Integrating Land Use and Transportation in a GIS Visualization Tool

Final Report

Submitted by

Fang Zhao, Ph.D., P.E.Min-Tang Li, Ph.D.

Lehman Center for Transportation ResearchFlorida International University

10555 W Flagler StreetMiami, Florida 33174Phone: (305) 348-3821Fax: (305) 348-2802

Email: fang@eng.fiu.edu

Jill Strube, Research AssociateMetropolitan Center

Florida International University150 SE 2nd Ave., Suite 1201

Miami, Florida, 33131Phone: (305) 349-1251Fax: (305) 349-1271

Email: JLStrube@aol.com

Francisco Ordaz, Research AssistantSchool of Urban PlanningFlorida Atlantic UniversityFort Lauderdale, Florida

July 2001

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ACKNOWLEDGMENTS

Many people have contributed their ideas and helped improve the product of this project.

Jo Penrose, who was the project manager at the FDOT District 6 before moving to Atlanta, wasinstrumental to shaping the concepts and framework of VOLUTI. David Korros became the projectmanager after Jo Penrose and guided the project to its successful completion.

Robert Shwartz with the Department of Economic Development of the City of Miami provided dataon Overtown and some important information about the historical and current economic conditionsin Overtown. Ronald Finegold of the Miami-Dade County Information Technology Division assistedin the acquisition of land use and property tax databases, and answered numerous questions aboutthe data. FDOT District 6 Planning Office provided the digital orthophotos and databases of thestate highway system in the district.

Frank Baron and Susan Schreiber of Miami-Dade Metropolitan Organization, David Dahlstrom ofthe South Florida Planning Council, and Fabian Cevallos of Broward Transit took out time fromtheir busy schedule to review the software developed for this project and provided many usefulsuggestions, most of which have been adopted.

Dr. Sydney Wong, the former Associate Director of the FAU/FIU Joint Center of EnvironmentalProblems and now an Associate Professor at the University of Pennsylvania, offered insights invarious aspects of Overtown.

Lee-Fang Chow, Research Associate with the Lehman Center for Transportation at FIU, with thehelp of Soon Chung and Xin Li, research assistants from the Lehman Center for TransportationResearch, coded most of the new GIS programs.

Contributions from all the above individuals are appreciated, as well as those from many others whoare not mentioned here but whose assistance is also acknowledged.

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TABLE OF CONTENTS

ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. RESEARCH OBJECTIVES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

3. BACKGROUND - THE OVERTOWN STUDY AREA . . . . . . . . . . . . . . . . . . . . . . . . . 3

4. LITERATURE REVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94.1 Sustainable Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94.2 Accessibility to Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114.3 Effect of Urban Forms on Travel Mode Choice . . . . . . . . . . . . . . . . . . . . . . . . . 134.4 Neighborhood and Urban Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194.5 Florida Sustainable Communities Network (FSCN) INDEX Software . . . . . . . 214.6 Land Use Planning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.7 Visualization Programs for Land Use and Transportation . . . . . . . . . . . . . . . . . 24

5. USER FEEDBACK OF THE PROTOTYPE VOLUTI . . . . . . . . . . . . . . . . . . . . . . . . . 28

6. DATA COLLECTION AND PROCESSING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306.1 Property Tax Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306.2 Parcel GIS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316.3 Zoning Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316.4 Land Use Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326.5 Employment data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336.6 Water and Sewer Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336.7 Public Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336.8 Transportation Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336.9 Traffic Analysis Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356.10 Photographs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

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6.11 Aerial Photos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356.12 Demographic and Socioeconomic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376.13 Environmental Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

7. DEVELOPMENT OF LAND USE INDICATORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397.1 Land Use Mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397.2 Job/Housing Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407.3 Average Parcel Size by TAZ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407.4 Open Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407.5 Land Use Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407.6 Changes in Population, Employment, and Dwelling Units by TAZs . . . . . . . . . 417.7 Tax Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

8. ACCESSIBILITY AND MOBILITY EVALUATION . . . . . . . . . . . . . . . . . . . . . . . . . . 438.1 Regional Accessibility by Highway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438.2 Regional Accessibility by Transit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448.3 Local Accessibility to Essential Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448.4 Contours of Highway Travel Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458.5 Contours of Transit Travel Time by Transit Modes . . . . . . . . . . . . . . . . . . . . . . 458.6 Shortest Transit Travel Time Contour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458.7 Transit Transfers Required . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468.8 Difference of Transit and Highway Travel Time . . . . . . . . . . . . . . . . . . . . . . . . 46

9. DEVELOPMENT OF LAND USE SCENARIOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489.1 Development Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489.2 Population Forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

9.2.1 Low, Medium, and High Projection Series . . . . . . . . . . . . . . . . . . . . . . . 499.2.2 Average Household Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

9.3 Vacant Land . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519.4 Jobs and Commercial Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539.5 Recreation and Open Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

10. ASSESSMENT OF IMPACT OF LAND USE CHANGE AND TRANSPORTATION PROJECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5610.1 Overview of Site Impact Analysis in VOLUTI . . . . . . . . . . . . . . . . . . . . . . . . . . 5610.2 Land Development Types and Intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5810.3 Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6010.4 Background Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6110.5 Trip Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6310.6 Estimation of ITE Vehicle Trips . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6410.7 Examination of Land Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6510.8 Trips Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6710.9 Creation of ZDATA3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

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10.10 Selected Zone Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7110.11 Results Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

11. VOLUTI GRAPHIC USER INTERFACE DESIGN . . . . . . . . . . . . . . . . . . . . . . . . . . . 7411.1 Top Level Graphic User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7411.2 Land Use Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7611.3 Environment Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8311.4 Socioeconomic Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8411.5 Transportation Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8711.6 Accessibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8811.7 Site Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9311.8 Travel Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

12. CONCLUSIONS AND RECOMMENDATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

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LIST OF TABLES

Table 4.1 Summary of Accessibility Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Table 4.2 Initial Indicators of FSCN INDEX Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Table 4.3 Software Packages for 3D Modeling and Visualization

(from McGaughey 1997) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Table 9.1 Average Household Size by Census Tract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50Table 9.2 Change in Dwelling Unit Demand in Tract 30.01 . . . . . . . . . . . . . . . . . . . . . . . . 50Table 9.3 Change in Dwelling Unit Demand in Tract 31.00 . . . . . . . . . . . . . . . . . . . . . . . . 50Table 9.4 Change in Dwelling Unit Demand in Tract 34.00 . . . . . . . . . . . . . . . . . . . . . . . . 51Table 9.5 Change in Dwelling Unit Demand in Tract 36.01 . . . . . . . . . . . . . . . . . . . . . . . . 51Table 9.6 Maximum Capacity of Existing Vacant Lands and Future

Demand for Dwelling Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Table 9.7 Projection of Jobs by Census Tract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Table 9.8 Projected Commercial Development in Sq-Feet . . . . . . . . . . . . . . . . . . . . . . . . . 54Table 9.9 Recreation and Open Space by Census Tract . . . . . . . . . . . . . . . . . . . . . . . . . . . 54Table 10.1 Codes, Types and Independent Variable Numbers for New Land Uses . . . . . . . 59Table 10.2 Variable Lookup Table for New Lane Uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Table 10.3 Socioeconomic Variables Used to Identify Zones with the Same Land Use . . . 66Table 11.1 Zoning Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

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LIST OF FIGURES

Figure 3.1 Miami-Dade County and Overtown Boundary . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Figure 4.1 CAD Drawing Overlay on Photographs (from Jha and McCall 2001) . . . . . . . . 26Figure 6.1 Detail of a Digital Orthophoto Quarter Quadrangle . . . . . . . . . . . . . . . . . . . . . . 36Figure 6.2 One-Meter Color Infrared Digital Orthophoto from the USGS . . . . . . . . . . . . . . 37Figure 6.3 One-Foot Digital Orthophoto from Miami-Dade County . . . . . . . . . . . . . . . . . . 37Figure 7.1 Dialog Box for Displaying Land Use Change . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Figure 7.2 Change in Single Family Land Use (1994 - 1998) . . . . . . . . . . . . . . . . . . . . . . . 41Figure 7.3 Change in Single Family Dwelling Units (1990 - 1999) . . . . . . . . . . . . . . . . . . . 41Figure 8.1 Dialog Box for Comparison of Transit and Highway Travel Time . . . . . . . . . . . 46Figure 8.2 Transit-Highway Travel Time Difference in Peak Hours with

