The integration of the regional MPO models into the Georgia statewide travel demand model: phase 1

GEORGIA DOT RESEARCH PROJECT 16-12 FINAL REPORT
THE INTEGRATION OF THE REGIONAL MPO MODELS INTO THE GEORGIA STATEWIDE
TRAVEL DEMAND MODEL PHASE 1
OFFICE OF PERFORMANCE-BASED MANAGEMENT AND RESEARCH
15 KENNEDY DRIVE FOREST PARK, GA 30297-2534

1. Report No.: FHWA-GA-18-1612

2. Government Accession No.:

3. Recipient's Catalog No.:

4. Title and Subtitle: The Integration of the Regional MPO Models into the Georgia Statewide Travel Demand Model Phase 1

5. Report Date: November 2018 (Final)
6. Performing Organization Code:

7. Author(s): Giovanni Circella, Ph.D.; Timothy Welch, Ph.D.; Ali Etezady; Alyas Widita 9. Performing Organization Name and Address: School of Civil and Environmental Engineering Georgia Institute of Technology 790 Atlantic Dr NW Atlanta, GA 30322
12. Sponsoring Agency Name and Address: Georgia Department of Transportation Office of Research 15 Kennedy Drive Forest Park, GA 30297-2534

8. Performing Organ. Report No.: 16-12
10. Work Unit No.:
11. PI number: 0015125 13. Type of Report and Period Covered: Final; May 2016 - November 2018
14. Sponsoring Agency Code:

15. Supplementary Notes: Prepared in cooperation with the U.S. Department of Transportation, Federal Highway Administration. 16. Abstract: The integration between regional and statewide travel demand models (TDMs) can often prove difficult, or even impossible, due to discrepancies in modeling assumptions, inputs, and outputs. Discrepancies in the zonal systems, socioeconomic inputs, and transportation networks limit the ability to provide external travel estimates that can be used in regional models. Similarly, state agencies cannot easily reconcile travel demand forecasts from regional agencies with the outputs of statewide models.
This research project was developed for the Georgia Department of Transportation (GDOT) and focuses on the integration of the Georgia Statewide Travel Demand Model (GSTDM) with the 14 regional TDMs used by metropolitan planning organizations (MPOs) within their respective regions and which are directly developed/managed by GDOT. To do this, the researchers propose a methodology that updates the GSTDM zonal system, socioeconomic inputs, and transportation network, and makes them consistent with the corresponding features in the MPO models. They also introduce a unified attribute table for the MPO and GSTDM networks, and a new attribute that identifies statewide-relevant links in the MPO networks, thus streamlining the process for future model updates.
This project helps GDOT streamline the travel demand modeling practice, and allows the data transfer and comparison between the statewide and MPO models to take place seamlessly. The project addresses a priority for GDOT and provides guidance to other DOTs with a solution that could be transferred to and replicated in other states and agencies. It provides an efficient way to integrate MPO models into the statewide model, compare inputs and outputs across models, and simplify future model maintenance.

17. Key Words: Travel Demand, Statewide Model, Metropolitan Planning Organization, Road Network, TAZ, GSTDM

18. Distribution Statement:

19. Security Classification 20. Security

21. Number of Pages:

(of this report):

Classification (of this

page):

131

Unclassified

Unclassified

Form DOT 1700.7 (8-69) GDOT Research Project No. 16-12

22. Price:

Final Report
THE INTEGRATION OF THE REGIONAL MPO MODELS INTO THE GEORGIA STATEWIDE TRAVEL DEMAND MODEL PHASE 1
By Giovanni Circella, Ph.D. Timothy Welch, Ph.D. Ali Etezady, Ph.D. student Alyas Widita, Ph.D. student
Georgia Institute of Technology Contract with
Georgia Department of Transportation In cooperation with
U.S. Department of Transportation Federal Highway Administration
November 26, 2018
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.

Table of Contents
Page
List of Tables ................................................................................................................... v List of Figures ................................................................................................................ vii Executive Summary........................................................................................................ ix Acknowledgements........................................................................................................ xii Chapter 1 Introduction .................................................................................................. 1
Purpose of the Research .............................................................................................. 2 Overview of the GSTDM ............................................................................................ 3 Overview of the MPO Models in Georgia .................................................................. 4 MPO Model Updates ................................................................................................... 7
Chapter 2 Integration of MPO TAZs in the GSTDM Modeling Framework ............... 9 Methodology................................................................................................................ 9
Stage I. Automation Process .............................................................................. 11 Stage II. Manual Checking/Revising ................................................................. 15 Proposed GSTDM TAZ System and CTPP Geographies ................................. 18 GSTDM TAZ Numbering System..................................................................... 21
Output Table and Documentation ............................................................................. 24
Chapter 3 Socioeconomic Data................................................................................... 27 Summary of Socioeconomic Variables by MPO....................................................... 28 Comparison of Socioeconomic Data between GSTDM and MPO Models .............. 30
Albany................................................................................................................ 32 Athens ................................................................................................................ 33 Augusta .............................................................................................................. 34 Brunswick .......................................................................................................... 35 Cartersville......................................................................................................... 36 Columbus ........................................................................................................... 37 Dalton................................................................................................................. 38 Gainesville ......................................................................................................... 39 Hinesville ........................................................................................................... 40 Macon ................................................................................................................ 41 Rome .................................................................................................................. 42 Savannah ............................................................................................................ 43 Valdosta ............................................................................................................. 44 Warner Robins ................................................................................................... 45
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Chapter 4 Integration of MPO Networks in the GSTDM Framework ....................... 47
Methodology.............................................................................................................. 47
Step 1: Deleting the Portions of the GSTDM Network inside the MPO Model Areas ................................................................................................. 51
Steps 2 and 3: Preparing the MPO Network and Carrying Out a Traffic Assignment .................................................................................................. 52
Steps 4 and 5: Identifying Relevant Portions of the MPO Network for Statewide Modeling Purposes and Renumbering Nodes ............................. 56
Step 6: Synchronizing the Attribute Tables of the GSTDM and MPO Model ........................................................................................................... 59
Steps 7 and 8: Joining the Selected Portions of the MPO Networks with the GSTDM Network................................................................................... 60
Step 9: Generating New Centroid Connectors................................................... 60
Output ........................................................................................................................ 61
Proposed Unified Attribute Table...................................................................... 62
Chapter 5 Conclusions and Future Maintenance of the GSDTM ............................... 67
Future Maintenance of the GSTDM.......................................................................... 68
Maintaining Consistency of TAZs and Socioeconomic Data............................ 69 Maintaining Consistency of Model Networks ................................................... 70
Chapter 6 References .................................................................................................. 75
Appendix A Additional Details on the Automation Process for TAZ Synchronization ......................................................................................................... 77
Appendix B Socioeconomic Data Comparison .......................................................... 83
Albany................................................................................................................ 83 Athens ................................................................................................................ 85 Augusta .............................................................................................................. 88 Brunswick .......................................................................................................... 91 Cartersville......................................................................................................... 93 Columbus ........................................................................................................... 94 Dalton................................................................................................................. 96 Gainesville ......................................................................................................... 98 Hinesville ......................................................................................................... 101 Macon .............................................................................................................. 103 Rome ................................................................................................................ 105 Savannah .......................................................................................................... 107 Valdosta ........................................................................................................... 111 Warner Robins ................................................................................................. 112
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Appendix C Projection Issues................................................................................... 115 iv

List of Tables

Table

Page

1. List of 16 MPO Models in Georgia and the Time of Their Most Recent Model Update (as of September 2018) ................................................................................... 8
2. Example of the MPO TAZs Matched with a GSTDM TAZ in the Albany MPO Region ....................................................................................................................... 12
3. MPO Codes and Corresponding MPO Model Area .................................................. 22
4. TAZ Numbering System Used in GSTDM ............................................................... 24
5. Updated Attribute Table of the Proposed GSTDM TAZ System ............................. 25
6. Total Population and Number of Households in Each MPO Area and Model Region ....................................................................................................................... 29
7. Number of MPO TAZs within Each MPO Area and Model Region ........................ 30
8. List of MPOs with Issues in Using GSTDM Projection System .............................. 55
9. MPO Node Renumbering System ............................................................................. 59
10. Proposed Attribute Table for Revised GSTDM and MPO Model Networks............ 63
11. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Albany MPO Model Area (# TAZs = 38) ......................... 83
12. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Athens MPO Model Area (# TAZs = 105)........................ 85
13. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Augusta MPO Model Area (# TAZs = 101) ...................... 88
14. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Brunswick MPO Model Area (# TAZs = 61) .................... 91
15. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Cartersville MPO Model Area (# TAZs = 29) .................. 93
16. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Columbus MPO Model Area (# TAZs = 89)..................... 94
17. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Dalton MPO Model Area (# TAZs = 65) .......................... 96
18. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Gainesville MPO Model Area (# TAZs = 113) ................. 98

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19. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Hinesville MPO Model Area (# TAZs = 44)................... 101
20. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Macon MPO Model Area (# TAZs = 77) ........................ 103
21. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Rome MPO Model Area (# TAZs = 52).......................... 105
22. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Savannah MPO Model Area (# TAZs = 147).................. 107
23. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Valdosta MPO Model Area (# TAZs = 51) ..................... 111
24. Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Warner Robins MPO Model Area (# TAZs = 55) ........... 112
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List of Figures

