2%

Caregiver Status by Age of Youngest Child

Noncaregiver

63.8%

7,520

71.7%

Youngest ages 04

16.9%

1,308

12.5%

Youngest ages 515

19.3%

1,662

15.8%

Vehicle Deficit Category by Household Size

Nondeficit, single potential driver

14.7%

1,905

18.2%

Nondeficit, 2+ potential drivers

60.5%

7,098

67.7%

Deficit

22.3%

1,314

12.5%

Zero-vehicle

2.6%

173

1.6%

Note: Work journeys and commutes are the same in terms of sample size; the two differ in terms of distance and duration.

See chapter 2 for more details.

A vehicle-deficit household owns at least one vehicle, but fewer vehicles than household members ages 16+ (i.e., potential

drivers). See chapter 1, Vehicle Ownership for more details.

514

ACKNOWLEDGMENTS The authors thank Stacey Bricka, Ali Etezady, Anthony Fucci, Laurie Garrow, Habte Kassa, Sung Hoo Kim, Yongsung Lee, Nancy McGuckin, Atiyya Shaw, and Xinyi Wang.
515

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