Penalties Applied to Transit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Figure 10.1 Interactions between VOLUTI and FSUTMS . . . . . . . . . . . . . . . . . . . . . . . . . . . 56Figure 10.2 Structure of a Land Use Scenario with Three TAZs . . . . . . . . . . . . . . . . . . . . . . 57Figure 10.3 Site Impact Analysis Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Figure 10.4 Initialization Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61Figure 10.5 Building Trip Table Control File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63Figure 10.6 Trip Generation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64Figure 10.7 Procedure for Estimation of ITE Vehicle Trip . . . . . . . . . . . . . . . . . . . . . . . . . . 65Figure 10.8 Procedure for Examining Land Use Mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66Figure 10.9 Trip Conversion Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Figure 10.10 Dialog Box for Editing Number of Trips . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69Figure 10.11 Procedure for Creating ZDATA3 File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70Figure 10.12 Sample ZDATA3 File for New Development . . . . . . . . . . . . . . . . . . . . . . . . . . . 71Figure 10.13 Change in Traffic Volume for the Low Development Scenario

(Scenario 101) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72Figure 10.14 Change in Volume/Capacity for the Low Development Scenario

(Scenario 101) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73Figure 11.1 Top-Level Menu in VOLUTI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Figure 11.2 The View Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Figure 11.3 Theme Manager Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75Figure 11.4 Land Use Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76Figure 11.5 Land Use Composition and Tax Base Make–Up . . . . . . . . . . . . . . . . . . . . . . . . . 78Figure 11.6 Commercial, Office, and Industrial Building Stocks . . . . . . . . . . . . . . . . . . . . . . 78Figure 11.7 Land Use Mix in Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79Figure 11.8 Job/Housing Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79Figure 11.9 Land Use Change Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

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Figure 11.10 Multifamily Land Use Change between 1994 and 1998 . . . . . . . . . . . . . . . . . . . 79Figure 11.11 ZDATA Change Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79Figure 11.12 Display of Sales Price History of One Property . . . . . . . . . . . . . . . . . . . . . . . . . 80Figure 11.13 Assessed Values of One Property . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80Figure 11.14 Dialog Box for Choosing the Type of Properties . . . . . . . . . . . . . . . . . . . . . . . . 81Figure 11.15 Average Assessed Values in a Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81Figure 11.16 Selected Public Facilities with a 2-Mile Radius . . . . . . . . . . . . . . . . . . . . . . . . . 82Figure 11.17 Water and Sewer Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82Figure 11.18 Environment Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Figure 11.19 Flood Zone Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Figure 11.20 Public Well Field Protection Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84Figure 11.21 Socioeconomic and Demographic Data Menu . . . . . . . . . . . . . . . . . . . . . . . . . . 84Figure 11.22 Selection of Roadway Segments for Buffer Analysis . . . . . . . . . . . . . . . . . . . . . 85Figure 11.23 Selecting a Variable for Buffer Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86Figure 11.24 Entering Buffer Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86Figure 11.25 Buffer Analysis Result Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86Figure 11.26 Buffer Analysis Result as a Distribution Map . . . . . . . . . . . . . . . . . . . . . . . . . . . 87Figure 11.27 Transportation Facility Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87Figure 11.28 Selecting a Video Clip Demonstrating LOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87Figure 11.29 Accessibility Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Figure 11.30 Congested Highway Travel Time Contours in Minutes . . . . . . . . . . . . . . . . . . . 88Figure 11.31 Transit Travel Time Contours (All Modes with Penalties) . . . . . . . . . . . . . . . . . 89Figure 11.32 Difference between Transit and Highway Travel Time . . . . . . . . . . . . . . . . . . . 89Figure 11.33 Transit Transfers Needed to Travel between One Zone to All Other Zones . . . . 90Figure 11.34 Regional Accessibility to Employment Opportunities by Car . . . . . . . . . . . . . . . 90Figure 11.35 Regional Accessibility to Employment Opportunities by Transit . . . . . . . . . . . . 91Figure 11.36 Local Accessibility Index for Miami-Dade County . . . . . . . . . . . . . . . . . . . . . . 91Figure 11.37 Local Accessibility Index for the Overtown Area . . . . . . . . . . . . . . . . . . . . . . . . 92Figure 11.38 Site Impact Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Figure 11.39 Land Use Scenario Input Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Figure 11.40 Adding Zonal Centroid and Connectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95Figure 11.41 Selecting a Scenario to Edit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95Figure 11.42 Deleting a Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95Figure 11.43 Traffic Impact Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96Figure 11.44 Traffic Volume Increase on Network Links Due to

Development for the Low Development Scenario . . . . . . . . . . . . . . . . . . . . . . 96Figure 11.45 V/C Ratio Increase and V/C Ratio of Network Links

Due to the Low Development Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96Figure 12.1 Complexity of Functional Linkage in Urban Systems Dynamics

(Southworth, 1995) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

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EXECUTIVE SUMMARY

Introduction

The Florida Atlantic University/Florida International University Joint Center for Environmental &Urban Problems completed a project under the management of the Florida Department ofTransportation, District VI called Transportation/Land Use Visualization Project in 1999 (York etal. 1999). The study investigated the best practices in integrating land use and transportationplanning in Florida through a survey and analysis of the survey results. The survey results indicatedthat while most transportation and land use planning organizations recognized the importance oflinking both planning fields, few had been able to incorporate this link into their practices. The studypointed out that visualization was a useful tool for communicating with communities to convincethe public of the benefits of some of the changes. As part of the project, a prototype program namedVOLUTI 1.0 (Visualization Of Land Use and Transportation Interactions) was developed, whichoffered a geographic information system (GIS) environment that supports visualization of land use,demographic, socioeconomic, and transportation data.

This report describes an effort to expand the prototype VOLUTI program into an integratedGIS-based tool that includes more land use and accessibility measurements, and additional functionsrelated to assessment of impacts of land use developments and transportation projects. Theimprovements involved incorporation of additional data sources, development of land use andaccessibility indicators, development of land use scenarios, and a stronger linkage between VOLUTIand FSUTMS (Florida Standard Urban Travel Model Structure), the standard travel demand modelin Florida. The tool incorporates a variety of databases, multimedia imaging, travel demand models,and useful evaluation methods to support visualization of land use and transportation information,and evaluation of land use and transportation interaction. Overtown, one of the Miami-Dade CountyEmpowerment Zone neighborhoods, was chosen for the project for demonstration purposes.

Literature Review

Sustainable Developments

Slowly but gradually, the concept of sustainable developments is being accepted in many U.S. cities.Current sustainable development policies are concerned with economy, equity, and environment,combining economic development with environmental and social policy to promote longer-termprospects for economic growth while at the same time protecting natural resources and environment(Colgan 1997). As part of the sustainable development concept, community design principles, suchas those relating to the size of the overall community, housing, jobs, services and activities, include

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guidelines relating to walkability, density, and diversity. Public space, open space, jobs-housingbalance in number and variety, connectivity, and efficient and practical use of geography and passivesolar energy are highly regarded concepts (Center for Livable Communities 1999).

Public participation is one of the cornerstones of sustainability theory. Public participation in thedecision-making process provides the foundation for implementing policies and developingstrategies that promote sustainable communities. This requires that government agencies providethe public adequate, accessible, and timely information and requires understanding and respect fordiffering social and economic views, values, traditions, and aspirations. In this regard, VOLUTI willfacilitate public involvement by providing information on land use and transportation in aninnovative manner.

Accessibility to Opportunities

Accessibility has been recognized as one of the most important factors that affect both land use andtravel behavior. Many definitions and measures of accessibility exist, which Richardson and Young(1982) classified into a spectrum of accessibility measures such as modal accessibility, legalaccessibility, temporal accessibility, relative accessibility, and integrated accessibility.

Of interest to this project is a gravity type accessibility measure described in Kockelman (1997). Theaccessibility index was defined as the sum of all attractions (e.g. employment) weighted by frictionterms that reflect the ease of travel between a location and activity centers. Zonal attractiveness maybe measured by total employment or commercial and service employment. Another accessibilitymeasure is opportunities related to essential household daily activities such as shopping at groceryand drug stores. The proximity of such opportunities to residential neighborhoods reduce the needfor travel by automobiles and promote walk and bicycle trips.