Figure

Page

1. Identification of MPO Model Areas with Respect to County Boundaries and GDOT Districts ........................................................................................................... 6
2. Conceptual Diagram of the Methodology to Integrate MPO and GSTDM Zonal Systems ........................................................................................................... 10
3. Example of MPO TAZs (Blue Line) and GSTDM TAZs (Red Line) within the Brunswick MPO Region: (a) Before and (b) After Steps in Stage I ......................... 14
4. Changing the Boundaries of TAZs in Brunswick Based on Population and Land Use.................................................................................................................... 17
5. Checking the Conformity of the GSTDM TAZ System in the MPO Region of Athens with the Census Blocks ................................................................................. 19
6. Checking the Conformity of the GSTDM TAZ System in the MPO Region of Athens with the CTPP TAZ System.......................................................................... 21
7. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Albany.......................... 32
8. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Athens .......................... 33
9. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Augusta ........................ 34
10. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Brunswick .................... 35
11. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Cartersville................... 36
12. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Columbus ..................... 37
13. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Dalton .......................... 38
14. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Gainesville ................... 39
15. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Hinesville ..................... 40
16. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Macon .......................... 41
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17. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ-Level, in Rome ........................... 42
18. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Savannah ...................... 43
19. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Valdosta ....................... 44
20. Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level, in Warner Robins ............. 45
21. Conceptual Diagram of the Methodologies Employed to Integrate the Statewide and Regional MPO Networks................................................................... 50
22. Overview of Areas of the Original Statewide Network that were Removed within the MPO Model Areas ................................................................................... 52
23. Athens MPO Network Overlaid on the Statewide TAZs within Athens MPO Model Area ................................................................................................................ 54
24. Portions of the Athens MPO Network (Red Line) to be Deleted in the GSTDM Network ..................................................................................................................... 58
25. Future Maintenance Process for GSTDM Transportation Network ......................... 71 26. Example of MPO TAZs (Blue Line) and GSTDM TAZs (Red Line) within the
Valdosta MPO Model Region ................................................................................... 78 27. Example of Existing MPO TAZs (Left) and GSTDM TAZs (Right) ....................... 80 28. Comparison between the Existing (Left) and Revised GSTDM TAZs (Right) ........ 81 29. Projection Issue as Detected in the Case of Cartersville MPO Network ................ 116
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Executive Summary
This report summarizes the approach used to integrate the Georgia Statewide Travel Demand Model (GSTDM) and the metropolitan planning organization (MPO) travel demand models that are currently being developed and maintained by the Georgia Department of Transportation (GDOT). It provides guidelines and recommendations to enable a streamlined, scalable, and replicable process that can be applied in future travel demand model development and updates. In doing so, the project team, with continuous coordination with GDOT, devised practical solutions to connect the GSTDM and MPO models and integrate three critical components of the travel demand forecasting models used at both the statewide and regional levels: the traffic analysis zones (TAZs), the socioeconomic input data, and the road network.
In integrating the TAZ systems, as discussed in Chapter 2, the research team in coordination with GDOT maintained the total number of TAZs constant--the GSTDM model includes a total of 3,770 TAZs, of which 3,243 are in Georgia, following the latest updates introduced in the model in 2017. However, the boundaries of the GSTDM TAZs were entirely redrawn to perfectly match those of the MPO models. This process was largely carried out using an automatic process, after which manual quality checks were applied to ensure quality of the output and consistency with higher-level geographies (e.g., state boundaries and county lines). This process ensured perfect nesting of the MPO TAZs into the GSTDM TAZs.
After the GSTDM TAZ system was synchronized with the MPO model TAZs, the comparison and transfer of model input and output data at the TAZ level became possible. Chapter 3 presents a systematic comparison of socioeconomic data between the
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statewide and MPO models in each regional modeling area. Although in the majority of the MPO model areas socioeconomic data compare rather well, there are few MPOs whose socioeconomic input data noticeably differ from the GSTDM socioeconomic data. The researchers recommend that GDOT carefully revise the model input data in these MPO model areas, with the aim of eliminating the observed discrepancies.
The proposed approach to conflate the model networks is presented in Chapter 4. At the basis of the proposed approach is the awareness that the best way to ensure consistency between statewide and MPO transportation networks--and simplify future model maintenance--requires rebuilding the statewide model network using the regional MPO networks as input in each MPO model area. To identify what parts of the regional networks are relevant for statewide modeling purposes, the research team applied a traffic assignment procedure on each MPO network using the GSTDM TAZs in the area as origins/destinations of trips. The output of this step helped identify links that would not receive any traffic loads when using the coarser statewide zonal system. The output from the previous step was further pruned based on several other criteria that were identified in consultation with GDOT. In this process, a unified attribute table for all networks was developed and recommended to GDOT to further streamline future rounds of model updates.
Finally, Chapter 5 presents the conclusions and discusses how the approaches developed in this study can be used to streamline--and greatly simplify--future maintenance of the GSTDM while maintaining consistency of model inputs and outputs with the regional models. This desired outcome was identified as a central objective in the definition of the entire approach proposed in this study. Specifically, in the
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development of the methods to update the statewide model components to ensure integration with the MPO models used in Georgia, the research team took special care to ensure the replicability of these methods and that future model maintenance would be as easy and simple as possible.
As a result of the approaches developed in this study, the research team presents three important recommendations and takeaways from this research:
First, the research team recommends that GDOT adopt a protocol to use the same sources and standards to generate and maintain socioeconomic data, TAZs, and road networks, at both the statewide- and MPO-level.
Second, in future model updates, adequate consideration should be given to the benefits offered by increasing the total number of statewide TAZs. In particular, using the same MPO-level TAZs in the GSTDM model would further simplify model maintenance and improve accuracy of the statewide model results.
Third, all future versions of the statewide and MPO models should use the same socioeconomic and network attributes.
The research team recognizes that the adoption of these recommendations would necessarily require the engagement of multiple stakeholders within GDOT and the MPOs in Georgia. However, considering the collective interests at stake and the overall objectives of obtaining more precise and time- and cost-effective travel demand model forecasting, the researchers are confident that the implementation of these recommendations would be beneficial to all stakeholders involved.
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Acknowledgements
This project builds on the previous work developed by numerous colleagues at Atkins and HNTB Corporation, who initially developed and then further updated the previous versions of the statewide travel demand model used by the Georgia Department of Transportation. We offer our gratitude to Dr. Ram Pendyala, who helped the research team during the early stages of this project and provided guidance and additional feedback throughout the course of the project. In addition, we appreciate the valuable feedback and collaboration provided by the modeling team at HNTB Corporation, who ensured coordination of this project with the other modeling activities and the maintenance process of the GSTDM and MPO models. Finally, our team extends special thanks to Mr. Habte Kassa from the GDOT Office of Planning, who proactively followed the activities of this project, provided valuable feedback and guidance throughout the project, and helped us access datasets and information needed to carry out our work. Not only did he serve as the technical/implementation manager for this project, he also acted as an active member of our research team and considerably contributed to the successful completion of the project.
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Chapter 1 Introduction
The Georgia Department of Transportation (GDOT) has developed a statewide travel demand model to assist with the formulation of statewide transportation plans. The Georgia Statewide Travel Demand Model (GSTDM) incorporates both freight and passenger travel demand forecasting components, and serves a variety of purposes including, but not limited to, the estimation of intercity passenger and truck travel volumes, interstate and state highway corridor volumes, changes in travel flows on major corridors due to changes in land use or economic policies, etc.
Despite the number of improvements in several GSTDM components, the statewide travel demand model, at this time, does not align well with metropolitan urban/regional models in the state. There are 16 regional travel demand models that are currently operated at the metropolitan planning organization (MPO) level in Georgia. While some of these models are independently developed by local MPOs and include a large number of details and fairly sophisticated modeling approaches--most notably, the Atlanta Regional Commission, or ARC, model (WSP, 2017)--many travel demand models for smaller MPOs have a more simplified scope, and are developed and maintained by GDOT and its consultants. MPO models are continuously updated to fulfill the Long Range Transportation Plan (LRTP) and conformity requirements, with the specific schedule for the MPO model updates varying by region.
The regional MPO models, in their current versions, are based on the use of zonal systems, socioeconomic inputs, and transportation networks that are independently created and maintained from those in the GSTDM framework (e.g., HNTB, 2015e). Among other limitations, this leads to a number of inconsistencies between the statewide
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travel demand model and the MPO models used at the regional level, which, to date, do not allow the models to interact with each other. Inconsistencies between the GSTDM and the regional MPO models include the zonal representation, socioeconomic input data, and highway and transit networks. Such inconsistencies not only result in limitations in using the statewide model to provide external travel estimates to the regional models, but also hinder the accurate assessment of the impacts of statewide projects on travel demand and congestion patterns within the metro area boundaries.
Consequently, the need for a better integration of the GSTDM and the MPO models has been included in the top priorities identified in a peer-review report of the statewide model development (FHWA, 2012). Accordingly, the main purpose of this project is to answer the critical need of making the GSTDM consistent with the regional MPO models within their respective boundaries.
Purpose of the Research The objective of this research study is to develop a model integration framework to incorporate the GSTDM with the regional models developed for travel demand forecasting purposes at the MPO (regional) level in Georgia. The regional models are used as analysis tool to develop the federally mandated LRTPs that MPOs are required to produce every 45 years. The integration framework will allow two-way comparisons of zonal structures, socioeconomic datasets, and transportation networks between the GSTDM and the MPO models. It will also provide a way to improve the performance of the statewide model through matching travel flows and relationships at the borders and external stations of the MPO models. As a result of this, GDOT will be better equipped to forecast future travel demand in the state of Georgia and make better-informed decisions
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in transportation planning. Thus, the project supports the Department's strategic goals of "improving the movement of people and goods across and within the state," while enhancing the "movement of people and products in a 21st century Georgia" (FHWA, 2012). It will allow for a streamlined process for future updates of the GSTDM model, which will ensure consistency with the MPO model components also in the future versions of these models.
Overview of the GSTDM The version of the GSTDM that was used as the input in this project covers the entire 48 continental U.S. states and includes 3,770 traffic analysis zones (TAZs), of which 3,243 are in the state of Georgia1. The TAZ sizes increase the farther the zones are from Georgia, since the modeling need for detailed zones outside the state diminishes the farther the zones are from the state. In developing the TAZ system, the major data sources included U.S Census Bureau data and TIGER files, the Bureau of Economic Analysis (BEA), and the Georgia Department of Labor (DOL) (Atkins, 2013a). The MPO area with the largest number of statewide TAZs is Atlanta with 930 zones, and the MPO area with the smallest number of statewide TAZs is Cartersville with 30 zones.
The highway network, including a total of 82,632 miles (of which 20,805 miles are in the state of Georgia), was developed largely based on the National Highway Planning Network (NHPN) database and cross-checked using GDOT's road
1 An earlier version of the GSTDM TAZ system included 3,505 TAZs, of which 2,978 were in the state of Georgia. That version of the model was calibrated to the 2010 base year.
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characteristics (RC) database. The developed network is based on a four-layered system, taking into account distance from Georgia, so that the details in the highway network would, similar to the TAZ system, gradually decrease the farther the roads are from the boundaries of the state. The roadway system has been categorized into urban and rural segments, with each segment further subcategorized into six functional classes to help determine the capacity and free-flow speed of links (Atkins, 2013a). More recently, however, this functional classification system has been updated to a seven-category system, essentially discarding the ruralurban segmentation of the roadway system.
The GSTDM model has been calibrated to the 2015 base year, and serves as an effective planning tool to help develop travel demand forecasts in the state until 2040. The technical staff from the GDOT Office of Planning continues to regularly update the GSTDM components in cooperation with a team of consultants contracted by GDOT, using updated information about transportation patterns, socioeconomic data, and observed traffic flows available from multiple sources. The additional sources used during the statewide model updates also include information received from other state and federal agencies and local MPOs in the state of Georgia.
Overview of the MPO Models in Georgia The scope of the project comprises 16 MPOs in Georgia. Figure 1 illustrates the delineation of the MPO model areas with respect to GDOT districts and counties in Georgia. As Figure 1 shows, the model areas of three MPOs--specifically, Augusta: Augusta Regional Transportation Study (ARTS) (HNTB, 2015e); Columbus: Columbus -Phenix City MPO (C-PCMPO) (HNTB, 2015b); and Chattanooga: Chattanooga Hamilton County Regional Planning Agency (CHC-RPA)--cross into neighboring states.
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Tennessee DOT is the lead agency for coordination of the CHC-RPA plan update and travel demand modeling activities. The travel demand forecasting model used for the Atlanta region is solely managed by the Atlanta Regional Commission (ARC). That leaves GDOT in charge of updating and maintaining a total of 14 MPOs in the state, i.e. Albany (Cambridge Systematics & HNTB, 2015a); Athens (HNTB, 2014); Brunswick (HNTB, 2015a); Cartersville (HNTB, 2016a); Dalton (HNTB, 2015d); Gainesville (HNTB, 2015c); Hinesville (HNTB, 2015e); Macon (Cambridge Systematics & HNTB, 2015b); Rome (HNTB, 2016b); Savannah (Atkins, 2013b); Valdosta (Cambridge Systematics & HNTB, 2015c); and Warner Robins (HNTB, 2015f), in addition to August and Columbus that were already mentioned before. These 14 MPOs are therefore the main focus of this project.
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FIGURE 1 Identification of MPO Model Areas with Respect to County Boundaries and GDOT Districts (Source: Created by the Authors)
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MPO Model Updates Prior to conducting the main tasks of integrating the statewide and regional MPO models, the project team assessed the characteristics of each regional MPO model in the state. Understanding the characteristics of the MPO models under study is an important preliminary step for this project. This assessment included analyzing each MPO model in terms of socioeconomic data, TAZs, transportation networks, and modeling methods used to forecast travel demand. In addition, and to get a better sense of the state of the work on MPO modeling, the project team summarized when each MPO model was last updated. A summary of these model updates is presented in Table 1. As Table 1 reports, most of the regional MPO models were updated fairly recently (in 2015 or later). Only a few MPOs had their regional models updated several years back, in 2012 and 2013. The versions of the MPO models that were last updated in the years listed in Table 1 were used as input in this project.
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TABLE 1
List of 16 MPO Models in Georgia and the Time of Their Most Recent Model Update (as of September 2018)

MPO

Name

Abbreviation

1 Albany

DARTS

2 Athens

MACORTS

3 Atlanta

ARC

4 Augusta

ARTS

5 Brunswick

BATS

6 Cartersville CBMPO

7 Chattanooga CHC-RPA

8 Columbus

C-PCTS

9 Dalton

GDMPO

10 Gainesville GHMPO

11 Hinesville

HAMPO

12 Macon

MATS

13 Rome

RFCMPO

14 Savannah

CORE

15 Valdosta

VLMPO

16 Warner Robins WRATS

Year 2015 2014 2016 2015 2015 2016 2013 2015 2015 2015 2015 2012 2016 2013 2015 2015

Last Update Month February October April October
September February February January September January October October March December October October

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Chapter 2 Integration of MPO TAZs in the GSTDM Modeling Framework
As part of this research project, the research team at the Georgia Institute of Technology worked on the development of solutions and recommendations that would allow the integration of the MPO models' zonal systems, data, and transportation networks into the GSTDM. This chapter summarizes the methodology used for the integration of the MPO TAZs in the GSTDM modeling framework. Methodology The methodology used to develop the revised TAZ system for the GSTDM involves two primary stages:
I. Automation process II. Manual checks and revisions These two stages were employed to achieve one of the primary objectives of this project, which is creating a coherent and synchronized TAZ system between the MPO models and the GSTDM. Figure 2 conceptually summarizes the methodology the project team employed to achieve this goal.
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STAGE I: AUTOMATION PROCESS
STAGE II: MANUAL CHECKING/ REVISING
FIGURE 2 Conceptual Diagram of the Methodology to Integrate MPO and GSTDM Zonal
Systems (Source: Created by the Authors)
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Stage I. Automation Process The automation process, as the first stage, is a high-level procedure to develop a raw draft of the GSTDM TAZ system based on the existing zonal geographies of the MPO models. As expected, there is a significant number of instances where the GSTDM TAZ borders do not neatly match those of the existing MPO TAZs. The research team, therefore, geared this procedure toward producing a proposed GSTDM TAZ system that corresponds neatly with the existing MPO TAZ systems.
To this end, the research team came up with a sophisticated approach by creating a script that would handle the procedure without having to rely solely on cursory manual checking. To the researchers' knowledge, this method is novel and has not been used in any previous project. The process, as illustrated in Figure 2, can be described as follows:
Step 1. Identify the longitude (X) and latitude (Y) of the MPO TAZ spatial centroids, which will be used to obtain information from the corresponding GSTDM TAZs.
Step 2. Spatially join the GSTDM TAZ IDs to the MPO TAZ centroids that fall within their boundaries. This process is then followed by joining the new information of the MPO TAZs' centroids back to the MPO TAZ polygons so that each MPO TAZ would have its corresponding GSTDM TAZ ID. An example of the outcome of this process for part of the Albany MPO is provided in Error! Reference source not found..
Step 3. Dissolve the inner boundaries of the MPO TAZs with the same GSTDM TAZ ID attribute to form the new statewide TAZs. This is the end result of the automation process.
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The entire process described above was completed using a relatively short script developed in the open-source software RStudio. The script is included in Appendix A.

TABLE 2
Example of the MPO TAZs Matched with a GSTDM TAZ in the Albany MPO Region

MPO TAZ ID 233 234 235 236 237 239 240 241 242 243 244 245 246 247

GSTDM TAZ ID 1420 1420 1420 1420 1420 1420 1420 1420 1420 1420 1420 1420 1420 1420

County Dougherty Dougherty Dougherty Dougherty Dougherty Dougherty Dougherty Dougherty Dougherty Dougherty Dougherty Dougherty Dougherty Dougherty

Example of Automation Process This section describes an example of the application of the automation process to redraw GSTDM TAZs that are synchronized with MPO TAZs inside an MPO model area. The example focuses on a cluster of MPO TAZs in the Brunswick MPO region (Figure 3). As shown in Figure 3(a), there are a number of discrepancies between the old GSTDM TAZ

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system and the MPO TAZs; the GSTDM TAZ borders do not match neatly with the MPO TAZ borders in the region.
As discussed in the previous section regarding the step-by-step automation process, the creation of XY centroids of the MPO TAZs plays an important role in obtaining the information on the GSTDM TAZ IDs. The centroids that fall within each GSTDM TAZ will be assigned that GSTDM TAZ ID, which will later be attached back to the information of the MPO TAZ polygons. This piece of information is used to dissolve the MPO TAZs into the proposed GSTDM TAZ system.
Assigning back the MPO TAZ centroids with the information of the GSTDM TAZ IDs to the MPO TAZ polygons is an intermediate step before dissolving the MPO TAZ polygons into a raw draft output of the automation process. Figure 3(b) shows the result after the MPO TAZ polygons are dissolved based on the GSTDM TAZ IDs, and how the draft proposed GSTDM TAZ system now matches neatly with the MPO TAZ boundaries. Appendix A contains additional examples of the application of the automation process.
13

(a)

(b)

FIGURE 3

Example of MPO TAZs (Blue Line) and GSTDM TAZs (Red Line) within the Brunswick MPO Region: (a) Before and (b) After Steps in Stage I
(Source: Created by the Authors)

14

Stage II. Manual Checking/Revising After the application of the automation process, the research team employed an additional step to manually check and control the results:
Step 4. Manual checks are employed to correct any potential issues and adjust the final outcome to respect higher hierarchy boundaries (e.g., state and county boundaries).
The automation process is helpful as the main step to produce the draft version of the proposed GSTDM TAZs. Manual checks and revisions, however, are necessary to complement the automation process and produce the final revised GSTDM TAZ system that is consistent not only with the existing MPO TAZ geographies, but that also aligns well, to the degree possible, with administrative boundaries, geographical features, and the transportation network.
In accordance with the primary objective of developing a proposed GSTDM TAZ system that matches neatly with the MPO TAZ geographies, the research team updated the GSTDM TAZ boundaries mainly using the MPO TAZs as reference. However, the researchers also considered ensuring minimal discrepancies with the census geographies, e.g., census tracts, block groups, and blocks (and also the Census Transportation Planning Products,1 or CTPP, as described in the following section) wherever possible.
There are a few instances in which relying solely on the automation process did not lead to a final acceptable solution. For example, the researchers manually revised the draft GSTDM TAZ system (output of the automation process) in the Augusta MPO
1 https://ctpp.transportation.org/ctpp-data-set-information/
15

region to better match the GSTDM TAZ boundaries with the state-level boundaries. Moreover, the research team revised one proposed GSTDM TAZ in the Chattanooga MPO region by dividing it into two TAZs because the original one included two physically separated areas located on the opposite sides of a river (and not connected by any bridge) in a single TAZ.
As another example, Figure 4 shows an area in Brunswick, Georgia, where a residential neighborhood is mixed with the port area. In the manual checking stage, the researchers investigated the population and sociodemographic characteristics of the blocks in the area. This observation warranted a change in the boundaries so that the GSTDM TAZ contains more homogeneous land uses, which is somewhat essential for travel demand modeling purposes.
16

FIGURE 4 Changing the Boundaries of TAZs in Brunswick Based on Population and
Land Use Distributions (Source: Created by the Authors)
17

Proposed GSTDM TAZ System and CTPP Geographies As previously mentioned, although the main goal of this project was to resolve the discrepancies between the current GSTDM TAZ system and the MPO TAZ systems, the consistency between the proposed GSTDM TAZ system and the block-level census geographies as well as the CTPP TAZ system was also checked (as illustrated in Figure 5 and Figure 6, respectively). This process was accomplished through the spatial join tool in ArcGIS to identify any discrepancies, and the results of this process were manually checked to decide whether further modifications were needed and to ensure the quality of the output.
In checking the conformity of the census geographies with the proposed GSTDM TAZs, minor discrepancies were exhibited, most of which could be disregarded with minimal to no actual effect on the accuracy of the proposed GSTDM TAZ system. In all these cases, the research team prioritized ensuring the consistency of the GSTDM TAZ system with the MPO TAZs, even if this meant retaining very minor discrepancies with the census block geographies. We recommend that such discrepancies with the census geographies be resolved during the next round of updates of the MPO travel demand models, so that their updated TAZs will follow census boundaries neatly in future versions of these models, and this perfect nesting of the census geographies will be carried over to the GSTDM model through the synchronization of the GSTDM-MPO TAZ systems that has been introduced as part of this project.
18

FIGURE 5 Checking the Conformity of the GSTDM TAZ System in the MPO Region of Athens with the Census Blocks
(Source: Created by the Authors)
By incorporating this manual checking process based on the census geographies, the research team created a correspondence table linking the census block identification numbers to the corresponding GSTDM TAZ IDs. This measure ensures the two-way
19

synchronization between the GSTDM and MPO TAZs, and between the GSTDM TAZs and the census blocks, and provides easier comparison of inputs and outputs between the models. The GSTDM-MPO TAZ correspondence tables for all MPO model areas in the state were provided to GDOT as part of the deliverables for the project.
In terms of conformity with the CTPP TAZ system, the researchers often encountered several major discrepancies, specifically in non-urban areas. Figure 6 below shows an example of such discrepancies for the case of the Athens MPO model area. Since the CTPP TAZs were of secondary importance to the main objective of this project, the researchers did not try to resolve those issues, as implementing such a change would have resulted in discrepancies with the MPO TAZ system. However, consistency with the CTPP boundaries was searched, and obtained, in the GSTDM TAZs for all non-MPO areas in the state.
20

FIGURE 6 Checking the Conformity of the GSTDM TAZ System in the MPO Region of Athens with the CTPP TAZ System
(Source: Created by the Authors)
GSTDM TAZ Numbering System Following the application of the integrated automation and manual checking process described previously, new GSTDM TAZ numbers were assigned as the next step. Also,
21

as part of this step, the MPO model area in which a TAZ is located (if any) is clearly identified, and the location information appended to the TAZ boundaries. In doing this, MPO model regions were listed alphabetically (as shown in Table 3), and the corresponding MPO code was assigned to each TAZ located in its model area.