Effect of Urban Forms on Travel Mode Choice

The need to understand how urban forms may affect travel behavior has taken on an urgency due torecent policy initiatives at the federal, state, and local levels to look for ways to improve mobilityand reduce congestion without building new highways. These policy initiatives are motivated by theIntermodal Surface Transportation Efficiency Act of 1991 (ISTEA), the Transportation Equity Actfor the 21st Century (TEA-21), the Clean Air Act Amendments of 1990 (CAAA), rising publicconcerns about petroleum consumption in the U.S. and global warming, and political pressure toreduce fuel consumption. In particular, TEA-21 initiated a new sustainable development pilotprogram to help state and local governments plan environmentally-friendly development, includingreducing vehicle miles traveled (VMT). One of the approaches to reduce VMT is to change travelbehavior via policies such as taxation, pricing, and land use planning.

Research evidences have suggested that a significant correlation between transit use and densityexists provided that transit services are adequate and major activity centers are accessible via transit(Pushkarev and Zupan 1977, Newman and Kenworth 1989). Frank and Pivo (1994) showed through

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regression analyses that urban-form variables did contribute to mode choice, with positive impacton transit use and walk and negative impact on SOV use, respectively. Additionally, land use mixseemed to better explain the choice of walk mode. The authors determined that significant shiftsfrom SOV to transit use and walking occur between an employment density of 20 and 75 employeesper acre and again when density exceeded 125.

Kockelman supported Pushkarev’s and Zupan’s conclusion in a study on the relative effect ofpopulation density and income on modal split (Kockelman, 1995). She showed that density (or otherfactors proxied by density such as land prices, parking fees, transit service frequency, and congestedroadways), not income, was the influential factor on modal split. In another study (Kockelman1997), Kockelman investigated the link between urban form and travel behaviors and concluded inthat accessibility, land use mixing, and land use balance were all statistically significant andinfluential to travel behaviors, including mode choice. It is concluded that accessibility is a far betterpredictor of vehicle kilometers traveled (VKT) than density.

A similar study by Sun et al. (1998) using the 1994 Portland Travel Survey data, density, land usemix, accessibility, annual household income, household size, dwelling type, number of phone linesin a household, presence of a car phone, auto ownership, home ownership, year in current residence,number of activities, and proximity to light rail are analyzed to determine their impact on householdtrip rates and VMT. Regression analysis showed that density and land use balance make littledifference in the number of daily trips but has a significant impact on house VMT. High density andhigh entropy both contribute to a reduction of VMT.

In a study of Miami-Dade County in Florida, Messenger and Ewing (1996) decided that the densityneeded to support a 25-minute bus headway was 8.4 dwelling units per acre (1.4 higher than thatproposed by Pushkarev and Zupan) at the transit operator’s minimum productivity and 19.4 dwellingunits per acre at the system wide average productivity. Bus mode share at trip origins is primarilya function of low automobile ownership, and secondarily of job-housing balance and transit servicelevel, although job-housing balance has a small effect. Street configuration is found to have noapparent effect on transit use. Bus mode share at trip destinations is primarily a function of parkingcost, overall density, and access to downtown.

In an attempt to determine if land use truly has a causal relationship with travel behavior or whetherit is other socioeconomic, demographic, and transportation supply characteristics, which are alsoassociated with land use, that are the real determinants of travel behavior, Kitamura et al. (1997)conducted a household survey (including a three-day travel diary) in five neighborhoods in the SanFrancisco Bay Area (SFBA) and investigated the travel behavior variables and a wide array ofvariables that are objectively or subjectively measured. Results of the analyses indicated that thevariables had weak power to explain mode choice. Nonetheless, these results led to the conclusionsthat have been generally agreed upon such as parking availability negatively impact total number ofperson trips, and high density, proximity to parks and bus stops, access to rail transit stations, andpresence of sidewalks encourage non-motorized travel. Furthermore, attitudes (pro-environment,pro-transit, suburbanite, automotive mobility, time pressure, urban villager, TCM, and workaholic)

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were determined to have more significant impact on travel behavior than socioeconomic and landuse characteristics, with land use characteristics being the weakest predictors.

The many facets of the relationship between urban form and transit were re-examined, explained,evaluated, and documented in a TCRP project (Seskin 1996), which attempts to answer the questionsof how urban form influences the demand for light rail and commuter rail transit and how transitinfluences land uses. Urban structure, employment and residential densities, land use mix and urbandesign were found to influence transit use. However, although land use mix and urban design wassignificant in explaining transit use, individual land use and design was not. Also, density is morepowerful than land use mix and urban design in explaining transit use. On the other hand, theinfluences of transit on urban form were described by using the following four factors: propertyvalue, intensity of development, urban structure, and timing of development.

Neighborhood and Urban Design

Neotraditional neighborhoods are characterized by a closely spaced street grid, high density, andlocation often near street car tracks. Such neighborhoods are often older and built before the end ofthe World War II. There has been much debate as whether urban design has any impact on transituse. Some argue that neotraditional neighborhood design encourages walking and transit use, whileothers disagree. Many studies have been conducted to determine the effect of urban design variables.

Handy (1992) studied shopping trips in the San Francisco Bay Area based on regional and localaccessibility indices and found that two to four more bicycle and walk trips were made by residentsin two areas that closely resemble neotraditional neighborhood than by those living in areas that areautomobile oriented. She did not conclude if these trips by non-motorized modes actually replacesome of the automobile trips or the neotraditional neighborhood simply encouraged more walk andbicycle trips.

In a study of travel characteristics comparison using data from San Francisco Bay Area and LosAngeles, Cervero carefully paired “transit neighborhoods” and “auto neighborhoods” by a set ofselection criteria (Cervero, 1994). The “transit neighborhoods” are defined as initially built alongstreet car lines or a rail station, primary grid street network, and built before 1945. The “autoneighborhoods” are those not designed for transit and have no transit services, primary random streetpatterns (over 50% of intersections being “T” intersections or cul-de-sac), and built after 1945. Acomparison of the SFBA paired neighborhoods revealed that while other demographic characteristicsof the neighborhood pairs do not differ significantly, most auto neighborhoods have a higher autoownership, produce much more drive-alone trips, have a lower transit use, and have much lowerwalk trip rates than transit neighborhoods, the latter being especially obvious. On average, transitneighborhoods generate around 70 percent more transit trips and 120 pedestrian/bicycle trips. Thismay be partially contributed to the fact that transit neighborhoods tend to have better transit servicesupplies (measured by daily VMT per acre). By comparison, the transit neighborhoods in LosAngeles do not demonstrate the same significant amount of transit use or reduction of singleoccupancy driving. Cervero contributed this phenomenon to the overall strong auto orientation in

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Los Angeles and believed that the positive effects of transit neighborhoods in such an environmentare limited.

Using data of the entire Los Angeles area, Cervero also regressed the percent of transit trips againstvariables including gross residential density (households per acre), natural logarithm of householdincome, neighborhood type (auto or transit), and density interaction (product of residential densityand neighborhood type). According to the model, all variables are significant. In Los Angeles,everything else held constant, transit neighborhoods will generate 1.4 percent transit trips per every1,000 households while those in SFBA will generate 5.1 percent transit trips. Another conclusionwas that in Los Angeles, density does more than neighborhood type in increasing transit use.Increasing density by one dwelling unit per acre will increase transit trips by two to four percent.The density-neighborhood type interaction term has a stronger effect in the SFBA than in LosAngeles. Work trips by transit averaged 8 percent more if density was 10 units per acre and 13.5percent more when density was 30 units per acre. What is not controlled for, but may influence themode choice, is congestion.

The inconclusive effects of various urban form variables on travel behaviors, particularly onreducing automobile dependency, were supported by Clifton and Handy (1998) in a study of sixAustin, Texas neighborhoods. The results suggest that the role of urban form plays in travelbehavior is not entirely straightforward, sometimes influencing travel choices directly, sometimesindirectly, sometimes influencing choices in the short term, sometimes in the long term, andsometimes not having any measurable influence on choices at all. In the end, it appears that certainland use policies can help to provide alternatives to driving, but that the reduction in driving is likelyto be small.

Florida Sustainable Communities Network (FSCN) INDEX Software

The Florida Sustainable Communities Network (FSCN) INDEX software is the result of thecollaboration between the Florida Department of Community Affairs (DCA) and CriterionPlanners/Engineers, Inc. (Criterion), available to city and county governments since February, 1999.Criterion designed the INDEX software for Florida Sustainable Communities Network (FSCN)utilizing GIS modeling to measure specific sustainability indicators. Indicator scores are calculatedfor any given community to review current conditions and to track future changes and trends.Criterion's initial model includes 25 FSCN "Starter" indicators (communities are free to addindicators as they see necessary and as data collection allows) in land use, conservation, housing,employment, transportation, water consumption, and park availability categories.

While there are many similarities between INDEX and VOLUTI in terms of land use indicators usedand being GIS based, there are several main differences between the two: (1) INDEX is a customizedplanning tool developed for individual communities. To use INDEX, Criterion’s service is requiredto set up the program and develop the applications. VOLUTI, on the other hand, is designed as asomewhat generic tool that may be applied by anyone, given that the necessary data are available;(2) INDEX is designed for area or community planning with area size ranging from specific sites to

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500 acres while VOLUTI is design for both small and large areas; (3) While INDEX may be linkedto a travel demand model, e.g. it uses model output to display mode shares and per capita VMT, itsfocus is on land use planning. VOLUTI emphasizes linkage between land use and transportation andtherefore travel demand models have a much stronger role.

Land Use Planning Models

There have been many land use models developed for land use forecasting purposes. Oryani et al.(1998) classified land use models into four groups: Lowry and Lowry Derivative Models,optimization models, econometric-regression models, and economically-based land use marketmodels. The basis of the Lowry and Lowry Derivative Models is the assumption that, everythingelse being equal, place of employment determines place of residence. Constrained by regionalemployment and population totals, the model will allocate residence population close to non-servicetype of work places then allocates service employment to serve the population, which in turn requiresthe allocation of more residence for the service employees. The optimization models are based onthe idea that urban developments on new lands occur with the “goal” of minimizing transportationcosts and development costs. The econometric regression models are built upon econometricmodels. The last group of models are based on economics and markets. These models emphasizethe location of housing and trade-offs between travel distance, density, and amenities.

A land use model that has been adopted by a number of metropolitan planning organizations inFlorida is ULAM (Urban Land use Allocation Model) developed by Transportation PlanningServices, Inc., in 1998. ULAM is a land use forecast model that generates data for the transportationdemand model FSUTMS. The link to transportation in ULAM is a travel factor determined for eachTAZ based on free-flow travel time from FSUTMS.

Visualization Programs for Land Use and Transportation

Jha and McCall (2001) described various states-of-the-art of visualization technologies including2D overlay of orthophotos on maps, 3D visualization with geometric models, 4D visualization withanimated geometric models, surface and terrain models, drape of orthophoto onto terrain,photo-simulation that uses photographs instead of 3D geometric models and rendering, animationof a series of image frames, and real-time virtual reality and simulation. The authors pointed out that3D geometric modeling of a simple street scene could take 2-3 months of work and would involveintensive computation, while painting photographs over simple 3D models will reduce the work to2 weeks. 3D modeling effort may be reduced by using predefined 2D and 3D geometric objectscreated in CAD software. This kind of visualization will be tremendously helpful with publicinvolvement.

These techniques have been applied in various applications. Envision Sustainable Tools developedan educational software called QuestTM (http://www.envisiontools.com) for the purpose of supportingsustainable development through education to illustrate what sustainability is and how to achieveit. Six aspects or relevant perspectives of sustainability are examined: world view, politics,

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priorities, population goals and targets, economic goals and targets, and land use (suburbanexpansion, urban densification, mixed growth, and no change).

Harrelson et al. (1998) developed a visualization tool for the purpose of evaluating redevelopmentstrategies for the Myrtle Beach Air Force Base. The visualization tool is built with WorldConstruction Set (WCS) Version 4, a proprietary software package by Questar Production(http://www.3dnature.com/index.html) in Brighton, Colorado. The application can render GISfeatures such as roads, wetland boundaries, forested wetlands, and vegetation, and can populateterrains with sparse trees, tree stands, or dense woods. This approach is, however, expensive.

An alternative approach to virtual reality modeling is to combine geometric models withphotographs, which eliminates the need to produce realistic surfaces and material rendering. TheUrban Simulation team at the University of Los Angeles is in the process of creating a virtual modelof the entire Los Angeles basin (http://www.aud.ucla.edu/proj/usim.htm).

Development of Land Use Indicators

A number of land use indicators are developed. They include land use mix, and job/housing balance.The land use mix is expressed as entropy, the value of which is between 0 and 1, with 0 indicatingsingle land use and 1 indicating good mix. Its computation involves dividing a zone into grid cellsand averaging the entropy indices of the center cell and the cells surrounding it within a certaindistance. In VOLUTI, the grid cell size is 448 feet (or 1/8 of a mile) and nine cells are used foraveraging the entropy indices to derive the value for the center cell.

Job/housing balance is the ratio of total employment by total households in each TAZ. A low ratioindicates a predominantly residential area. A large ratio greater than 1.5 may indicate apredominately nonresidential area.

Average parcel size is an indicator of land use development intensity and potential for furtherdevelopment. In urban areas where high density development has occurred, the parcel sizes tend tobe small. Large parcel sizes are an indicator that land use may be intensified by further subdividingthe parcels therefore increasing the density.

Open space measure is the park acreage per 1,000 residents by TAZ. The City of Miami defines theacceptable level of service standard with regards to recreation and open space as a minimum of 1.3acres of public park space per 1,000 residents (City of Miami Planning Department 1993).

Land use changes are calculated for each TAZ between 1994 and 1998 by the following 15 land usecategories: agriculture, airports/ports, cemeteries, communications, utilities, terminals, plants, industrialinstitutional, multi-family, office, parks (including preserves & conservation), shopping centers,commercial, stadiums, tracks, single-family, streets/roads, expressways, ramps, transient-residential(hotels/motels), vacant, and water. Land use change is measured as the percentage increase ordecrease of the total area of a particular land use in each zone.

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Changes in total population, total employment, single family dwelling units, and multi-familydwelling units for each TAZ between 1990 and 1999 are measured in percentages.

Accessibility and Mobility Evaluation

Several indices have been developed to measure accessibility and mobility. Regional accessibilityby highway and transit modes, respectively, measures the accessibility to opportunities in a regionassuming driving as the travel mode. The opportunities may be employment or population (laborforce). Accessibility has been recognized as one of the most important factors that affect both landuse and travel behavior.

In VOLUTI, local accessibility is considered a measure of accessibility to “essential services.” These“essential services” include grocery stores, supermarkets, convenience stores (e.g., Seven-Eleven),bakeries, and drug stores. Availability of such essential services is both an indication of local landuse mix and of potential demand on transportation facilities as none or little service availabilitymeans that people will have to travel far to meet their needs instead of possibly walking or bicyclingto these destinations. Local accessibility to essential services is defined as a zonal index, computedas the ratio of the total employment in businesses that provide “essential services” in a zone to thezonal population.

Mobility is measured by travel times. In VOLUTI, a user may display a contour map of highway andtransit travel times for any selected zone. The travel time data are produced from the 1990 Miami-Dade County FSUTMS model. The model considers both highway and transit modes, and the resultsare the congested travel time based on the shortest paths. The transit travel times may be by modeor for all modes. Additionally, the differences in highway and transit travel times may be comparedto identify areas where transit services are weak. The map may be updated after the user makeschanges to either the transportation network (e.g., changing roadway attributes such as number oflanes or facility types) or to land uses (through land development). The contour maps provide ageneral sense of the relative ease of travel by cars. Another measure of mobility is the number of transfers required for traveling by transit, which is animportant measure of transit service quality. Transfers have negative impact on service quality aswell as on ridership because of the inconvenience and delay involved. Information on transfers isuseful to determine areas where travel by transit is inconvenient because of transfers required.Combined with transit travel time map and socioeconomic data, areas with inadequate transitservices may be identified and possible improvements can be investigated. The number of transfersis obtained by finding the shortest path between a zone pair considering the penalty applied and thendetermining how many transfers have been involved.

Development of Land Use Scenarios

Three land use scenarios are developed to test the VOLUTI site impact analysis ability. The threescenarios include low, medium, and high projection series. The population and housing projections

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were made based on the 1990 census data and population and dwelling unit projection series at thecensus tract level prepared by the Miami-Dade County Department of Planning. Jobs andcommercial development projections are made based on assumed jobs-to-housing ratios. Recreationand open space projections are made according to the acceptable level of service standard for theCity of Miami with regards to recreation and open space.