MPO Code 1 2 3 4 5 6 7 8

TABLE 3 MPO Codes and Corresponding MPO Model Area

MPO Albany Athens Atlanta Augusta Brunswick Cartersville Chattanooga Columbus

MPO Code 9 10 11 12 13 14 15 16

MPO Dalton Gainesville Hinesville Macon Rome Savannah Valdosta Warner Robins

A Python script in ArcGIS was employed to assign TAZ numbers to the GSTDM TAZs in each MPO region, updating the attribute field `GSTDM_TAZ' in the attribute table of the respective shapefile. The researchers first used the script for the MPO region of Atlanta, and then assigned subsequent TAZ numbers to the following MPO model regions.
Specifically, the Python script employed by the research team assigned numbers to the GSTDM TAZs starting from `1' in the Atlanta model area. The script then generated sequential numbers for all TAZs in the Atlanta model region from `1' to `930', indicating that there are 930 TAZs in the region. We then used the same script for the

22

remaining MPO regions, changing the pStart value according to the last TAZ number of the previous MPO region. During this process, in agreement with the GDOT Office of Planning, the research team followed a sequence that is different from the alphabetical order used for the MPO codes reported in Table 3.1 Thus, the GSTDM TAZs included in the Atlanta MPO model region are numbered from `1' to `930', and the GSTDM TAZs in the Gainesville model area, which is the second MPO based on the ordering as specified in Table 4, start with `931'.
The process yielded new GSTDM TAZ numbers for all MPO model regions, as illustrated in Table 4. For the TAZs located in the rural areas that do not fit in any MPO model region, researchers assigned TAZ numbers starting from `2061', since the last GSTDM TAZ located in an MPO model region has a number of `2060'. There is a total of 3,770 TAZs in the GSTDM after the work done by Georgia Tech and the consultant teams.
1 The MPO codes were revised to follow an alphabetical order later during this project, and to make these codes consistent across TAZ and road networks.
23

MPO Atlanta Gainesville Cartersville Rome Athens Dalton Augusta Macon Columbus Warner Robins Albany Hinesville Savannah Brunswick Valdosta Chattanooga

TABLE 4 TAZ Numbering System Used in GSTDM

From 1
931 1053 1083 1136 1244 1317 1423 1507 1599 1659 1708 1752 1902 1964 2015

To 930 1052 1082 1135 1243 1316 1422 1506 1598 1658 1707 1751 1901 1963 2014 2060

# of TAZs 930 122 30 53 108 73 106 84 92 60 49 44 150 62 51 46

MPO Code 3 10 6 13 2 9 4 12 8 16 1 11 14 5 15 16

Output Table and Documentation Following the automation, manual checking, and revision processes, and then the TAZ numbering procedure, the output of the TAZ updating process included a shapefile with the proposed GSTDM TAZ geographies, and an attribute table containing the fields summarized in Table 5. Per recommendations received from GDOT, the research team included the following additional attributes to identify for each TAZ:

24

Georgia Department of Transportation District Code and District Name. Each TAZ is assigned a code and district name indicating its location within a specific district in Georgia.
MPO Code and Region. Each TAZ is assigned an MPO code from 0 to 16, following the order of MPOs reported in Table 3, where the value of `0' indicates that the TAZ is not located within any specific MPO model region.
State. Each MPO is assigned a state code to identify its location; the model also includes TAZs located in neighboring states.
Table 5 summarizes the attribute fields included in the updated GSTDM TAZ system.

TABLE 5 Updated Attribute Table of the Proposed GSTDM TAZ System

Variable

Description

GDOT_DISTR

Georgia Department of Transportation district code

DISTRICT_N District name

Value
1 2 3 4 5 6 7 District One- Gainesville District Two- Tennille District Three- Thomaston District Four- Tifton District Five- Jesup District Six- Cartersville District Seven- Chamblee

25

Variable

Description

MPO Code MPO code

GSTDM_TAZ Georgia statewide TAZ number

STATE

State where the TAZ is located

Region

MPO region

Value
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 13770 GA AL FL NC SC TN N/A Atlanta Gainesville Cartersville Rome Athens Dalton Augusta Macon Columbus Warner Robins Albany Hinesville Savannah Brunswick Valdosta Chattanooga

26

Chapter 3 Socioeconomic Data
Socioeconomic data are an important input and the primary driver of the travel demand model. The socioeconomic data of the 14 MPOs, which were extracted from each MPO model documentation and/or model files, provide information at the TAZ level on total population, total number of households, number of jobs (total and by category, e.g., retail, industrial, service), income, and school and university enrollment, where applicable. A comparison of these data between the GSTDM and MPO models was not possible before this project but can now be performed after redrawing the GSTDM TAZs to match the MPO TAZ boundaries so that MPO TAZs perfectly nest into GSTDM TAZs. This comparison can assist GDOT in better assessing the accuracy and consistency of the data at the statewide and regional levels and, if needed, take action to resolve observed differences/discrepancies.
In the remainder of this chapter, as well as in the following chapter that focuses on the integration of the model networks, this report primarily focuses on the 14 MPOs that are directly maintained by GDOT. In-depth analyses for Atlanta and Chattanooga were not carried out. The project team and GDOT agreed to exclude these two MPOs due to the following reasons:
1. Atlanta has its own model characteristics that are remarkably different from those of the other MPOs in Georgia. That is, while the 14 MPO models developed and maintained by GDOT use a traditional four-step transportation modeling approach, the model that is developed and maintained by the Atlanta Regional Commission is built on activity-based travel demand modeling. Moreover, the
27

model is not directly maintained and managed by GDOT, thus the integration with the GSTDM is a lower priority for GDOT. 2. Even though a portion of the Chattanooga MPO model region is located in Georgia, this MPO is based in Tennessee and directly reports to the Tennessee Department of Transportation. Summary of Socioeconomic Variables by MPO Table 6 provides a summary of the total population and number of households within each MPO area and MPO model region. The research team specifically focused on the total population and number of households since these two variables are consistently present in both GSTDM and MPO socioeconomic datasets. Other socioeconomic variables, particularly employment, did not show such consistency. Most MPOs have model regions that expand beyond their administrative MPO area boundaries. Only for a few MPOs--Brunswick, Cartersville, Macon, Rome, Savannah, and Valdosta--the model region coincides with the MPO administrative boundaries. For these MPOs, the total population and number of households do not differ between MPO administrative boundaries and model areas.
28

TABLE 6 Total Population and Number of Households in Each MPO Area and Model Region

Model Albany

MPO DARTS

Population

Total Model Area

Total MPO

121,176 118,835

Households

Total Model Area

Total MPO

48,861

48,045

Athens

MACO

191,929 153,220 75,182

60,784

Augusta

ARMPO

510,946 444,098 200,151 175,572

Brunswick

BATSMPO

79,494

37,886

Cartersville CBMPO

99,167

35,781

Columbus

C-PCMPO

308,863 255,731 122,677 102,727

Dalton

GDMPO

142,215 118,044 49,221

40,561

Gainesville GHMPO

238,355 191,861 92,602

74,079

Hinesville

HAMPO

75,998

66,634

27,116

23,973

Macon

MATS

179,994

70,881

Rome

RFCMPO

94,854

39,974

Savannah

CORE

342,653

139,801

Valdosta

VLMPO

110,781

45,040

Warner Robins WRATS-MPO 167,626 139,824 63,055

53,028

* Gray cells indicate that the total population and number of households in a given MPO are the same for the MPO area and MPO model region, because these MPO models operate inside the same MPO administrative boundaries.

Table 7 summarizes the number of MPO TAZs within the MPO administrative region,

outside of the MPO administrative boundaries, and the total number of TAZs in the

model area for each MPO model. In most cases, MPOs tend to use model areas that are

bigger than the MPO administrative region, and therefore include a number of TAZs

outside of the MPO administrative boundaries. Table 7 also reports the number of

external stations that connect the transportation network of each MPO model to outside

areas.

29

TABLE 7

Number of MPO TAZs within Each MPO Area and Model Region

MPO Name
Albany

MPO Agency
DARTS

TAZs within MPO Area
438

Additional TAZs in MPO Model Region
20

Total TAZs in
MPO Model
458

External Stations
31

Athens

MACO

312

136

448

38

Augusta

ARMPO

842

265

1,107

35

Brunswick

BATSMPO

397

397

10

Cartersville CBMPO

215

215

19

Columbus

C-PCMPO

500

198

698

46

Dalton

GDMPO

262

31

293

23

Gainesville GHMPO

397

112

509

48

Hinesville

HAMPO

193

28

221

12

Macon

MATS

483

483

34

Rome

RFCMPO

190

190

26

Savannah

CORE

796

796

20

Valdosta

VLMPO

419

419

26

Warner Robins WRATS-MPO

330

93

423

21

* The gray cells indicate that there are no additional TAZs as the MPO model boundaries coincide with the MPO administrative region.

Comparison of Socioeconomic Data between GSTDM and MPO Models This section focuses on the comparison of TAZ-level socioeconomic data between the statewide and the MPO models. These comparisons allow GDOT to identify MPOs where significant discrepancies in the distribution of these data exist, and take appropriate measures to resolve such issues. The following graphs systematically compare total population and household counts for the GTSDM and MPO models aggregated at the GSTDM TAZ level (using the correspondence tables between GSTDM

30

and MPO TAZs to aggregate MPO model data). In the comparisons reported in the remainder of this chapter (and in Appendix B), the research team used data from the 2010 GSTDM TAZ system (not the newer TAZs from the 2015 model update) to make the data more comparable to the corresponding socioeconomic data in the MPO models. While 2010 socioeconomic data were available for all MPOs, only a subset of the MPO models had already been updated to the 2015 base year at the time of this project. The full details of these comparisons are provided in the set of tables included in Appendix B.
As a summary, the socioeconomic data for most MPOs match the GSTDM socioeconomic data reasonably well. A slight discrepancy was found for the Macon model area where the scatterplot of the aggregated MPO model data and the corresponding GSTDM data did not fall neatly along the desired 45-degree line, indicating that there is a sub-optimal match level for this MPO. The following sections further elaborate the findings for each MPO.
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Albany Figure 7(a) compares total population and Figure 7(b) compares household counts between the GSTDM TAZs and the MPO TAZs that nest into them for the Albany MPO model area. The data points on the 45-degree dotted line identify a perfect match between the two models. The larger the deviation from this line, the larger the discrepancy between GSTDM- and MPO-level data. As Figure 7 shows, differences in the TAZ-level data between the GSTDM and Albany MPO model are rather minor, with only a few TAZs having total population higher in the GSTDM than in the MPO model, and a few with household counts higher in the MPO model.

(a)

(b)

FIGURE 7

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Albany (Source: Created by the Authors)

32

Athens Figure 8 illustrates how population and household data compare between the GSTDM and Athens MPO model. In this case, most points also fall on or near the 45-degree line, thus indicating a rather good match of data between the two models. Some TAZs have larger estimates (in particular for the household counts) in the MPO model.

(a)

(b)

FIGURE 8

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Athens (Source: Created by the Authors)

33

Augusta Figure 9 presents total population and household data for the GSTDM and MPO model in Augusta. The TAZ-level data generally compare well, with only a few TAZs showing some deviation from the dotted line with the 1:1 match.

(a)

(b)

FIGURE 9

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Augusta (Source: Created by the Authors)

34

Brunswick The TAZ-level assessment in the case of Brunswick indicates that the GSTDM and MPO data generally compare well, as shown in Figure 10. This is especially relevant in terms of the number of population where the data clusters along the 45-degree line. However, the assessment of the number of households suggests that MPO data appear to overestimate the number of households relative to the GSTDM data, to some extent.

(a)

(b)

FIGURE 10

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Brunswick (Source: Created by the Authors)

35

Cartersville The case of Cartersville indicates a noticeably good match between the GSTDM and MPO data, as shown in Figure 11. Both population and household data fall neatly along the 45-degree line. A few TAZs show a very minor discrepancy in terms of the number of population. The overall TAZ-level assessment in the case of Cartersville, nevertheless, shows consistent socioeconomic data across the GSTDM and MPO models.

(a)

(b)

FIGURE 11

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Cartersville (Source: Created by the Authors)

36

Columbus Figure 12 shows a good match of the population and household data between the GSTDM and the Columbus MPO model. With the exception of a handful of outliers, most cases fall on or very close to the 45-degree line.

(a)

(b)

FIGURE 12

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Columbus (Source: Created by the Authors)

37

Dalton Figure 13 shows the scatterplots of population and household data for the GSTDM and MPO model in the Dalton region. The scatterplots show a very good match, indicating that in this MPO model area the data from the MPO model match the GSTDM socioeconomic data well.

(a)

(b)

FIGURE 13

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Dalton (Source: Created by the Authors)

38

Gainesville As shown in Figure 14, except for a few outliers, population and household counts match quite well between the GSTDM and MPO model in the Gainesville region.

(a)

(b)

FIGURE 14

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Gainesville (Source: Created by the Authors)

39

Hinesville Figure 15 shows the comparison of population and household data between the Hinesville MPO model and the GSTDM. In this MPO, there is a good match between the data from the two models, with most cases falling on or very close to the 45-degree line.

(a)

(b)

FIGURE 15

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Hinesville (Source: Created by the Authors)

40

Macon In the case of Macon, as shown in Figure 16, a relatively large number of TAZs does not fall neatly along the 45-degree line, indicating some deviation of the MPO model data from the GSTDM socioeconomic data for some TAZs in this model area.

(a)

(b)

FIGURE 16

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Macon (Source: Created by the Authors)

41

Rome As shown in Figure 17, both population and household data from the statewide and MPO model have a good fit for the Rome MPO, with only a few minor deviations.

(a)

(b)

FIGURE 17

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ-Level in Rome (Source: Created by the Authors)

42

Savannah The analysis of the socioeconomic data in the Savannah MPO region suggests that the MPO model and the GSTDM share reasonably comparable socioeconomic data, as shown in Figure 18. While a few minor deviations appear in both the population and household comparisons, the case of Savannah points to a good match between GSTDM and MPO data where most observations fall neatly along the 45-degree line.

(a)

(b)

FIGURE 18

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Savannah (Source: Created by the Authors)

43

Valdosta As shown in Figure 19, the socioeconomic data of the Valdosta MPO model area match the GSTDM socioeconomic data rather well, with the majority of the TAZs that fall neatly along the 45-degree line.

(a)

(b)

FIGURE 19

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Valdosta (Source: Created by the Authors)

44

Warner Robins The analysis of the Warner Robins MPO indicates that the MPO model data have a good fit with the GSTDM data. As shown in Figure 20, there are only noticeable deviations in a few TAZs.

(a)

(b)

FIGURE 20

Distribution in GSTDM and MPO Socioeconomic Data: (a) Total Population and (b) Number of Households at GSTDM TAZ Level in Warner Robins (Source: Created by the Authors)

45

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Chapter 4 Integration of MPO Networks in the GSTDM Framework
An important part of this phase of the research involved the integration of the MPO and statewide networks. The research team explored various approaches to complete this task. One of these included integrating the full MPO road networks as a whole into the statewide model. However, after consultation with GDOT, it was decided not to consider this approach, since the coarser statewide TAZ system (which was updated during the previous step in this project) would not justify the use of the more detailed MPO-level network. The project team, therefore, considered alternative approaches to identify which parts of the MPO network can effectively work with the statewide TAZ system and import them into the statewide model network.
The approach that was ultimately used rebuilds the GSTDM road network using portions of the MPO model networks as inputs in each MPO model region. This chapter elaborates on the details of the methodology that were used to achieve the aforementioned goal, and further discusses the output of this process, and how it can be used in the future to simplify updating the statewide network. Chapter 5 explains how, as a result of the process used in this part of the project, the future model maintenance will be much simplified.
Methodology The project team used a nine-step approach to conflate the statewide and regional MPO networks and ensure consistency in network representation and future updates in the GSTDM network. Figure 21 provides a flowchart with the nine steps followed in this process. All tasks that were carried out in the Citilabs Cube software are marked in green.
47

The tasks that were run in ESRI ArcGIS are marked in yellow. The nine steps include the following:
Step 1: Deleting the portions of the GSTDM network inside the MPO model areas. This step is accomplished using ArcGIS software.1
Step 2: Preparing the MPO network for a traffic assignment to identify links relevant to the statewide scope. The MPO network and the updated GSTDM TAZs in each MPO model area are linked to generate new centroids and centroid connectors. The team used the Cube (Voyager) software package to complete this step. Also, where applicable, the project team corrected the MPO network coordinate system to allow matching the geographical location of the zonal and road systems. Appendix C provides further information on this topic.
Step 3: Carrying out a traffic assignment procedure. The research team used a hypothetical origindestination (OD) matrix using the GSTDM TAZ system to run a traffic assignment on the MPO network using Citilabs Cube Voyager.2 Additional details on how Cube processes are run according to travel demand forecasting assumptions can be found in Ortuzar & Willumsen (2011), which serves as the underlying modeling theory reference for this software package.
Step 4: Identifying the MPO subnetwork to be imported into the statewide network. The results from Step 3 and additional criteria--including considerations of traffic volumes, road class hierarchy, connectivity of the statewide network,
1 Version 10.3 2 Version 6.4
48

and the eventual presence of a link from the MPO network in the previous version of the statewide network--are used to identify the MPO subnetwork that is considered relevant for statewide modeling purposes. Step 5: Renumbering MPO nodes to prevent conflict with existing nodes in the GSTDM network. The node numbers in the MPO subnetworks are recoded into certain ranges to prevent conflicts. This step is done in ArcGIS. Step 6: Synchronizing MPO attribute tables with the statewide attribute table. This step involves: (1) matching the field names for attributes that are included in both the GSTDM and MPO networks, and (2) joining additional data from external sources for attributes that are needed in the statewide model but are not currently present in the MPO models. Steps 7 and 8: Joining the GSTDM network and MPO subnetwork for each MPO model region. This task is accomplished in Cube Voyager. Afterward, the project team manually edited the draft statewide network that includes the selected MPO subnetworks by connecting MPO external stations to the rest of the GSTDM network. This task is accomplished using the ArcGIS engine in the Citilabs Cube software. Step 9: Generating new centroid connectors in the GSTDM. New centroid connectors are generated to connect the GSTDM TAZ centroids to the updated GSTDM network.
49

FIGURE 21 Conceptual Diagram of the Methodologies Employed to Integrate
the Statewide and Regional MPO Networks (Source: Created by the Authors)
50

Step 1: Deleting the Portions of the GSTDM Network inside the MPO Model Areas To enrich the current statewide network using information from the MPO networks, the project team first needed to delete the portions of the statewide road network currently in the MPO model areas. The team carried out a location-based selection in the MPO model areas using ArcGIS, and then deleted the selected links. The output of this step is a statewide network ready to receive new networks for the MPO model areas, as shown in Figure 22. As a note, the research team, upon consultation with the GDOT Office of Planning, decided not to work on the Atlanta MPO model network, considering that the ARC maintains a very different model structure from the rest of the MPOs, and Chattanooga MPO model network, as this MPO is based in Tennessee and directly reports to the Tennessee Department of Transportation. For this reason, and as shown in Figure 22, the GSTDM model network was not modified in these MPO model areas.
51

FIGURE 22 Overview of Areas of the Original Statewide Network that were Removed
within the MPO Model Areas (Source: Created by the Authors)
Steps 2 and 3: Preparing the MPO Network and Carrying Out a Traffic Assignment To prepare the MPO network for a traffic assignment procedure using the statewide TAZs in this area as origins and destinations of the trips, the team had to first delete the
52

MPO zonal-based centroid and centroid connectors in each MPO network. The team carried out this part of the task using a Cube script file. After deleting the MPO-based centroids and centroid connectors, the research team generated new centroids and connectors using the statewide TAZ system in each MPO model area using the Citilabs Cube software. We then used the output of this step as input into the traffic assignment. Figure 23 shows an example from the Athens MPO model area.
53

FIGURE 23 Athens MPO Network Overlaid on the Statewide TAZs
within Athens MPO Model Area (Source: Created by Authors)
54

One of the challenges the project team faced in integrating the statewide and the regional MPO networks was defining a unique projection system to be used for both the statewide and the MPO networks. Using the same projection system in the GSTDM and a given MPO model is of particular importance to complete the following steps in this process. Table 8 summarizes the MPO models in which different projection systems were detected, precluding a faster integration with the GSTDM model.

TABLE 8 List of MPOs with Issues in Using GSTDM Projection System

MPO Code 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

MPO Albany Athens Atlanta Augusta Brunswick Cartersville Chattanooga Columbus Dalton Gainesville Hinesville Macon Rome Savannah Valdosta Warner Robins

Projection Issue




55

To address this issue, the research team developed a script-based automated process in the open-source RStudio platform. Appendix C contains a summary of the code and further discussion of this issue.
In all the MPO model areas with a projection issue, using this script-based process, the research team saved an updated MPO network in a GIS shapefile format using the same projection system of the GSTDM. Afterward, the research team imported the updated MPO networks into the Cube software to run the traffic assignment process.
Steps 4 and 5: Identifying Relevant Portions of the MPO Network for Statewide Modeling Purposes and Renumbering Nodes The output of the traffic assignment step identifies the portions of an MPO network that, considering the coarser statewide TAZ system, would not receive any traffic load and can be considered for exclusion from the GSTDM model. The project team used the output of the traffic assignment step and considered a number of additional criteria to suggest whether certain links in the MPO road network should be excluded from the updated statewide model network.
The additional criteria that were selected in consultation with the GDOT staff include the following:
Road class hierarchy: The project team made sure that road links with higher road classifications would not be excluded from the proposed network, even in the case of zero traffic load in the traffic assignment. They, therefore, decided to keep all roads classified as minor arterial, or higher, in the updated GSTDM road network.
Network connectivity: The team ensured that there would be no dangling links or portions of a through route that was removed from the final proposed GSTDM
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road network. In addition to simple graph connectivity, they also kept roads to the degree that made sense in terms of traffic flows and uninterrupted routes in a region. For example, if a segment of a road after an intersection was flagged as having zero-load in the traffic assignment but excluding that segment from the network seemed non-natural in terms of replicating a realistic representation of traffic flows in real life, they decided to retain those links in the road network. Existence in the current statewide model: Since the aim is to enrich, rather than modify, the statewide model network, the researchers kept all road links that were already included in the previous version of the GSTDM network in the modified statewide network. Statewide zonal system and base maps: The research team further checked the road network selection against base maps and the statewide TAZ system to make well-informed decisions on the links to keep in the final road network. Figure 24 shows an example of the identified portion of the Athens network based on the procedure described above. MPO network nodes were renumbered to prevent conflicts with other node numbers when merging the MPO networks into the revised statewide network. To do this, the researchers reserved an interval of 20,000 node numbers to each MPO. This provides a range of node numbers large enough to accommodate further model updates, as well. Table 9 summarizes the range of node numbers for all nodes imported from the various MPO model networks.
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FIGURE 24 Portions of the Athens MPO Network (Red Line) to be Deleted
in the GSTDM Network (Source: Created by the Authors)
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TABLE 9 MPO Node Renumbering System

MPO Name Albany Athens Atlanta Augusta Brunswick Cartersville Chattanooga Columbus Dalton Gainesville Hinesville Macon Rome Savannah Valdosta Warner Robins

Starting Node Number 80000 100000 120000 140000 160000 180000 200000 220000 240000 260000 280000 300000 320000 340000 360000 380000

Step 6: Synchronizing the Attribute Tables of the GSTDM and MPO Model Before joining the selected portions of the MPO model networks into the updated GSTDM road network, the project team modified the attribute table of the MPO networks to make it consistent with the statewide model. As part of this task, the research team renamed the MPO attribute fields to match the attribute names in the statewide network (the following section of this report, and Table 10, provides more details on the GSTDM and MPO network attributes).
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However, the attribute table of the GSTDM road network also contains a number of attributes that are not present in the MPO model networks. These attributes include 2015 passenger and truck counts, state route indicator, NHS road classification, strategic highway network classification, and state screenline. These attributes were added to the MPO model networks using external datasets provided by GDOT.
Moving forward, a modified attribute table was defined to standardize the attribute fields in the GSTDM and MPO model networks. The researchers recommend that future versions of the MPO models adopt this attribute table, which will greatly simplify transferring these pieces of information into future versions of the GSTDM.
Steps 7 and 8: Joining the Selected Portions of the MPO Networks with the GSTDM Network After completing Steps 16, and updating the attribute tables for all MPO model networks, the selected portions of the MPO networks that were considered relevant for statewide modeling purposes were joined with the statewide network from Step 1. Using a Cube script, the team joined the selected subnetworks from all MPO models to the remaining portions of the statewide network for non-MPO areas from Step 1. After joining the network components to form the updated GSTDM network, they still had to stitch the external stations of the MPO subnetworks to the rest of the statewide model, therefore allowing full connectivity across all areas of the statewide network. The project team completed this part of the task manually using the GIS engine in Cube.
Step 9: Generating New Centroid Connectors Using Cube Voyager, new centroid connectors were generated for the new portions of the GSTDM network that were imported from the MPO model networks. The automatic
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centroid connector generation function was used in the Cube platform, followed by additional manual adjustments, to complete this task.
Output The output of the methodology described above is a unified GSTDM transportation network that is based on the MPO model network components inside all MPO model areas. This outcome brings several advantages compared to the old GSTDM network. First, the statewide model network is now fully consistent with the MPO model networks and is based on the same data sources. This allows better comparison of traffic flows on the components of the road networks between the models. Second, the updated GSTDM network benefits from a much larger number of details imported from the MPO models in all MPO model areas. These include: (1) greater density of the road network and a larger set of road links included in the statewide network; (2) better representation of road intersections, e.g. with proper representation of freeway ramps; and (3) adoption of the "true shape" of roads in the GTSDM network.1 It facilitates future updates of the statewide network, in addition to the MPO networks sharing the same attribute table as the GSTDM network. Future updates of the GSTDM network will be much simplified as they will benefit from the process of updating the MPO model networks, with all updates in the MPO road networks being transferred and consistently imported also into the GSTDM network.
1 The true shape of roads was imported for all road links inside MPO model areas as part of the processed used for the integration of the MPO model networks in the GSTDM network. In future updates of the GSTDM, true road shapes for the remaining non-MPO areas could be imported with a relatively modest effort.
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Proposed Unified Attribute Table As discussed, the updated unified road network attribute table (see Table 10) resolves the discrepancies between the attribute tables of GSTDM and MPO models. While MPOs might require additional attributes tailored to their needs and area to be included in their models, the research team recommends that, at minimum, future updates of the MPO models adopt the attribute fields used by the GSTDM and include all information summarized in this table.
The updated attribute table also includes one new binary attribute, "GSTDMMPO," that was added to identify links in the MPO model networks that are relevant for statewide purposes and should be included in the GSTDM network. Future updates of these model networks will rely on this attribute to identify links in the MPO networks that should be included in the GSTDM; modelers will focus on updating the MPO model networks in all MPO model areas, and through updating this field appropriately, these changes will be carried over also to the GSTDM transportation network (for both the base year and future scenario networks).
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TABLE 10 Proposed Attribute Table for Revised GSTDM and MPO Model Networks

Proposed Attribute Name
A

Previous GSTDM Attribute
A

Previous MPO Model Attribute
A (modified)

B Road_name

B
PRIMARY_NA, SECONDARY_, LOCAL_NAME

B (modified) ROAD_NAME

Description
Beginning node number Ending node number
Road name

Distance

Distance

DISTANCE

Link length

FCLASS
FC2015 LANES STATUS NHS STRAHNET

FCLASS
FC2015 LANES STATUS NHS, NHS2010 STRAHNET

FTYPE, uab2010
FTYPE LANES, LANESAM, LANESPM, TOTAL_LANE -
-
-

Old road functional classification (keeping for compatibility purposes) New road functional classification
Number of lanes
Status of current road NHS category (latest) Strategic highway system

County

COUNTY

-

County name

AADT2015

AADT2015

-

AADT 2015 count

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Proposed Attribute Name
TRK2015

Previous GSTDM Attribute
TRK2015

STATEROUTE

STATEROUTE

EXT_STATIO

EXT_STATIO

EXT_DIRECT

EXT_DIRECT

PCTOLL TKTOLL Use Remi2016 MPO_CODE MPO_MODEL MPO_NAME FIPS SCREENLINE TC_NUMBER

PCTOLL TKTOLL Use Remi2016 MPO_CODE MPO_MODEL MPO_NAME FIPS SCREENLINE TC_NUMBER

Previous MPO Model Attribute
-

Description Truck 2015 count

-

State route indicator

CSTATION FOR THE BOUNDARY MPOS

State external station (applicable to MPOs on the boundary only)

State external

location by

-

orientation (applicable to

MPOs on the

boundary only)

-

Passenger toll section

-

Truck toll section

-

Truck-only lane indicator

-

Remi districts

-

MPO code

-

MPO model area code

-

MPO name

COUNTY

FIPS code

CSTATION

Screenline indicator
Traffic count station number

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Proposed Attribute Name
MPOSTATION