Assessment of Impact of Land Use Change and Transportation Projects

One of the major improvements in VOLUTI is the capability of evaluating the impact of landdevelopment projects on the transportation system and vice versa. This improvement is made in twoways. First the user can select and define property parcels for development and specify land useintensities, then evaluate the impact of the development in terms of increased traffic volumes, thevolume over capacity ratio (V/C) in the transportation network FSUTMS, and the accessibilitymeasures. The second approach allows the user to modify the transportation system and evaluatethe system performance. Since VOLUTI is not an integrated model for transportation and land useplanning, the interactions between land use and transportation cannot be fully captured. Theinteraction is only modeled through accessibility.

Site impact analysis is the study of the impact of land use developments on transportation facilities,usually in terms of changes in traffic volumes and in roadway level of service. The analysis istypically referred to DRI analysis, or analysis of Development of Regional Impact. The methodologyused for this analysis in VOLUTI is based on the procedure described in Site Impact Handbook(FDOT 1997). A statement needs to be made here that the DRI analyses performed in VOLUTI arepreliminary in nature, and can not be taken as a DRI analysis normally conducted by engineeringfirms. An actual DRI analysis will require much more detailed information. Information abouttransportation improvement projects, either having occurred since the last FSUTMS model update,having been committed, or being anticipated, must be collected and the transportation network editedaccordingly to reflect the conditions of the transportation system at the expected time of the land useproject. Similarly, land use changes must also be accounted for to reflect the land use conditions atthe expected time of the land use project.

To perform DRI analysis in VOLUTI, land development projects must first be defined. Adevelopment scenario is defined as projects located in a number of new TAZs, each of a single landuse such as single-family residential, multi-family residential, shopping center, etc. VOLUTIcurrently does not have the capability to modify the land use in a zone.

The methodology employed for site impact analysis is based on the model method described inFDOT 1997 Site Impact Handbook. The analysis involves three tasks: estimation of trips generatedby a development, proportion the estimated trips to different trip purposes based on the land uses,definition of productions and attractions for the new TAZs, which are treated as special generators,execution of the FSUTMS model for site impact analysis, and generate database files from the modeloutput for GIS display. To simplify the problem, the transit service in the transportation network is

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ignored at present, which leaves the highway-only analysis the only travel demand modeling optionin VOLUTI.

The results of a site impact analysis are given as maps showing changes in traffic volumes bynetwork link and changes in volume over capacity ratios by link. Because the model is outdated, theresults cannot be considered reliable and are provided only for illustration purposes.

VOLUTI Graphic User Interface Design

VOLUTI is developed within ArcView®, an Environmental System Research Institute product,customized with Avenue, the ArcView script language, and VisualBasic®. To allow people withlimited knowledge of ArcView or GIS to use VOLUTI, it is designed as a menu driven program, inwhich all queries may be made by selecting from the menus. Some customized tools are added toallow the user to interact with a map, such as selecting a TAZ or a network link. The standardArcView menu is customized with additional menus and menu selections. They are:

View menu includes a theme (layer) manager, image background control, Overtown boundarydisplay, geocode one address, clearing matched address, set and show default display area, redrawingmaps, and clearing all queries.

Land Use menu allows a user to view site photos, zoning map, vacant land, vacant land of given size,underdeveloped land, underdeveloped land of given size, total dwelling units, single family dwellingunits, and multifamily dwelling units, single family vacant dwelling units and multifamily vacantdwelling units dwelling units, dwelling units per acre. This is a measure of density or land useintensity, 1998 land use, land use composition in a region, building stock, land use mix andjob/housing balance, zdata change (1990/1999), average parcel size and park acreage, sales pricehistory (one property), assessed value (one property), assessed value (region), public facilities neara site, public facilities in a region, set search radius for site search, and water lines and sewer lines.

Environment menu provides information on shorelines, lakes and canals, flood zones, public wellfield protection area, trash centers and land fills, and hazardous waste sites.

Socioeconomic menu allows the user to display socioeconomic and demographic data by TAZ or bycensus block group. The data that can be displayed by TAZ include population density, populationof age 16 and younger, population between the ages of 16 - 65, population aged over 65, single-family population, multi-family population, commercial employment, service employment, industrialemployment, total employment, employment density, and school enrollment. The data available atcensus block group level include population, population density, number of housing units, vacanthousing units, median rent. Buffer analyses can also be performed on these data.

Transportation Facilities menu supports queries related to types of transportation facilities availableand selected attributes of roadways. The types of transportation facilities include public transitfacilities including bus routes and bus stops, limited access highways, principal arterials, collectors,

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and railroad tracks. The roadway attributes include number of lanes, 1996 average annual dailytraffic (or AADT) on state roads, traffic volume from the 1990 FSUTMS model, and 1996 level ofservice (LOS) on state roads.

Accessibility menu displays travel time contours (highway and transit), transit-highway travel timedifferences, transfers required for traveling between zones by transit, regional accessibility toemployment and population, respectively, by highway and transit, and local accessibility.Site Impact menu provides options for the user to create, edit, and delete land development scenarios.

Travel Demand menu allows the user to select a scenario and run FSUTMS to obtain traffic impactinformation.

Conclusions and Recommendations

This project has expanded significantly the earlier version of VOLUTI, with many additional data,queries, and analysis capabilities. Accessibility measures have been added to give a regional senseof the number of opportunities and transportation system conditions. A DRI analysis tool has beenimplemented to perform quick and preliminary assessment of impacts of land development projectson the transportation network as well as accessibility.

To further enhance the tool and make it easily adapted for other localities, the following issues needattentions and in some cases improvements are recommended.

1. GIS Data Maintenance and Availability. GIS applications are data intensive. Not only asignificant amount of data must be available initially, they need to be updated continually ifVOLUTI is to be useful a few years after its initial installation. There are several problemsthat will hinder the data maintenance effort. There is a fragmentation of data sources and alack of metadata, or documentation on the data in many cases.

The solution to this problem is to establish an enterprise GIS database within the county andmunicipalities, respectively, and close coordination between the county and the localgovernments to make arrangements on data collection, maintenance, and sharing. This willbe a long process, and will require some changes in the business processes. Theadvancement in information technology in recent years is moving the businesses in thatdirection with more data sharing. For instance, more data are becoming available on theInternet. However, a true enterprise GIS database will take a long term effort and a greatdeal of work toward inter- and intra-agency collaboration and coordination.

2. Site Impact Analysis. An immediate need is to update the FSUTMS to the 1999 model onceit is calibrated. The 2025 model should also be added. The transit mode needs to beincluded to evaluate at system level the development impact on transit ridership and toinvestigate land use alternatives and transportation programs that promotes public transit and

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reduce single-occupancy car use. The current VOLUTI implementation does not include allthe possible land uses, which should be added.

Another issue to be investigated is the interpolation between the base year and future yearmodels. Land use projects are typically planned with a time frame of several years to overten years, which are unlikely to occur in the model base year or the future year. It isnecessary, therefore, to reflect the conditions at the project implementation time. Suchconditions include demographic (e.g. population, household size, dwelling units, etc.),socioeconomic (mainly employment information), and transportation system (roadwaychanges, additional transit services, tolls, parking fees, etc.). Some of the information is notreadily available in digital format at present, and some does not exist. To perform such anestimate will be a challenge. Employment estimation by zone will be another challenge,regardless of the year for which it is needed. The 1999 Miami-Dade County FSUTMS modelhas also adopted a lifestyle trip generation model, which consider such variables as presenceof children in households and number of workers in households as the basis of determiningthe number of trips produce by households for different trip purposes. Methods forestimating these variables are being developed by the county Planning Department.

The transportation network update involves reflecting all of the changes in the roadways,transit services, toll, parking costs, fuel costs, etc., in the model. Some data may not beeasily forecast, such as fuel costs. Information on transportation improvement projects thathave been carried out or expected to be completed around the time of the developmentprojects to be modeled may be continually collected and a database constructed, which maybe used in model network update. The database should be spatiotemporal in nature, i.e., bothproject location information and specifics about the projects need to be coded. Programsmay be developed to automatically take information from the database and the modelnetwork may be updated for any given time.