Previous GSTDM Attribute
MPOSTATION

SCLSTATION

SCLSTATION

Previous MPO Model Attribute -
-

GSTDM-MPO

-

-

Description
MPO external station number
Screenline station
MPO link included in GSTDM (new attribute)

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Chapter 5 Conclusions and Future Maintenance of the GSDTM
In this report, the project team stated the need for the conflation of the TAZ and network system of the GSTDM and MPO models, and summarized the methods the team used to carry out each task required to accomplish this goal. The research team redrew the GSTDM TAZ boundaries from scratch using an automatic process and resolved discrepancies between the MPO-level and state-level TAZs. After discussion with GDOT and their consultant, it was decided to keep the number of TAZs constant to a total of 3,770 TAZs, of which 3,243 are in Georgia. The result of this process allows for the easy transfer and comparison of data between the models, and it makes the maintenance of the GSTDM straightforward. As MPO TAZs are now uniquely linked to GSTDM TAZs, future modifications of MPO TAZ boundaries will be translated into corresponding modifications in the GSTDM TAZs, and the process of integration of the zonal system will be maintained in future versions of the model.
The statewide network in the MPO model areas was also updated with the portions of the more detailed MPO-level networks that are relevant to the GSTDM scope. To identify the relevant portions of the MPO networks, the research team carried out a traffic assignment on the MPO networks using the statewide TAZ system in their areas as origins and destinations of the trips. This approach helped identify what parts of the more detailed MPO networks are relevant for statewide purposes and what other portions of the networks would not receive traffic load due to the coarser-level statewide TAZ system. The team, subsequently, added a binary attribute to each MPO network file to flag whether a link is statewide relevant. Future updates of the network (as discussed in
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details below) can use this attribute to carry over any MPO network updates into the GSTDM network system.
As a result of the approaches developed in this research project, the research team presents three important recommendations and takeaways from this work:
First, we recommend that GDOT adopt a protocol to use the same sources and standards to generate and maintain socioeconomic data, TAZs, and road networks, at both the statewide and MPO level.
Second, in future model updates, adequate consideration should be given to the benefits offered by increasing the total number of statewide TAZs. In particular, using the same MPO-level TAZs and networks in the GSTDM model would further simplify model maintenance and improve the accuracy of the statewide model results.
Third, all future versions of the statewide and MPO models should use the same socioeconomic and network attributes.
Future Maintenance of the GSTDM One of the primary challenges in development and maintenance of travel demand models is developing scalable and replicable methods that can be easily applied (and updated) during future maintenance of the model. Maintaining a complex travel demand model such as the Georgia Statewide Travel Demand Model (GSTDM) may prove to be a daunting assignment if replicability of tasks during future modeling updates is not possible. In this research, the project team developed a process to integrate the MPO models in the GSTDM that will ensure consistency among the models during future model updates and allow for easier maintenance of the model. The remainder of this
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section describes the steps that will be required to ensure that the integration of the MPO models with the GSTDM is retained during future model updates, and that the benefits from this integration are maximized.
Maintaining Consistency of TAZs and Socioeconomic Data As an outcome of this project, the research team delivered a revised TAZ system for the GSTDM that ensures consistency of the GSTDM TAZs with the zonal system used by the MPO models. By redrawing the GSTDM TAZ boundaries to perfectly align with the MPO TAZs, the two zonal systems were synchronized; correspondence tables uniquely linked the list of MPO TAZs that now nest perfectly inside GSTDM TAZs. Through maintaining this synchronization in future model updates, if MPO TAZ boundaries are modified in the future, the boundaries of the corresponding GSTDM TAZs will change accordingly. Similarly, the process developed in this project ensured that socioeconomic data (and any other input/output data) can now be easily compared between MPO and GSTDM TAZs. Thus, the research team recommends that GDOT review carefully any eventual discrepancies in the current distribution of the socioeconomic data between these models.
Moving forward, only one master set of current socioeconomic data, obtained from official sources (e.g., U.S. decennial census and American Community Survey, or ACS, data), and of future estimates (for future model scenarios) would be required. These data can be generated for the MPO TAZs. Through a process of aggregation of these data, using the correspondence tables between MPO and GSTDM TAZs, they can be merged to provide input to the statewide travel demand model.
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Maintaining Consistency of Model Networks Figure 25 illustrates the combined conceptual approach, indicating the research workflow the project team employed over the course of this project on the left and the streamlined maintenance process that GDOT would require to maintain the GSTDM network on the right. As shown in the figure, the proposed GDOT maintenance process has significantly fewer steps than the research workflow that was originally carried out for this project, making it simpler to adopt and implement during future maintenance of the model. Specifically, while the research workflow employed by the project team has nine steps, the model maintenance process would only require four steps, at maximum, assuming that MPO networks are maintained using a configuration compatible with the proposed approach and use the unified attribute table that was defined. Specifically, no traffic assignment or other demanding procedures that were carried out as part of this project will need to be replicated during the future model updates.
Central to the future maintenance of the GSTDM network is the attribute flagging the GSTDM relevance for road links included in the MPO networks, as described in the preceding chapter. This attribute can be used during future MPO model network updates to identify the portion of the network that is considered relevant for statewide modeling purposes, and that will be automatically imported in future versions of the GSTDM.
Once the MPO model networks are updated during future MPO model updates, the proposed GSTDM network maintenance process will only require working on the statewide model (orange boxes in Figure 25) without the need to carry out and replicate heavy tasks on the MPO model components (blue boxes in Figure 25) as long as the latter maintain the structure (i.e., attribute table, node numbering system, and updated MPOGSTDM relevance attribute) as proposed in this report.
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FIGURE 25 Future Maintenance Process for GSTDM Transportation Network
(Source: Created by the Authors) The four-step process for future GSTDM network maintenance requires the following steps: Step 1: Deleting portions of the existing GSTDM network within MPO model
areas. As described in Chapter 4, this step is considerably straightforward; the
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existing statewide network within the MPO model areas are deleted, technically creating "holes" in the network. This step is easy to accomplish and the project team has developed a series of replicable script using the Cube programming language, which can be easily integrated into a Cube "catalog" to ease the maintenance process. Steps 2 and 3: Joining the MPO subnetworks to the GSTDM network and connecting the MPO external stations with the rest of the network. These steps require joining the MPO subnetworks (e.g., the portions of the MPO networks considered relevant for statewide modeling purposes and identified through the new MPOGSTDM attribute) to the remaining portions of the GSTDM network for non-MPO areas. The statewide-relevant portions of the MPO model networks would fill the "holes" produced in the GSTDM network during Step 1. This process would be followed by manual checks to ensure proper connectivity at the MPO external boundary crossings to make sure that the entire GSTDM transportation network is properly connected. The researchers expect that no additional manual modifications would actually be required, as long as the new MPO node numbering system is maintained and no additional MPO external crossings are added.1 Steps 2 and 3 are the most critical steps to be run during the future model maintenance, but they will ensure that the integration between the GSTDM and MPO model networks is maintained without additional tasks. For
1 If new external stations are added to the MPO model area boundaries, some modifications would be required to ensure connectivity of these new links in the updated GSTDM network.
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the successful application of this approach, the project team stresses the importance of adopting a unified structure (i.e., attribute tables, non-overlapping node numbers) between the GSTDM and MPO model networks. Step 4: Updating centroids and centroid connectors. As indicated in the previous chapter, the project team has developed a replicable script to allow updating the statewide centroids and centroid connectors only within MPO areas or for a specific MPO of interest, which is a necessary process to obtain a working statewide model network.
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Chapter 6 References
Atkins. (2013a). Development of Statewide Model Report. Report prepared for the Georgia Department of Transportation.
Atkins. (2013b). Travel Demand Model Documentation for the Savannah MPO. Report prepared for the Georgia Department of Transportation.
Cambridge Systematics, & HNTB. (2015a). Travel Demand Model Documentation for the Dougherty Area Regional Transportation Study. Report prepared for the Georgia Department of Transportation.
Cambridge Systematics, & HNTB. (2015b). Travel Demand Model Documentation for the Macon MPO. Report prepared for the Georgia Department of Transportation.
Cambridge Systematics, & HNTB. (2015c). Travel Demand Model Documentation for the Valdosta-Lowndes MPO. Report prepared for the Georgia Department of Transportation.
FHWA. (2012). Georgia Department of Transportation (GDOT) Statewide Travel Model Peer Review Report. Report prepared for the Georgia Department of Transportation. Retrieved October 3rd, 2018, from http://www.dot.ga.gov/InvestSmart/Documents/Travel%20Demand%20Model/St atewide%20Travel%20Model%20Peer%20Review%20Report%20%20Sept%202012.pdf
HNTB. (2014). Travel Demand Model Documentation for the Madison Athens-Clarke Oconee Regional Transportation Study. Report prepared for the Georgia Department of Transportation.
HNTB. (2015a). Travel Demand Model Documentation for the Brunswick/Glynn County Metropolitan Planning Organization. Report prepared for the Georgia Department of Transportation.
HNTB. (2015b). Travel Demand Model Documentation for the ColumbusPhenix City Transportation Study. Report prepared for the Georgia Department of Transportation.
HNTB. (2015c). Travel Demand Model Documentation for the GainesvilleHall Metropolitan Planning Organization. Report prepared for the Georgia Department of Transportation.
HNTB. (2015d). Travel Demand Model Documentation for the Greater Dalton Metropolitan Planning Organization. Report prepared for the Georgia Department of Transportation.
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HNTB. (2015e). Travel Demand Model Documentation for the Hinesville Area Metropolitan Planning Organization. Report prepared for the Georgia Department of Transportation.
HNTB. (2015f). Travel Demand Model Documentation for the Warner Robins Area Transportation Study. Report prepared for the Georgia Department of Transportation.
HNTB. (2016a). Travel Demand Model Documentation for the CartersvilleBartow Metropolitan Planning Organization. Report prepared for the Georgia Department of Transportation.
HNTB. (2016b). Travel Demand Model Documentation for the RomeFloyd County Metropolitan Planning Organization. Report prepared for the Georgia Department of Transportation.
Ortuzar, J., & Willumsen, L. G. (2011). Modelling Transport: John Wiley & Sons. WSP. (2017). Activity-Based Model Specification Report. Report prepared for the Atlanta
Regional Commission. Retrieved October 1st, 2018, from https://cdn.atlantaregional.org/wp-content/uploads/abm-specification-report2017.pdf
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Appendix A Additional Details on the Automation Process for TAZ Synchronization
The following pages present additional examples of the TAZ integration process between the GSTDM and MPO models.
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FIGURE 26 Example of MPO TAZs (Blue Line) and GSTDM TAZs (Red Line)
within the Valdosta MPO Model Region (Source: Created by the Authors)
78

FIGURE 26 (Continued) Example of MPO TAZs (Blue Line) and GSTDM TAZs (Red Line)
within the Valdosta MPO Model Region (Source: Created by the Authors)
79

FIGURE 27 Example of Existing MPO TAZs (Left) and GSTDM TAZs (Right)
(Source: Created by the Authors)
80

FIGURE 28 Comparison between the Existing (Left) and Revised GSTDM TAZs (Right)
(Source: Created by the Authors)
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The following script was used in RStudio to automate the MPO and GSTDM TAZs integration process.
mpos<-read.csv("(1)",header = T,stringsAsFactors=FALSE)
for(i in 1:nrow(mpos)){ loc=mpos[i,1] lyr=mpos[i,2] tname=mpos[i,3] mpo_s<-readOGR(dsn=paste0("(2)",loc), layer=lyr) mpo_s<-mpo_s[,tname] mpo_s.df<-data.frame(mpo_s[,tname]) mpo_c = data.frame(gCentroid(mpo_s,byid=TRUE))
mpo_c.spdf <- SpatialPointsDataFrame(coords = mpo_c, data =mpo_s.df, proj4string = CRS(projection(stw)))
#Join points layer to polygons mpo_sp<-point.in.poly(mpo_c.spdf, stw)
#Merge back with MPO TAZs polygon mpo_s@data<-merge(mpo_s@data, mpo_sp@data, by.x=tname, by.y=tname)
#Dissolve mpo_s.dis<-unionSpatialPolygons(mpo_s,ID=mpo_s@data$TAZ_ID,threshold = 0) mpo_df<-as(mpo_s, "data.frame") mpo_df_agg<-aggregate(as.numeric(mpo_df[,2]), list(mpo_df$TAZ_ID), sum) row.names(mpo_df_agg)<-as.character(mpo_df_agg$Group.1)
mpo_s.dis<-SpatialPolygonsDataFrame(mpo_s.dis,mpo_df_agg)
writeOGR(obj=mpo_s.dis,dsn="(3)",layer=paste0(loc,"D"), driver="ESRI Shapefil e",overwrite_layer=T,check_exists=T) } Notes: (1) Location directory for a CSV table indicating the name of each MPO TAZ shapefile (2) Location directory for the GSTDM TAZ shapefile (3) Location directory to save the output MPO TAZ shapefile
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Appendix B Socioeconomic Data Comparison

This section provides the comparison of population and household data (at the GSTDM TAZ level) between the statewide and MPO models. In the comparisons reported in this appendix, the research team used data from the 2010 GSTDM TAZ system (and not the newer TAZs from the 2015 model update) to make the data more comparable to the corresponding socioeconomic data in the MPO models. While 2010 socioeconomic data were available for all MPOs, only selected MPO models had been updated with the 2015 base year at the time of this project. This explains why the total number of statewide TAZs in each MPO model area in the comparisons of the socioeconomic data in this Appendix B is slightly different than the total number of statewide TAZs in each MPO model area resulting from the process of TAZ synchronization described in Chapter 2 (Table 4).