3. Evaluation of Scenarios. Procedures and tools should be developed to allow differentscenarios to be evaluated. The evaluation may involve comparison of density, land use mix,vehicle miles traveled (VMT), travel time, trip length, etc., between two or more scenarios.

4. Link to a Land Use Model. VOLUTI may be linked to a land use forecast model such asULAM. This link will allow a better understanding of the impact of transportation ongrowth, that is how transportation improvements will affect growth in population and jobsin different areas.

5. Accessibility Measures. Population and employment resulted from new developmentsshould be added to existing TAZs before accessibility measures are update to reflect theimproved accessibility due to new developments.

6. Decision Support. Current VOLUTI implementation has limited capability of decisionsupport. A better capability may be arrived at by supporting more sophisticated queries and

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providing more analysis functions. Examples of queries and analyses that support decisionmaking may be to evaluate potentials of land for development, identify land developmentopportunities for a given goal or objective, determine adverse factors that may make adevelopment project questionable or increase the cost significantly, and performtransportation equity analysis.

7. Visualization. While virtual reality remains to be an expensive technology and is unlikelyto be practical on a large regional scale, the visualization may be further enhanced. Onepotential type of data that can be used for visualization is the video logs that FDOT routinelycollects on all the state roads. Presently, the LOS measures and display of operatingconditions are only available for state roads. The possibility of adding the capability ofshowing the user the LOS or operating conditions on local highways should be investigated.While FDOT does have the software to calculate LOS for local highways, it may requiremore detailed information that is not currently available in VOLUTI. A simplified algorithmthat gives a preliminary evaluation of LOS may be developed. Additionally, it is possibleto develop a methodology to categorize the local highway operating conditions based ontypical roadway configurations, intersection configurations, signalizations, and trafficvolumes to display video clips for different operating conditions. This will make it mucheasier for elected officials and the public to understand how the transportation system isfunctioning or what impact development projects will have on the roadways.

For developments at a scale smaller than regional ones, three-dimensional models ofbuildings and roadways may be useful for visualizing the aesthetic effects of highway ordevelopment projects. This may also be achieved with two-dimensional graphics. Forinstance, AutoCad and 3D-Studio may be used to create the graphics, which may then be“painted” on the three-dimensional models in ArcView.

8. Software. VOLUTI needs to be rewritten for ArcView 8, which is a new object-orientedArcView program, released in May 2001 by the Environment System Research Institute(ESRI). Although for the foreseeable future, Arcview 3.X versions will continue to besupported by its vendor due to the large number of existing ArcView applications, ArcView8 will certainly gradually replace ArcView 3.X versions in the future.

To make VOLUTI portable to different localities that use different databases, a mechanismto automatically configure the program for different databases and database setups is needed.The setup program will guide the user through installation, check the presence of differentdatabases and their structures, and determine what functions should be available or how thefunctions should be modified to accommodate the given data.

In addition to software improvements, VOLUTI needs to be marketed to planners in the state,including the planners working for public entities and private sectors. This may be done by freedistribution of the software and workshops held in various parts of the state.

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1. INTRODUCTION

The Florida Atlantic University/Florida International University Joint Center for Environmental &Urban Problems completed a project under the management of the Florida Department ofTransportation, District VI called Transportation/Land Use Visualization Project in 1999 (York etal. 1999). The project offers a significant advance in transportation/land use visualization programsbased on the best practices of transportation planning organizations around the State of Florida. Thestudy investigated the best practices in integrating land use and transportation planning in Floridathrough a survey and analysis of the survey results. The survey results indicated that while mosttransportation and land use planning organizations recognized the importance of linking bothplanning fields, few had been able to incorporate this link into their practices. This was due toseveral reasons including, e.g., lack of institutional coordination between the transportation and landuse planning organizations, difficulty in building consensus among many municipalities affected bylarge scale land use or transportation projects, inadequate integrated planning and modeling tools,and the lack of up-to-date comprehensive land use data. However, the study pointed out thatvisualization was a useful tool for communicating with communities to convince the public of thebenefits of some of the changes. In fact, maps, aerial photographs, digital photographs, Internet, andeven 3-D animation have been used by Hillsborough County for some projects.

The study also investigated different technologies that might be incorporated into visualization tools.These included GIS, multimedia, the web, and the global positioning system (GPS). The 3-Danimation was considered an attractive technology because it had the ability to create realistic 3-Dscenes and animation effects. However, even though the technology is mature, its use is still ratherexpensive and is not affordable for the project. To study the feasibility, technological options, anddesign and implementation issues, a prototype software program called VOLUTI (Visualization ofLand Use and Transportation Interaction) was developed. It is a geographic information systems(GIS) based program that combines various data sources with the latest visualization methods suchas digital photography, and dynamically generated maps to help decision-makers and the publicunderstand transportation decisions and their impacts.

The pilot VOLUTI program uses data on land use patterns, environment, socioeconomic,transportation facilities, and images data on the South Dade busway corridor. The data came fromseveral sources, including Miami-Dade County, FDOT District VI, and Florida Power & Light.Combining numerical and graphical data, the software is designed for people with little knowledgeof GIS, and may be used by users to generate visual maps or graphics through menus.

While the VOLUTI program provides a useful means of linking data and displaying the results fromFSUTMS models (Florida Standard Urban Transportation Model Structure), its capabilities werelimited because it did not include adequate measurements of land uses and transportation systems,or the interaction among them. Some of these limitations are to be overcome in the project describedin this report.

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2. RESEARCH OBJECTIVES

Building upon the previous research findings and tools developed for the prototype version ofVOLUTI, this project continued to develop an integrated GIS-based tool that includes more land useand accessibility measurements, and additional functions related to assessment of impacts of landuse developments and transportation projects.

These improvements involved incorporation of additional data sources, development of land use andaccessibility indicators, development of land use scenarios, and a stronger linkage between VOLUTIand FSUTMS (Florida Standard Urban Travel Model Structure), the standard travel demand modelin Florida. The tool incorporates a variety of databases, multimedia imaging, travel demand models,and useful evaluation methods to support visualization of land use and transportation information,and evaluation of land use and transportation interaction. Overtown, one of the Miami-Dade CountyEmpowerment Zone neighborhoods, was chosen for the project for demonstration purposes.

The project attempted to achieve the following objectives:

(1) Identify and collect additional useful data for the study area that allow the enhancement ofVOLUTI capabilities;

(2) Identify potential sources of historical data that may be used to build temporal GIS anddatabases to support longitudinal analysis of land use and transportation;

(3) Enhance visualization capabilities in VOLUTI;(4) Develop a set of measurements for the evaluation of land use and transportation; and(5) Improve the link between VOLUTI and FSUTMS.

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Figure 3.1 Miami-Dade County and Overtown Boundary

3. BACKGROUND - THE OVERTOWN STUDY AREA

Overtown is a small community of less than one square mile at about 470 acres. Figure 3.1 showsthe boundary of the overtown area. Despise its size, it has a rich history within the context of theCity of Miami. In the late 1800s as Miami was being built, "Colored Town" was established on thewest side of the Florida East Coast (FEC) railroad tracks. Specifically restricted through segregationstatutes, Overtown was the only area in which blacks were allowed to purchase properties. In about1940, though the official designation of the area was the Central Negro District and the historicalreferences were to Colored Town, the neighborhood's popular name became "Overtown," whichdeveloped from the colloquial reference to the area. People would often say, "I'm going over town"because it was necessary to go "over" downtown to get to Colored Town from Coconut Grove (Dunn1997).

The neighborhood developed its own subculture and many businesses, and individuals thrived in thearea despite racial tensions, municipal neglect, and persistent poverty (Dunn 1997, Dluhy 1998).Once a vibrant and stable African-American community, economic and social forces, modified bypublic interventions, have served to debilitate the community since its heyday during the 1920s to1940s. Three clear periods of decline were identified in the 1998 Transportation Impacts Study bythe FIU Institute of Government (IOG). Throughout these phases, rumors and threats of freeway anddowntown expansion, out-migration of the most stable, more middle-class residents and businesses,and in-migration of the more transient populations have served to exacerbate the severity ofpopulation losses due to public actions.

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The first phase took place from the 1940s to 1965. The area had been extremely overcrowded,approaching a population of 40,000 in 1940, with substandard housing conditions. There were asmany as 30 small wooden "shotgun shacks" to an acre in Overtown. During this period, thecommunity experienced a gradual rate of destabilization and business decline, mainly due to theforces of integration, school desegregation, increased opportunities for housing in the suburbs, andaggressive code enforcement; many homeowners sold their properties to slumlords who constructedthe "concrete monsters" and created an increase in apartment living. Additionally, from 1950 to1956, the State Road Board drafted a proposal for an elevated highway that would run into thedowntown area to alleviate the traffic problems there. Residents and businesses were displaced andrelocated during 1965 and 1966, but concern over displacement was evident several years earlier.In 1966, construction began on I-95 through Overtown.

Several public projects took place during the second phase, between 1966 and 1970, the mostconsequential of which were the construction of the I-95/I-395/SR836 highway interchange andUrban Renewal. Both projects displaced the residents and commercial activities, weakening theeconomic base and employment centers within and near Overtown. While in the first phase the mostaffluent and economically active population moved away, opening the community to economicvulnerability, the remaining businesses seemed healthy and many key institutions were intact,bringing many former residents back to the churches, schools, and businesses in the area. However,the influx of new renters and the projects that called for massive displacement of the populationdestabilized the social cohesion of the community. Absentee landlords and speculative land ownersput up no resistance to the condemnation of 70 acres of residential, commercial, and mixed-use lots,demolishing and displacing many businesses that had established themselves in the area for decades.Vital and integral services in the community were removed, dissolving community cohesion. Thefreeway divided the community into four quadrants, separating a primarily residential area from thebusiness corridors and the important educational institution, Booker T. Washington High School,and used up about 200 acres, or 42 percent of the land in Overtown. Businesses, cut off from thepopulation center, declined rapidly. The Urban Renewal projects displaced about 7,500 residents(2,400 families) and all of the businesses on the west side of NW 3rd Avenue. Several years passedbefore new structures were built, resulting in vast amounts of vacant tracts of land that wereeventually turned into standardized, monotonous housing in large single-use plots.

The damage that these public projects did to the Overtown community might have been reduced hadmitigation and reinvestment taken place immediately after the impacts were apparent or hadstrategies been developed in response to the problems that they caused. However, no publicintervention was created in these years, and the initial decline escalated due to the lack of investment.The final phase, taking place over the last thirty-odd years, is characterized by disinvestment and lackof revitalization that has created a fragmented, impoverished, distressed neighborhood. Public effortsto improve the neighborhood, due to their sporadic, inconsistent, and uncoordinated character, havefallen far short of attracting the level of private capital necessary to help the community recover. Anincreasing amount of land has been acquired by churches, community based organizations, and thegovernment through housing development efforts, condemnation through code enforcement, andtaking properties that had liens against them. Several community leaders living in exile desire to

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re-create the community they knew in the prosperous pre-War era but have no faith in the existinggovernment structures or public decision-makers. Especially in the latter half of this phase, a greatmany economic development strategies have been developed and implemented with no significantimpact on the community and no private, non-subsidized investment. Successful economicdevelopment efforts may continue to be elusive in this community until open communication amongtrusting partners in the community, the commercial, and the governmental actors is achieved.

Today, Overtown has a population of about 8,000 and is primarily a residential community, mainlyconsisting of low- to medium-density multi-family housing including a large proportion of publicand cooperative housing units. A larger percentage of land is publicly owned and therefore off thetax rolls (City of Miami 1993). In addition, there are many "concrete monsters" remaining from theapartment boom of the 1950s and 1960s that are in great disrepair (Dunn 1997).

Commercial activity, once central to the thriving community, was reduced from 380 establishmentsin 1949 to only 31 in 1989. There are two important commercial corridors, including the length ofNW 3rd Avenue, and NW 3rd Street between NW 3rd Avenue and Miami Avenue. Othercommercial properties are scattered throughout the residential sections. There is also an importantindustrial section in the northeast quadrant of the area (City of Miami 1993; Dluhy 1998).

A 1993 study by the City of Miami's Planning, Building and Zoning Department (PB&Z) reports onthis latter phase (City of Miami 1993). It states that the overwhelming need in the Overtowncommunity has attracted a great deal of attention and money during this time, with little to show forthe efforts. Economic development projects have had minimal success; for example, the SoutheastOvertown Park West Redevelopment Area was expected to provide financial spillover due to theinvestment that took place there in the 1980s. That spillover never occurred.

In this study, the PB&Z identified the following concerns in the community:

• Crime rate• Private/public housing conditions• Delivery of adequate social and health programs• Economic development• Park improvement• Code enforcement/violations• Employment/training• Security• Physical appearance/aesthetics• Homelessness

Housing problems include the shortage of housing and lack of affordable housing, outdated andsubstandard housing for both single and multifamily something, a lack of resident homeowners andan abundance of transients and renters, overcrowding, and absentee landlord ownership. The PB&Zcalls for stringent code enforcement, demolition and replacement of substandard multifamily housing

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and the construction of new, affordable multifamily housing, and incentive programs designed bothto encourage developers to construct new multifamily housing under the mixed-use paradigm as wellas to attract younger and more affluent families to the area.

Some of the problems that commercial enterprises face with respect to economic developmentinclude the low purchasing power of the residents, crime, drug trafficking and poor public image,high vacancy rates, higher interest rates on loans and insurance rates, and the lack of a strong,cohesive commercial center. The PB&Z suggests building mixed-use developments, thedevelopment of a strong center of specialty shops (like the Grove Village or the French Quarter inNew Orleans) through the development of the Overtown Folklife Village District to improve thecommercial aspect of economic development. In addition, there is a great need to build the skill-levelof the current local residents; the PB&Z suggests developing technical and vocational trainingfacilities, simplification of the enterprise zone application process, year-round employment foryouths and a "head-hunter" service to improve residents' chances of getting and maintaining gainfulemployment.

Economic development in Overtown will have to overcome a number of serious obstacles. A 1992study determined that there were no unassigned vacant sites available for development around theOvertown Metrorail station, and that the several vacant sites near the Culmer Metrorail station wereencumbered due to the private market financing interests (FCUDR 1992). Private investment hasbeen unavailable because historically there has been no governmental priority for communitypreservation or reinvestment initiatives in the area, because local Community Based Organizations(CBOs) have had a limited development track record and poor access to private financial institutions.Overtown represents a high-risk environment that has not been able to attract private outsideinvestment.

In addition, the lack of private ownership and wealth in the area inhibit traditional investmentopportunities. About 40% of the property is owned by the government (most of it housing) and offthe tax rolls-most of the multi-family housing is government owned and semi-privately managed andoperated. A high percent of households receive government subsidies due to the high poverty rate,low education level, and low employment opportunities that characterize the neighborhood.Overtown has one of the lowest median household income levels in the city, the highest poverty rate,and a lower unemployment rate than the total unemployment rate of the entire city (City of Miami1993).

Especially relevant to this project, the PB&Z assessed public transportation as providing excellentaccessibility by Metrobus and Metrorail, as well as by the availability of other public and privateservices (City of Miami 1993). However, they found that the community could greatly benefit froma decrease in the price of fares and from the construction of the additional East/West andNorth/South Metrorail corridors. The welfare-to-work study (Dluhy and Topinka 1998) indicatesthat access to the Airport and Miami Beach via an East/West line and to North Dade County and theNorth Miami Beach/Aventura areas via a North/South line will help ensure that jobs located in theseareas are available to Overtown residents on a practical level, because a substantial problem for

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Overtown residents is the amount of time needed to travel to these areas, particularly for those whoare able to find employment in those centers and for shopping. Street improvements andbeautification are the second aspect of transportation that the PB&Z identified. Problems hereincluded signage, gateways, landscaping, security fences, lighting, and other aesthetic improvements.

In addition to the need for access to jobs and shopping, residents in this area rely on public transitfor all their mobility needs to a greater extent than do residents in the rest of the county. A studyconducted by the FIU Institute of Government found that the Overtown/Liberty City study area wasthe neighborhood which was least likely to drive alone and most likely to use transit, and that theOvertown/Liberty City study area has the highest percentage of people who must commute morethan five miles to their places of employment (Dluhy and Topinka 1998). A high proportion ofresidents rely on public subsidies, and this population has very specific transportation needs. Forexample, few participants of the Work and Gain Economic Self-Sufficiency (WAGES) program owna personal automobile, many need to make multiple trips, and most need to take very long trips dueto the lack of commercial activity in the neighborhoods near their homes. None have the resourcesto spend much money on their transportation needs, and many need public transit services thatoperate on the third shift schedule.