Albany

TABLE 11

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Albany MPO Model Area (# TAZs = 38)

TAZ_ID
1414 1415 1416 1417 1418 1419 1420 1421 1422 1423

MPO 1708 2173 6215 3491 6606 4798 6276 2938 1783 4151

# Population
GSTDM 1708 2173 6215 3491 6606 4798 6276 2938 1783 4151

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MPO 661 854 2402 1284 2630 2181 2961 1364 820 1837

# Households
GSTDM 661 854 2402 1284 2630 2181 2961 1364 820 1837

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

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TAZ_ID
1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 Total

MPO 2106 2082 1607 1419 2825 2520 4798 3123 5944 4580 1831 6748 2953 2423 3751 1780 2898 579 1008 639 2325 759 1299 4645 4792 1680 3586 6337 121176

# Population
GSTDM 2106 2082 1607 1557 2896 2520 5627 3123 5944 4580 1831 6748 2953 2423 3751 1780 2898 561 1026 639 2325 1408 1299 4645 4792 1680 3586 6337
122863

% diff. 0.00 0.00 0.00 -8.86 -2.45 0.00 -14.73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.21 -1.75 0.00 0.00 -46.09 0.00 0.00 0.00 0.00 0.00 0.00 -1.37

MPO 877 910 646 644 1133 1013 1657 1154 2244 2239 669 3696 1135 900 1442 686 1121 206 354 250 763 243 443 1593 1751 618 1377 2103 48861

# Households
GSTDM 877 910 646 585 1161 1013 1657 1154 2244 1463 669 1851 1135 900 1442 686 1121 200 360 250 763 248 443 1593 1751 618 1272 2208
46214

% diff. 0.00 0.00 0.00 10.09 -2.41 0.00 0.00 0.00 0.00 53.04 0.00 99.68 0.00 0.00 0.00 0.00 0.00 3.00 -1.67 0.00 0.00 -2.02 0.00 0.00 0.00 0.00 8.25 -4.76 5.73

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Athens

TABLE 12

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Athens MPO Model Area (# TAZs = 105)

TAZ_ID
1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034

MPO 657 2419 576 1504 2426 1963 352 1680 495 1853 1351 178 333 1157 935 1312 799 265 1497 1541 2060 2674 1334 721 1593 0 7798 1213 2138 737 2335 1226 1753 2788

# Population
GSTDM 657 2442 495 1417 2426 2003 352 1821 432 1844 1704 178 333 1164 944 1232 799 265 1527 1535 2060 2672 1292 721 1593 124 7869 1213 2076 1365 2365 1226 1753 2788

% diff. 0.00 -0.94 16.36 6.14 0.00 -2.00 0.00 -7.74 14.58 0.49 -20.72 0.00 0.00 -0.60 -0.95 6.49 0.00 0.00 -1.96 0.39 0.00 0.07 3.25 0.00 0.00
-100.00 -0.90 0.00 2.99 -46.01 -1.27 0.00 0.00 0.00

MPO 229 841 234 1114 1454 670 117 556 190 609 505 58 118 424 341 459 274 104 564 547 724 976 456 261 571 0 3229 530 869 263 1183 563 580 1002

# Households
GSTDM 229 848 202 530 823 683 117 601 165 575 621 58 118 427 352 459 274 104 564 544 724 980 441 261 571 95 3258 530 840 617 1195 563 580 1002

% diff. 0.00 -0.83 15.84 110.19 76.67 -1.90 0.00 -7.49 15.15 5.91 -18.68 0.00 0.00 -0.70 -3.13 0.00 0.00 0.00 0.00 0.55 0.00 -0.41 3.40 0.00 0.00
-100.00 -0.89 0.00 3.45 -57.37 -1.00 0.00 0.00 0.00

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TAZ_ID
1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073

MPO 84 3629 1414
11984 1978 6005 12557 2252 765 2852 3025 1909 1115 1233 2416 2803 1016 2008 1636 819 1356 1596 2642 2136 2474 834 2317 3002 1369 471 590 3017 4577 975 776 5718 5735 1966 1882

# Population
GSTDM 84
2603 1419 11956 1978 6005 11901 2252 975 2824 2646 1909 1128 1233 2615 3001 1016 1566 1640 789 1344 1596 2685 2136 2287 872 2317 3002 1369 471 595 3017 4548 1153 776 5709 5804 1904 2025

% diff. 0.00 39.42 -0.35 0.23 0.00 0.00 5.51 0.00 -21.54 0.99 14.32 0.00 -1.15 0.00 -7.61 -6.60 0.00 28.22 -0.24 3.80 0.89 0.00 -1.60 0.00 8.18 -4.36 0.00 0.00 0.00 0.00 -0.84 0.00 0.64 -15.44 0.00 0.16 -1.19 3.26 -7.06

MPO 43 1579 541 5319 931 2835 2964 1061 410 1258 1619 837 439 435 1228 1104 329 1059 746 364 450 736 1062 910 891 184 520 1156 580 184 234 946 1610 430 256 1749 2308 751 905

# Households
GSTDM 43
1139 542 4909 928 2835 2555 1061 463 1244 1392 837 451 435 1264 1193 329 798 729 364 450 735 1086 910 871 206 820 1156 580 184 236 946 1588 402 256 1743 2242 715 762

% diff. 0.00 38.63 -0.18 8.35 0.32 0.00 16.01 0.00 -11.45 1.13 16.31 0.00 -2.66 0.00 -2.85 -7.46 0.00 32.71 2.33 0.00 0.00 0.14 -2.21 0.00 2.30 -10.68 -36.59 0.00 0.00 0.00 -0.85 0.00 1.39 6.97 0.00 0.34 2.94 5.03 18.77

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TAZ_ID
1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1987 1988 1989 1990 1991 1992 1993 1994 2972 Total

MPO 1149 1017 2479 654 336 789 1031 925 683 844 470 804 190 965 1076 422 1841 2524 612 779 371 470 451 379 1462 1283 885 3603 137 505 1128 5697 192557

# Population
GSTDM 1748 1017 2535 664 290 804 1092 926 683 809 470 928 190 965 1125 408 1841 2570 676 772 371 470 451 391 1462 1295 896 3420 137 492 1108 5698
192541

% diff. -34.27 0.00 -2.21 -1.51 15.86 -1.87 -5.59 -0.11 0.00 4.33 0.00 -13.36 0.00 0.00 -4.36 3.43 0.00 -1.79 -9.47 0.91 0.00 0.00 0.00 -3.07 0.00 -0.93 -1.23 5.35 0.00 2.64 1.81 -0.02 0.01

MPO 503 367 921 248 99 316 381 327 283 320 164 306 72 352 410 164 714 915 245 299 162 180 177 139 558 519 345 1333 45 205 412 2117 75176

# Households
GSTDM 665 367 934 248 80 321 403 328 283 305 164 353 72 352 430 161 714 934 268 296 162 180 174 143 558 524 349 1272 45 199 409 2148
73191

% diff. -24.36 0.00 -1.39 0.00 23.75 -1.56 -5.46 -0.30 0.00 4.92 0.00 -13.31 0.00 0.00 -4.65 1.86 0.00 -2.03 -8.58 1.01 0.00 0.00 1.72 -2.80 0.00 -0.95 -1.15 4.80 0.00 3.02 0.73 -1.44 2.71

87

Augusta

TABLE 13

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Augusta MPO Model Area (# TAZs = 101)

TAZ_ID
1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181

MPO 1162 935 950 3835 1597 917 5615 2145 7275 6534 5042 3893 2386 7923 4005 5800 7174 9740 4199 6413 668 9539 1224 4295 4240 6714 802 2985 1559 1175 1340 2019 9694 3263

# Population
GSTDM 1162 935 950 3835 1597 917 5615 1305 8115 6534 5042 3893 2386 7923 4005 5800 7174 9740 4199 6413 668 9539 1224 4295 4240 6655 802 2985 1559 1175 1340 2019 9753 3263

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 64.37 -10.35 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.88 0.00 0.00 0.00 0.00 0.00 0.00 -0.60 0.00

MPO 420 345 391 1463 577 220 2048 773 2781 2275 1876 1340 756 2417 1418 1896 2568 3745 1652 2525 300 3587 467 1611 1453 2346 278 1085 590 474 503 738 3911 1340

# Households
GSTDM 420 345 391 1463 577 220 2048 434 3120 2275 1876 1340 756 2417 1418 1896 2568 3745 1652 2525 300 3587 467 1611 1453 2322 278 1085 590 474 503 738 1365 790

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 78.11 -10.87 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.02 0.00 0.00 0.00 0.00 0.00 0.00 186.52 69.92

88

TAZ_ID
1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220

MPO 4796 5594 5153 3987 5183 3816 6303 6541 3588 11286 2303 3723 5106 4904 2081 6019 5600 8158 6841 8625 11280 5243 6226 2907 1072 3908 3441 5594 2590 1718 2578 2208 124 1743 4013 2935 1103 1312 3185

# Population
GSTDM 4796 5594 5153 3987 5183 3816 6303 6527 3588 11300 2303 3723 5106 4904 2081 6019 5600 8158 6841 8625 11280 5243 6226 2907 1072 3908 3441 5594 2590 1718 2578 2208 124 2457 4013 2935 1103 1312 3185

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.21 0.00 -0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -29.06 0.00 0.00 0.00 0.00 0.00

MPO 1758 3140 2294 2041 2646 1706 2858 3254 1726 4932 1015 1744 2319 1997 943 2073 2203 3309 2326 3026 3854 1763 2195 1048 385 1383 1212 2004 832 613 903 813 70 642 1541 1088 424 495 1263

# Households
GSTDM 1753 3020 2243 2015 2646 1706 2531 3223 1726 4936 1006 1744 2267 1941 943 2027 2203 3299 2323 3012 3847 1759 2082 1048 377 1372 1198 2001 832 613 903 811 70 637 1532 1086 424 491 1261

% diff. 0.29 3.97 2.27 1.29 0.00 0.00 12.92 0.96 0.00 -0.08 0.89 0.00 2.29 2.89 0.00 2.27 0.00 0.30 0.13 0.46 0.18 0.23 5.43 0.00 2.12 0.80 1.17 0.15 0.00 0.00 0.00 0.25 0.00 0.78 0.59 0.18 0.00 0.81 0.16

89

TAZ_ID
1221 1222 1223 1224 1225 1226 1227 3393 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 3413 Total

MPO 62 3845 935 2471 2785 672 3262 9396 4651 3653 9272
13601 12699 20319 20520 13063 15432 4832 5871 4230 7154 8320 5707 4625 3084 5728 10642 4271 510946

# Population
GSTDM 62 3845 935 2471 2785 672 3262 9401 4660 3648 9276
13593 12692 20328 20584 12973 15587 4830 5817 4193 7150 8312 5635 4815 2589 6149 11388 3464 511674

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.05 -0.19 0.14 -0.04 0.06 0.06 -0.04 -0.31 0.69 -0.99 0.04 0.93 0.88 0.06 0.10 1.28 -3.95 19.12 -6.85 -6.55 23.30 -0.14

MPO 26 1564 571 1333 1348 441 1267 2944 1750 1187 3455 5892 5536 7867 9193 5194 5905 1855 2217 1573 2674 3157 2010 1804 1259 2139 4292 1691
200151

# Households
GSTDM 26 1539 400 1184 1036 414 1262 2943 1757 1189 3459 5886 5531 7872 9222 5156 5966 1854 2197 1561 2673 3151 1980 1877 1073 2295 4577 1382
195419

% diff. 0.00 1.62 42.75 12.58 30.12 6.52 0.40 0.03 -0.40 -0.17 -0.12 0.10 0.09 -0.06 -0.31 0.74 -1.02 0.05 0.91 0.77 0.04 0.19 1.52 -3.89 17.33 -6.80 -6.23 22.36 2.42

90

Brunswick

TABLE 14

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Brunswick MPO Model Area (# TAZs = 61)

TAZ_ID
1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652

MPO 880 885 789 285 131 895 1376 3986 3013 1144 1749
1 0 1016 377 1221 496 448 384 4007 490 804 0 499 3551 2 1330 1811 1632 1978 612 1423 1238 1010

# Population
GSTDM 880 885 789 285 131 895 1376 3986 3013 1164 1729 1 0 1016 377 1221 496 448 384 4007 490 805 0 499 3551 2 1776 1811 1675 1984 612 1423 1318 1010

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.72 1.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.12 0.00 0.00 0.00 0.00 -25.11 0.00 -2.57 -0.30 0.00 0.00 -6.07 0.00

MPO 384 377 348 111 51 344 587 1682 1240 493 708 1 0 397 155 508 238 196 182 1562 220 609 0 213 1729 1 527 794 731 933 266 614 555 436

# Households
GSTDM 335 327 305 106 48 306 519 1467 1155 467 647 1 0 365 138 460 204 172 156 1417 195 435 0 183 1404 1 454 669 625 789 225 513 455 396

% diff. 14.63 15.29 14.10 4.72 6.25 12.42 13.10 14.66 7.36 5.57 9.43 0.00 0.00 8.77 12.32 10.43 16.67 13.95 16.67 10.23 12.82 40.00 0.00 16.39 23.15 0.00 16.08 18.68 16.96 18.25 18.22 19.69 21.98 10.10

91

TAZ_ID
1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 Total

MPO 1219 1155 7207 1047 954 352 573 812 1333 806 2354
23 3103 165 3527 673
25 1237 3089 1141 2730 305 724 3507 1889
0 81 79494

# Population
GSTDM 1219 705 7207 1047 954 340 573 812 1333 806 1987 390 3103 165 3530 671 25 1132 3194 1651 2732 305 188 3507 1890 11 110 79626

% diff. 0.00 63.83 0.00 0.00 0.00 3.53 0.00 0.00 0.00 0.00 18.47 -94.10 0.00 0.00 -0.08 0.30 0.00 9.28 -3.29 -30.89 -0.07 0.00 285.11 0.00 -0.05
-100.00 -26.36 -0.17

MPO 509 462 3276 546 517 203 254 331 584 373 833 12 1395 84 1662 441 13 796 2070 704 1608 173 384 2122 1301 0 41 37886

# Households
GSTDM 457 324 2869 441 437 169 219 297 500 343 732 10 1206 53 1383 375 17 517 1770 792 1229 140 102 1556 841 0 56 31774

% diff. 11.38 42.59 14.19 23.81 18.31 20.12 15.98 11.45 16.80 8.75 13.80 20.00 15.67 58.49 20.17 17.60 -23.53 53.97 16.95 -11.11 30.84 23.57 276.47 36.38 54.70 0.00 -26.79 19.24

92

Cartersville

TABLE 15

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Cartersville MPO Model Area (# TAZs = 29)

TAZ_ID
830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 Total

MPO 6895 2048 3091 5507 3403 4260 2744 3254 1964 1125 1694 2845 1444 4494 3838 3859 5008 3189 4195 6732 1714 2231 3846 2325 2947 3713 5659 2657 2486 99167

# Population
GSTDM 6895 2058 3091 5511 3458 4512 2744 3254 1964 1125 1694 2845 1444 4494 4425 3936 5008 3189 4195 6737 1714 2231 3846 2279 2993 3713 5594 2657 2486
100092

% diff. 0.00 -0.49 0.00 -0.07 -1.59 -5.59 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -13.27 -1.96 0.00 0.00 0.00 -0.07 0.00 0.00 0.00 2.02 -1.54 0.00 1.16 0.00 0.00 -0.92

MPO 2461 807 1103 2155 1294 1618 1117 1128 679 412 631 1023 515 1578 1463 1669 1700 1227 1501 2243 586 728 1245 728 999 1303 2083 937 848 35781

# Households
GSTDM 2461 807 1103 2155 1295 1624 1117 1128 679 412 631 1023 515 1578 1457 1669 1700 1227 1501 2243 586 728 1245 711 1016 1303 2058 937 848 35757

% diff. 0.00 0.00 0.00 0.00 -0.08 -0.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.39 -1.67 0.00 1.21 0.00 0.00 0.07

93

Columbus

TABLE 16

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Columbus MPO Model Area (# TAZs = 89)

TAZ_ID
1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328

MPO 1690 3896 837 2007 3590
0 931 0 1212 1471 66 22 601 1609 5773 699 2797 5311 2231 1104 3269 753 2461 1191 2841 2354 6394 1426 4415 1910 3530 2176 462 2931

# Population
GSTDM 1690 3896 837 2007 3590 0 931 0 1212 1471 66 22 601 1609 5773 699 2797 5311 2231 1104 3269 753 2461 1191 2841 2354 6394 1426 4415 2317 3530 2176 462 2931

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -17.57 0.00 0.00 0.00 0.00

MPO 777 1352 349 882 1424
0 362
0 539 575 51
8 198 795 2412 224 1182 1946 926 405 1308 305 952 458 1323 991 2686 664 1751 810 1663 824 166 968

# Households
GSTDM 748 1350 351 882 1424 0 362 0 539 575 51 8 198 795 2412 224 1182 1946 926 434 1308 305 952 458 1323 991 2686 664 1751 810 1663 824 166 968

% diff. 3.88 0.15 -0.57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -6.68 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

94

TAZ_ID
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1363 2293 2294 2295 2296 2297

MPO 1511 5757
9 3604 9174 8921 3981 3298 5912 2377 2823 3268 2612 2045 2331 475 416 104 593 809 1406
0 1611 5223 2878 3827 3640 4073 4814 2338 8709 5995 1189 682 811 2163 2193 3335 5149

# Population
GSTDM 1511 5757 1843 3604 9174 8921 3981 3298 5912 2377 2823 3268 2612 2045 2402 695 492 104 690 809 2326 0 1611 5223 2878 3827 3640 4073 4814 2338 8709 5995 1189 682 809 2165 2193 3335 5149

% diff. 0.00 0.00 -99.51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.96 -31.65 -15.45 0.00 -14.06 0.00 -39.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.25 -0.09 0.00 0.00 0.00

MPO 569 2627
3 1302 3232 3290 1561 1376 2375 930 1497 1648 1016 970 901 173 170 66 573 506 641
0 620 2314 1285 1552 1372 1657 1725 927 3427 2440 436 235 324 892 885 1247 1904

# Households
GSTDM 569 2627 33 1302 3216 3290 1561 1376 2375 930 1497 1648 1016 970 901 173 170 66 250 506 641 0 620 2314 1285 1552 1372 1657 1725 927 3427 2440 436 235 323 893 885 1247 1904

% diff. 0.00 0.00 -90.91 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
129.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31 -0.11 0.00 0.00 0.00

95

TAZ_ID
2298 2299 2300 2301 3585 3586 3587 3588 3589 3590 3591 3592 3599 3600 3601 3602 Total

MPO 4085 4242 5444 4602 15805 6903 6672 7031 6946 3727 1873 3990 7623 20814 6621 10469 308863

# Population
GSTDM 4085 4242 5196 4850 15805 6903 6672 7031 6946 3727 1873 3990 7623 20814 6621 10469 312488

% diff. 0.00 0.00 4.77 -5.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.16

MPO 1439 1511 1937 1684 6880 2735 2451 2799 2486 1439 855 1584 2936 7630 2518 3849 122677

# Households
GSTDM 1439 1511 1844 1777 6880 2735 2451 2799 2486 1439 855 1584 2936 7630 2518 3849 122368

% diff. 0.00 0.00 5.04 -5.23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.25

Dalton

TABLE 17

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Dalton MPO Model Area (# TAZs = 65)

TAZ_ID
1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108

MPO 4438 1737 2266 2856 1199 1994 2259 2450 1716 5515 2921 772

# Population GSTDM 4438 1737 2266 2856 1333 1994 2259 2450 1716 5515 2923 770

% diff. 0.00 0.00 0.00 0.00 -10.05 0.00 0.00 0.00 0.00 0.00 -0.07 0.26

MPO 1567 605 697 1057 420 710 780 741 624 1984 918 266

# Households GSTDM 1581 605 697 1057 466 710 780 741 624 1984 919 265

% diff. -0.89 0.00 0.00 0.00 -9.87 0.00 0.00 0.00 0.00 0.00 -0.11 0.38

96

TAZ_ID
1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147

MPO 2438 920 2771 646 2846 1364 1107 1899 4100 1353 2545 2105 1459 2460 2075 3722 2086 85 62 4111 2115 1215 3934 2382 2255 1229 1747 2114 1340 1260 1101 1858 1017 2078 1521 1441 846 844 2004

# Population GSTDM 2438 920 2771 646 2846 1364 1107 1899 4100 1353 2545 2112 1459 2460 2075 3722 2088 85 111 4111 2114 1216 3934 2382 2255 1229 1747 2059 1340 1260 1101 1858 1017 2078 1514 1441 679 826 2004

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.33 0.00 0.00 0.00 0.00 -0.10 0.00 -44.14 0.00 0.05 -0.08 0.00 0.00 0.00 0.00 0.00 2.67 0.00 0.00 0.00 0.00 0.00 0.00 0.46 0.00 24.59 2.18 0.00

MPO 979 324 1041 243 1040 518 389 709 1483 677 970 643 537 981 777 1288 606 47 30 1199 693 351 1132 733 676 392 569 711 419 440 330 545 329 643 522 526 292 298 690

# Households GSTDM 979 324 1041 243 1040 518 389 709 1483 677 970 637 537 981 777 1287 606 47 30 1199 693 385 1132 733 676 392 569 698 419 440 330 545 329 643 520 526 229 292 690

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.94 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 -8.83 0.00 0.00 0.00 0.00 0.00 1.86 0.00 0.00 0.00 0.00 0.00 0.00 0.38 0.00 27.51 2.05 0.00

97

TAZ_ID
1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 Total

MPO 1227 1807 553 2212 6901 1843 200 2111 1541 6240 5087 4348 2939 2628 142215

# Population GSTDM 1227 1807 553 2212 6758 1843 199 2117 1536 6240 5087 4348 2939 2838 142227

% diff. 0.00 0.00 0.00 0.00 2.12 0.00 0.50 -0.28 0.33 0.00 0.00 0.00 0.00 -7.40 -0.01

MPO 465 675 219 736 2208 722 90 804 571 2324 1740 1584 1008 934 49221

# Households GSTDM 465 675 219 736 2162 722 89 807 569 2324 1740 1584 1008 1016 49260

% diff. 0.00 0.00 0.00 0.00 2.13 0.00 1.12 -0.37 0.35 0.00 0.00 0.00 0.00 -8.07 -0.08

Gainesville

TABLE 18

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Gainesville MPO Model Area (# TAZs = 113)

TAZ_ID
678 679 680 681 682 683 684 685 686 687 688 689 690 691

MPO 4734 544 2453 4312 3703 4483 1311 2096
0 2867 1922 2111 2588 2180

# Population GSTDM 4734 544 2453 4312 3703 4483 1311 2096 951 2964 1922 2111 2588 2180

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
-100.00 -3.27 0.00 0.00 0.00 0.00

MPO 1770 192 823 1366 1200 1428 460 799
0 1201 799 793 1014 868

# Households

GSTDM % diff.

1770

0.00

192

0.00

823

0.00

1366

0.00

1200

0.00

1428

0.00

460

0.00

799

0.00

373

-100.00

1201

0.00

799

0.00

793

0.00

1014

0.00

868

0.00

98

TAZ_ID
692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730

MPO 4280 1011 2224 1889 2919 790 325 1305 2270 3436 1378 6002 2184 1413 568 3690 551 1469 2590 333 2974 4832 228 2415 3879 1595 1201 606 1897 617 715 835 1142 646 2310 815 1630 1328 1923

# Population GSTDM 4280 1011 2224 1889 2919 790 325 1305 2270 3436 1378 6002 2184 1413 568 5017 551 1469 2590 333 2974 4832 228 2415 3879 1595 1201 606 1897 617 836 835 1142 646 2310 815 1630 1328 1917

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -26.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -14.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31

MPO 1508 381 770 691 829 232 145 569 876 1072 490 2030 750 485 192 1067 144 361 689 113 783 1273 80 668 1098 515 415 90 710 273 334 314 423 253 841 298 570 470 693

# Households

GSTDM % diff.

1508

0.00

381

0.00

770

0.00

691

0.00

829

0.00

232

0.00

145

0.00

569

0.00

876

0.00

1072

0.00

490

0.00

2030

0.00

750

0.00

485

0.00

192

0.00

1067

0.00

144

0.00

361

0.00

689

0.00

113

0.00

783

0.00

1273

0.00

80

0.00

668

0.00

1098

0.00

515

0.00

415

0.00

65

38.46

710

0.00

273

0.00

334

0.00

314

0.00

423

0.00

253

0.00

841

0.00

298

0.00

570

0.00

470

0.00

691

0.29

99

TAZ_ID
731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 1954 1955 1956 1957 1958

MPO 127 658 876 1100 248 470 827 284 1084 2521 1888 2296 3206 3746 1253
0 248 2477 2743 3827 1418 5578 2947 719 1356 3883 3409 1788 1603 2570 3535 4452 3914 2321 1982 961 1231 3399 4858

# Population GSTDM 127 658 876 1100 248 470 827 284 1084 2521 1888 2296 3206 3746 1253 28 274 2720 2743 3827 1418 5578 2947 719 1356 3883 3409 1788 1603 2570 3535 4452 3914 2321 1982 961 1231 3399 4858

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
-100.00 -9.49 -8.93 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MPO 53 255 309 389 99 172 290 114 421 821 761 839 1038 1457 555 0 57 604 611 848 505 1653 1312 207 562 1474 1226 663 557 1067 1250 1608 1430 912 678 377 400 1165 1643

# Households

GSTDM % diff.

53

0.00

255

0.00

309

0.00

389

0.00

99

0.00

172

0.00

290

0.00

114

0.00

421

0.00

821

0.00

761

0.00

839

0.00

1038

0.00

1457

0.00

555

0.00

16

-100.00

57

0.00

604

0.00

611

0.00

848

0.00

505

0.00

1653

0.00

1312

0.00

207

0.00

562

0.00

1474

0.00

1226

0.00

663

0.00

557

0.00

1067

0.00

1250

0.00

1608

0.00

1430

0.00

912

0.00

678

0.00

377

0.00

400

0.00

1165

0.00

1643

0.00

100

TAZ_ID
1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 2973 2974 2975 2976 2977 Total

MPO 614 4261 170 3760 1164 1603 2391 830 2796 2698 3681 5078 1413 1554 5073 2525 2791 1943 1489 1184 1036 237376

# Population GSTDM 614 4261 170 3760 1164 1603 2391 830 2796 2698 3681 5084 1413 1554 5073 2525 2791 1943 1489 1178 1036 240163

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.51 0.00 -1.16

MPO 214 1477 62 1312 475 650 895 314 971 990 1336 1759 635 523 1694 858 925 691 540 512 247 81670

# Households

GSTDM % diff.

214

0.00

1477

0.00

62

0.00

1312

0.00

475

0.00

650

0.00

895

0.00

314

0.00

971

0.00

990

0.00

1336

0.00

1762

-0.17

635

0.00

523

0.00

1694

0.00

858

0.00

925

0.00

691

0.00

540

0.00

509

0.59

247

0.00

82032

-0.44

Hinesville

TABLE 19

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Hinesville MPO Model Area (# TAZs = 44)

TAZ_ID
1452 1453 1454 1455 1456 1457 1458

MPO 752 382 4336 2748 2383 2856 1248

# Population GSTDM 752 382 4093 2750 2383 2856 1248

% diff. 0.00 0.00 5.94 -0.07 0.00 0.00 0.00

MPO 268 158 1383 943 840 1024 392

# Households GSTDM 268 158 1397 944 840 1024 392

% diff. 0.00 0.00 -1.00 -0.11 0.00 0.00 0.00

101

TAZ_ID
1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 Total

MPO 0 0 0 0 2
7171 0
3015 837 834 5265 3217 1151 463 782 2253 531 2300 10415 3846 1446 574 240 3187 1896 100 427 526 2028 3819 1574 473 319 432 249 1691 28 75796

# Population GSTDM 0 0 0 0 39 9382 0 3007 837 834 5265 3358 1012 463 782 2251 531 2300 10537 3846 1446 574 240 3187 1896 100 427 526 2028 3819 1574 473 319 432 249 1691 28 77917

% diff. 0.00 0.00 0.00 0.00 -94.87 -23.57 0.00 0.27 0.00 0.00 0.00 -4.20 13.74 0.00 0.00 0.09 0.00 0.00 -1.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.72

MPO 0 0 0 0 2
2102 0
1214 373 411 1915 1204 406 173 306 674 212 827 3617 1446 600 216 85 1133 733 41 145 198 652 1541 585 188 137 151 95 716 10 27116

# Households GSTDM 0 0 0 0 2 2102 0 1211 373 411 1915 1256 355 173 306 673 212 827 3667 1446 600 216 85 1133 733 41 145 198 652 1541 585 188 137 151 95 716 10 27178

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.25 0.00 0.00 0.00 -4.14 14.37 0.00 0.00 0.15 0.00 0.00 -1.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.23

102

Macon

TABLE 20

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Macon MPO Model Area (# TAZs = 77)

TAZ_ID
1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261

MPO 3976 1144 4371 3788 2094 1270 2168 3399 3160 1711 752 640 5569 1008 1035 1475 1264 2364 13495 3493 2122 1167 2703 3553 3638 1986 4804 3029 2266 1978 2327 6721 4215 2339

# Population GSTDM 2950 1089 4420 4229 2057 1265 1821 4143 3589 1865 784 631 5618 1273 1087 1685 2009 2177 13411 3533 2853 1561 2766 3506 3017 1698 4526 2843 2519 2090 2583 7619 4137 2196

% diff. 34.78 5.05 -1.11 -10.43 1.80 0.40 19.06 -17.96 -11.95 -8.26 -4.08 1.43 -0.87 -20.82 -4.78 -12.46 -37.08 8.59 0.63 -1.13 -25.62 -25.24 -2.28 1.34 20.58 16.96 6.14 6.54 -10.04 -5.36 -9.91 -11.79 1.89 6.51

MPO 1703 368 1538 1678 863 444 820 1188 1172 726 282 247 2282 346 413 572 490 792 4714 1354 978 431 1099 1354 1447 944 1939 1099 878 962 1112 3272 1929 1268

# Households GSTDM 1485 410 1664 1758 913 517 774 1559 1391 806 296 242 2103 488 436 626 396 785 4556 1296 1055 603 1048 1371 1230 807 1788 1042 918 1016 1167 3344 1981 1320

% diff. 14.68 -10.24 -7.57 -4.55 -5.48 -14.12 5.94 -23.80 -15.74 -9.93 -4.73 2.07 8.51 -29.10 -5.28 -8.63 23.74 0.89 3.47 4.48 -7.30 -28.52 4.87 -1.24 17.64 16.98 8.45 5.47 -4.36 -5.31 -4.71 -2.15 -2.62 -3.94

103

TAZ_ID
1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1768 1769 1770 1771 1772 1773

MPO 371 1175 2136 1547 1764 5487 2035 4802 2930 190 2203 895 880 192 0 465 1525 1706 2817 4351 2082 2832 1828 1778 2666 1644 1079 1347 935 946 718 2561 3798 447 1172 1966 2626 4606 952

# Population GSTDM 409 2156 3697 1401 1649 5097 2144 5376 3199 163 1773 711 874 257 4 375 1320 1849 2571 3872 2539 2455 1513 1845 2912 1836 1179 1424 1059 851 1941 2711 2397 683 891 1745 4125 3107 1291

% diff. -9.29 -45.50 -42.22 10.42 6.97 7.65 -5.08 -10.68 -8.41 16.56 24.25 25.88 0.69 -25.29 -100.00 24.00 15.53 -7.73 9.57 12.37 -18.00 15.36 20.82 -3.63 -8.45 -10.46 -8.48 -5.41 -11.71 11.16 -63.01 -5.53 58.45 -34.55 31.54 12.66 -36.34 48.25 -26.26

MPO 284 454 950 597 593 2072 763 1877 1077 78 868 303 366 95 0 165 585 559 994 1471 742 1211 610 876 1237 764 399 497 353 357 269 961 1423 165 432 725 932 1637 358

# Households GSTDM 248 526 918 517 590 1762 825 2093 1194 67 679 252 280 103 0 137 483 611 936 1427 901 1084 506 901 1251 813 448 517 407 312 729 1035 880 245 347 632 1394 1175 488

% diff. 14.52 -13.69 3.49 15.47 0.51 17.59 -7.52 -10.32 -9.80 16.42 27.84 20.24 30.71 -7.77 0.00 20.44 21.12 -8.51 6.20 3.08 -17.65 11.72 20.55 -2.77 -1.12 -6.03 -10.94 -3.87 -13.27 14.42 -63.10 -7.15 61.70 -32.65 24.50 14.72 -33.14 39.32 -26.64

104

TAZ_ID
1774 1775 Total

MPO 2698 2818 179994

# Population GSTDM 2997 2180 184128

% diff. -9.98 29.27 -2.25

MPO 1016 1062 70881

# Households GSTDM 1148 800 70852

% diff. -11.50 32.75 0.04

Rome

TABLE 21

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Rome MPO Model Area (# TAZs = 52)

TAZ_ID
949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974

MPO 1736 2574 2018 1555 1607 1013 591 398 1794 1226 2932 1674 2690 3537 4621 3585 1720 937 63 1185 1080 2319 2338 795 2896 1097

# Population GSTDM 1874 2589 2019 1613 1552 1013 591 398 1794 1256 2932 1674 2690 3537 4613 3585 1720 1053 63 1185 1080 2319 2338 1220 2896 1097

% diff. -7.36 -0.58 -0.05 -3.60 3.54 0.00 0.00 0.00 0.00 -2.39 0.00 0.00 0.00 0.00 0.17 0.00 0.00 -11.02 0.00 0.00 0.00 0.00 0.00 -34.84 0.00 0.00

MPO 708 1016 814 731 705 417 292 186 782 481 1194 609 1170 1684 2077 1654 688 385 26 539 586 1064 991 355 1140 457

# Households GSTDM 688 947 722 664 600 367 236 149 693 449 1122 499 926 1417 1785 1483 609 391 22 513 507 970 886 438 1066 407

% diff. 2.91 7.29 12.74 10.09 17.50 13.62 23.73 24.83 12.84 7.13 6.42 22.04 26.35 18.84 16.36 11.53 12.97 -1.53 18.18 5.07 15.58 9.69 11.85 -18.95 6.94 12.29

105

TAZ_ID
975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 Total

MPO 536 4472 638 1667 722 4091 3725 1870 7685 3956 11 1884 795 2830 511 1790 1804 67 445 1768 2024 280 526 1121 725 930 94854

# Population GSTDM 536 4472 632 1667 770 4091 4223 1870 7685 3956 11 1884 900 2830 511 1790 1804 67 445 1768 2024 280 405 1121 791 962 96196

% diff. 0.00 0.00 0.95 0.00 -6.23 0.00 -11.79 0.00 0.00 0.00 0.00 0.00 -11.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 29.88 0.00 -8.34 -3.33 -1.40

MPO 233 2009 409 960 270 1507 1414 801 3181 1515
4 233 282 1170 209 840 761 22 198 854 856 113 230 460 297 395 39974

# Households GSTDM 216 1616 353 866 241 1352 1491 759 2963 1282 4 214 300 1073 190 728 642 21 176 729 738 102 165 430 300 376 35883

% diff. 7.87 24.32 15.86 10.85 12.03 11.46 -5.16 5.53 7.36 18.17 0.00 8.88 -6.00 9.04 10.00 15.38 18.54 4.76 12.50 17.15 15.99 10.78 39.39 6.98 -1.00 5.05 11.40

106

Savannah

TABLE 22

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Savannah MPO Model Area (# TAZs = 147)

TAZ_ID
1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529

MPO 370 917 463 170 1381 3913 88 66 382 183 971 245 592 2984 3430 1603 1091 510 755 161 5116 4526 1113 717 626 229 683 894 79
0 17 0 398 1679

# Population GSTDM 370 917 463 170 1381 3913 88 66 382 183 971 245 592 2984 3514 1603 1091 510 755 161 5116 4526 1113 717 626 229 683 979 79 0 1638 0 398 1679

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.39 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -8.68 0.00 0.00 -98.96 0.00 0.00 0.00

MPO 131 352 187 64 591 1311 37 23 141 68 372 104 193 1022 1275 695 594 182 293 58 2120 1676 398 297 259 92 285 391 29 0 9 0 138 706

# Households GSTDM 131 352 187 64 591 1311 37 23 141 68 372 104 193 1022 1275 695 594 182 293 58 2120 1676 398 297 259 92 285 391 29 0 9 0 131 703

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.34 0.43

107

TAZ_ID
1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568

MPO 2693 1380 116 320 1303
8 750 497 1049 9698 761 222 2369 164 258 2089 1326 1647 953 2962 7827 2977 159 1127 11657 7550 3120 1594 4026 3717 2075 1465 5453 1787 3016 1173 975 2306 1335

# Population GSTDM 2693 1380 116 320 1303 8 750 2228 1049 9698 761 222 2369 164 258 2089 1326 1647 953 2962 7827 2977 159 1187 11756 7550 3120 1594 4026 3717 2075 1465 5453 1787 3016 1173 975 2306 1335

% diff. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -77.69 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -5.05 -0.84 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MPO 969 579 51 128 436 3 419 279 458 3699 265 91 870 75 116 756 481 734 408 1009 3110 1254 74 513 5203 3185 1299 694 1987 1602 1072 631 2215 880 1353 609 567 889 500

# Households GSTDM 963 579 51 128 436 3 417 279 458 3678 265 91 865 75 116 756 481 734 408 1009 3107 1254 74 532 4293 3185 1294 597 1710 1547 330 631 1902 880 1353 477 430 889 500

% diff. 0.62 0.00 0.00 0.00 0.00 0.00 0.48 0.00 0.00 0.57 0.00 0.00 0.58 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 -3.57 21.20 0.00 0.39 16.25 16.20 3.56
224.85 0.00 16.46 0.00 0.00 27.67 31.86 0.00 0.00

108

TAZ_ID
1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607

MPO 2358 1015 2104 4604 2361 3395 2380 2058 1722 217 1704 443 3160
5 831
2 0 1352 2051 937 1580 6650 4202 3187 1495 2 11 42 289 932 2230 7093 8294 1120 1605 0 3187 15138 2966

# Population GSTDM 2358 1015 2104 4604 2476 3395 2380 2058 1722 217 1704 701 3160 5 831 2 0 1352 2098 937 1777 6650 4252 3187 1495 2 11 121 289 957 2230 7093 8294 1120 1605 0 3306 15138 2966

% diff. 0.00 0.00 0.00 0.00 -4.64 0.00 0.00 0.00 0.00 0.00 0.00 -36.80 0.00 0.00 0.00 0.00 0.00 0.00 -2.24 0.00 -11.09 0.00 -1.18 0.00 0.00 0.00 0.00 -65.29 0.00 -2.61 0.00 0.00 0.00 0.00 0.00 0.00 -3.60 0.00 0.00

MPO 963 504 1232 2130 886 1247 886 796 592 90 663 161 1381 3 305 2 0 481 1501 572 1137 3827 2006 1282 597 2 5 27 148 333 884 2726 3217 482 661 0 1513 6385 1144

# Households GSTDM 963 493 907 2130 871 1243 886 796 592 83 663 161 1050 3 305 0 0 481 270 201 709 3622 1919 1282 597 0 5 15 148 333 884 2716 3214 482 661 0 1513 6378 1144

% diff. 0.00 2.23 35.83 0.00 1.72 0.32 0.00 0.00 0.00 8.43 0.00 0.00 31.52 0.00 0.00 0.00 0.00
455.93 184.58 60.37
5.66 4.53 0.00 0.00 0.00 0.00 80.00 0.00 0.00 0.00 0.37 0.09 0.00 0.00 0.00 0.00 0.11 0.00

109

TAZ_ID
1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1776 1777 1778 1779 1780 1782 1783 1784 1785 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2950 2951 2952 2953 2954 Total

MPO 2825 5796 4586 3666 2226 1439 1005 2742 4523 8384
0 624 803 899 2021 3669
0 0 1858 9485 1151 2054 3474 8690 5242 4476 5511 4500 2647 5277 6883 2269 7117 2111 1722 342653

# Population GSTDM 2825 5922 4872 3666 2226 2225 1005 2778 4523 7598 0 624 803 899 2020 3670 0 0 1858 9425 1125 2061 3500 8690 5235 4476 5511 4490 2647 5277 6883 2269 7127 2111 1722 347611

% diff. 0.00 -2.13 -5.87 0.00 0.00 -35.33 0.00 -1.30 0.00 10.34 0.00 0.00 0.00 0.00 0.05 -0.03 0.00 0.00 0.00 0.64 2.31 -0.34 -0.74 0.00 0.13 0.00 0.00 0.22 0.00 0.00 0.00 0.00 -0.14 0.00 0.00 -1.43

MPO 1301 3583 1716 1398 821 588 432 1110 1766 3929
0 238 296 350 742 1335 0 0 666 3290 418 775 1191 2769 1864 1480 1982 1620 972 1821 2458 753 2471 729 610 139801

# Households GSTDM 1301 1497 1716 1398 821 923 392 1110 1766 3553 0 238 296 350 741 1336 0 0 666 3271 409 778 1200 2769 1861 1480 1982 1615 972 1821 2458 753 2476 729 610 131868

% diff. 0.00
139.35 0.00 0.00 0.00 -36.29 10.20 0.00 0.00 10.58 0.00 0.00 0.00 0.00 0.13 -0.07 0.00 0.00 0.00 0.58 2.20 -0.39 -0.75 0.00 0.16 0.00 0.00 0.31 0.00 0.00 0.00 0.00 -0.20 0.00 0.00 6.02

110

Valdosta

TABLE 23

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Valdosta MPO Model Area (# TAZs = 51)

TAZ ID
1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713

MPO 733 1554 2290 1558 4318 1949 981 4285 3352 2581 2305 1393 3479 4925 1008 421 4322 7343 673 9157 3399 3995 3515 2283 4818 3339 2782 736 677 547 92 1127 2173 1149

# Population GSTDM 733 3015 2181 1647 4318 1949 981 4341 3352 2581 2305 1120 3543 4925 983 421 4322 7594 673 9157 3737 3995 3515 2283 4818 3339 2782 736 677 547 92 1922 2173 1149

% diff. 0.00 -48.46 5.00 -5.40 0.00 0.00 0.00 -1.29 0.00 0.00 0.00 24.38 -1.81 0.00 2.54 0.00 0.00 -3.31 0.00 0.00 -9.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -41.36 0.00 0.00

MPO 268 571 858 580 1492 653 337 1524 1118 1044 1131 1049 1180 1834 388 167 1628 2709 244 3457 1523 1846 2868 1014 2137 1756 1024 373 273 229 32 411 817 415

# Households GSTDM 268 571 822 605 1491 653 335 1533 1118 1044 1130 188 1206 1834 379 167 1628 2705 242 3454 1523 1846 641 1011 2128 1227 1023 373 272 229 32 411 817 415

% diff. 0.00 0.00 4.38 -4.13 0.07 0.00 0.60 -0.59 0.00 0.00 0.09
457.98 -2.16 0.00 2.37 0.00 0.00 0.15 0.83 0.09 0.00 0.00 347.43 0.30 0.42 43.11 0.10 0.00 0.37 0.00 0.00 0.00 0.00 0.00

111

TAZ ID
1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 Total

MPO 1359 640 1234 1353 2518 441 1200 1528 2345 2305 1691 688 106561

# Population GSTDM 1360 640 1193 1393 2518 441 1200 1481 2484 2175 1735 707 109233

% diff. -0.07 0.00 3.44 -2.87 0.00 0.00 0.00 3.17 -5.60 5.98 -2.54 -2.69 -2.45

MPO 463 205 443 539 852 166 485 619 921 859 609 259 43370

# Households GSTDM 464 205 429 552 852 166 485 603 962 819 624 265 39747

% diff. -0.22 0.00 3.26 -2.36 0.00 0.00 0.00 2.65 -4.26 4.88 -2.40 -2.26 9.12

Warner Robins

TABLE 24

Comparison of Socioeconomic Data between the GSTDM and MPO Models, by GSTDM TAZ, in the Warner Robins MPO Model Area (# TAZs = 55)

TAZ_ID
1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382

MPO 2414 4586 4099 5812 3255 4149 983 1869 6673 2479 1786 350 5548 3445

# Population GSTDM 2469 4586 4099 5812 3255 4149 983 1869 6673 2479 1786 350 5599 3445

% diff. -2.23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.91 0.00

MPO 920 1641 1730 2249 1433 1595 368 734 2480 957 741 155 2147 1222

# Households GSTDM 938 1641 1730 2249 1433 1595 368 734 2480 957 741 155 2167 1222

% diff. -1.92 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.92 0.00

112

TAZ_ID
1383 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 2162 2163 2164 2165 2166 2167 2168 2169 2170

MPO 1216 1620 3481 2205 4621 8139 3224 1490 3175 1778 6164 3918 8198 14133 7023 843 1493 2324 1701 613 1256 711 2777 935 1880 3357 985 1259 884 938 2429 349 3329 1297 4267 881 1663 2046 2904

# Population GSTDM 178 1620 3451 2217 4642 8139 3224 1490 2211 2742 6113 3918 8200 14133 7023 843 1493 2324 1701 613 1291 711 2755 967 1880 3360 979 1259 884 938 2429 347 3247 1283 4267 880 1664 2046 2904

% diff. 583.15 0.00 0.87 -0.54 -0.45 0.00 0.00 0.00 43.60 -35.16 0.83 0.00 -0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.71 0.00 0.61 -3.31 0.00 -0.09 0.61 0.00 0.00 0.00 0.00 0.58 2.53 1.09 0.00 0.11 -0.06 0.00 0.00

MPO 249 624 1126 824 1585 3007 1260 523 1521 761 2624 1621 3106 4929 2444 322 595 1000 657 265 577 268 1070 338 634 1202 369 474 315 353 940 157 1322 500 1684 359 601 701 1072

# Households GSTDM 64 624 1114 827 1595 3007 1260 523 1151 1131 2604 1621 3107 4929 2444 322 595 1000 657 265 590 268 1059 350 634 1203 367 474 315 353 940 156 1294 495 1652 358 602 701 1072

% diff. 289.06 0.00 1.08 -0.36 -0.63 0.00 0.00 0.00 32.15 -32.71 0.77 0.00 -0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.20 0.00 1.04 -3.43 0.00 -0.08 0.54 0.00 0.00 0.00 0.00 0.64 2.16 1.01 1.94 0.28 -0.17 0.00 0.00

113

TAZ_ID
2171 2172 Total

MPO 2286 6386 167626

# Population GSTDM 2286 6351 166557

% diff. 0.00 0.55 0.64

MPO 941 1763 63055

# Households GSTDM 941 1750 62824

% diff. 0.00 0.74 0.37

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Appendix C Projection Issues
Figure 29 illustrates the projection issues as detected in the case of the Cartersville MPO model network. As shown in the figure, the geographic location of the Cartersville MPO network (blue) is mistakenly projected far away from its intended location relative to the statewide TAZs (green). Similar projection issues were found in the Valdosta and Columbus MPO network.
The projection issue was handled with the following approach: Step 1. Prepare the MPO network file (usually in CUBE binary .NET format) and
export it to a GIS shapefile format. The Citilabs Cube software can handle this step. Step 2. Import the statewide TAZs for the corresponding MPO model region, e.g., Cartersville in this example, into RStudio. Step 3. Import the GIS shapefile of the MPO network into RStudio. Step 4. Ascertain the MPO TAZs projection system. Step 5. Assign the MPO TAZs projection system to the MPO network.
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Statewide TAZs
Cartersville MPO network `
FIGURE 29 Projection Issue as Detected in the Case of Cartersville MPO Network This process can be handled using the following script. Note that the process is not particularly lengthy.
1. net_shp < - readOGR(dsn = "(Fill with the file's pathname)", layer = "(Fill with layer name)") #Open MPO network shapefile.
2. net_shp < - net_shp[!(net_shp$FTYPE == 32), ] #Delete centroid connectors, which are represented as network segments with attribute value 32 in the FTYPE column
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3. taz_shp < -readOGR(dsn = ".", layer = ".") #Open MPO TAZs derived from the statewide TAZs 4. taz_shp$TAZ_SEQ < - seq(1, nrow(taz_shp@ data)) #[Optional] Create new column to number
the TAZs sequentially 5. summary(taz_shp) #Use the summary command to obtain the MPO TAZ projection system 6. proj4string(net_shp) < -CRS(".") #Assign the MPO TAZ projection system to the MPO network 7. writeOGR(net_shp, dsn = "(Fill with the destination pathname)", layer = "(Fll with intended new
file name)", driver = "ESRI Shapefile") #Write a new MPO network shapefile with the correct projection system as output of the process
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