The needs identified in the parks and recreation category of the PB&Z report (City of Miami 1993)may also be relevant to this project. Nine recreational parks, ranging from very small, passive parksto large-scale, active community parks are in need of renovation and rehabilitation. At that time, allof the parks needed lights for nighttime games and activities to also help reduce possibilities of crimeand violence in the area. Coordinating the community use of school open space and recreationalfacilities after school and on evenings and weekends were also recommended. In addition, the PB&Zcalled for the removal of Range Park No. 1 from underneath I-95 to be redeveloped for a differentuse.

Several other public utilities needs were also addressed by the PB&Z report. Problems with regardto water, sewer, drainage and lighting facilities included the inadequacy of sanitary sewers (PumpStation No. 5 in particular), localized storm drainage (which are improved as the streets areupgraded), and inadequate lighting in the parks and on several streets. In terms of solid waste, therewas found to be a need to control illegal dumping of rubber tires and construction debris, autoabandonment, excessive accumulation of garbage due to overcrowding of units, litter alongsidewalks and at bus stops, and code enforcement issues with regard to trash and maintenance.

The PB&Z identified other important neighborhood issues, including historic preservation, aestheticsand urban design, and quality of life issues. Forums that have allowed for public participation bearthis out. For example, the South Florida Regional Planning Council held a charrette in the Overtownarea in July 1999. Their mission was "to engage the entire community in creating a unified visionfor the residential and commercial renaissance of Overtown. The vision aims to restore Overtownas a destination and to higher levels of self-sufficiency and economic and social viability" (SFRPC1999: 1). They found that the local participants are very interested in immediately implementingphysical improvements based on historical, aesthetic, and quality of life concerns.

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Some other initiatives also offer opportunities. During the fiscal year 1999-2000 ended June 20th,2000, the Empowerment Zone (EZ) Trust completed its first phase planning efforts on the TownPark Housing New Markets project in Overtown. The EZ Trust has been awarded $10 million fromthe Miami-Dade County Housing Agency to support this project. The Trust’s objective is to developa mixed income, single detached and semi-gated community in Overtown (Dade County/MiamiEmpowerment Trust 2000).

The Overtown Neighborhood Assembly has made a clear commitment to economic developmentby being the first Assembly to pledge 100 percent of its funds ($200,000) to the Empowerment TrustMicro-Loan Fund (ETML Fund). Applications for funding were released on July 26, 2000. Privateinvestment pledges to the ETML Fund and the number of new jobs to be created by this program hasnot been determined (Ibid).

In July 1999, the Overtown Advisory Board, Eastward Ho! (which encourages redevelopment ininterior neighborhoods rather than westward) and other agencies sponsored a design charrette inOvertown. The charrette was a formal week-long brainstorming session where residents workedwith designers, town planners, and government officials to design a new Overtown. The OvertownRedevelopment Area Design Charrette Report published in March 2000 proposed the creation of acenter for Overtown and the redevelopment of commercial and entertainment districts among otherdevelopments. However, no funding was identified to make the citizens’ vision a reality (TreasureCoast Regional Planning Council 2000).

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4. LITERATURE REVIEW

This chapter presents an update of the literature on land use and transportation linkages, whichincludes reviews of literature not covered in the report “Land Use and Transportation Connection:Report on the Creation of a GIS-Based Visualization Tool Based on Best Practices,” (York et al.1999). Visualization techniques are also included in the review.

4.1 Sustainable Developments

Current sustainable development policies are concerned with economy, equity, and environment,combining economic development with environmental and social policy to promote longer-termprospects for economic growth while at the same time protecting natural resources and environment(Colgan 1997). This approach attempts to integrate environmental and economic decisions andplanning across sector functions in a long-term framework in which recognizes that borders aremeaningless in a global ecology. A mix of scientific and technological innovations along with the"reduce, reuse, recycle" mantra and economic efficiency market incentives, conservation,anticipation, and prevention of adverse economic and environmental impacts as well asrehabilitation, reclamation, and enhancement of natural ecosystems for higher productivity areaddressed (Colgan 1997, Center for Livable Communities 1999).

Community design principles, such as those relating to the size of the overall community, housing,jobs, services and activities, include guidelines relating to walkability, density, and diversity. Publicspace, open space, a jobs-housing balance in number and variety, connectivity, and efficient andpractical use of geography and passive solar energy are highly regarded concepts (Center for LivableCommunities 1999). These are in fact guiding some of the economic and community developmentinitiatives in the Overtown area (see for example, SFRPC 1999).

Public participation is one of the cornerstones of sustainability theory. Every individual shares theresponsibility and is accountable for the stewardship of the economy and the environment for thebenefit of present and future generations. This requires that government agencies provide the publicadequate, accessible, and timely information and requires understanding and respect for differingsocial and economic views, values, traditions, and aspirations. All individuals and governmentelected and appointed officials share this responsibility in a spirit of partnership and opencooperation (Colgan 1997, Center for Livable Communities, 1999).

Public participation in the decision-making process provides the foundation for implementingpolicies and developing strategies that promote sustainable communities. Meaningful participationensures that individual community members will take responsibility for the outcomes of theprojects-they will have had the opportunity to decide and design them and will have a stake in theultimate success or failure. Although it is a critical component to any economic or communitydevelopment initiative, public involvement is not always easy to attain. Forkenbrock and Schweitzer(1997) suggest the following guidelines towards successfully getting and utilizing public input:

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• Strengthen the role of neighborhood and community-based organizations in the planning process;hold community leaders accountable for participation.

• Educate planners on strategies to actively promote citizen involvement, and in particular, addressspecific cultural issues to facilitate communication.

• Use liaison organization to link neighborhoods with respect to regional issues and area-wideplanning.

• Recognize the limitations of traditional public hearings and comment periods; use innovativeapproaches to elicit involvement and recognize that what is practical for the planner may serveto exclude the resident.

• Involve minority and low-income populations in the early stages of the planning process.

• Provide information on key issues and changes; make better use of advertisements andannouncements at prime time.

The FCUDR report (1992) describes the community development principals of urban partnershipparticipation as based on most advanced national experience of the time:

• Meaningful partnership roles for neighborhoods on basis of self-help development: enabling theneighborhood residents, property owners, and businesses to become capable and responsible todevelop and service enterprises based on their own agenda and initiatives.

• Strong private sector involvement in multi-faceted partnership roles: public-private partnership,based on practical risk-sharing attitudes to lead to creative community reinvestment and makespecific projects work out.

• Decisive/reliable governmental priorities and partnership commitments based on sound paybacksto tax payers: in the era of "do more with less," public-private partnerships can be used toforward a number of public policies through the development of the distressed neighborhoods,such as infill development and revitalization of the CBD, zoning, code enforcement,environmental maintenance, and other programs provide more confidence in the private marketplace due to the encouragement of initiatives by property owners, local businesses, developersand lenders.

Furthermore, they emphasize that the best results occurred in cities in which the lines of authoritywere clearly outlined and provided control of policy by elected officials as well as administrativeflexibility in implementation, which was "the key ingredient for achieving private businesscommunity involvement in partnerships. While government continues to be the best source ofsupport for many public facilities, partnership agreements for specific development projects are

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typically based on solid evidence of net economic and fiscal returns to the tax payers" (FCUDR,1992, p. 6).

With respect to the Overtown neighborhood, participation by community stakeholders has onlyrecently been seriously undertaken and many would argue that it is still a long way from the idealdescribed here. However, several initiatives are on the table that utilize the community design andthe sustainability principles mentioned above.

4.2 Accessibility to Opportunities

Accessibility has been recognized as one of the most important factors that affect both land use andtravel behavior. How to define and measure accessibility has attracted the attention of manyresearchers and many forms of accessibility measures have been developed, which Richardson andYoung (1982) classified into a spectrum of accessibility measures as shown in Table 4.1.

Table 4.1 Summary of Accessibility MeasuresAccessibility Measures Features

Topological indicates if two points are connected by a transportation link

modal accessibility the degree of connectivity of two places depending on the modesavailable. This is consistent with the PPMS concept.

temporal accessibility accessibility varying during different time periods (e.g. transitservice is available only part of a day). PPMS utilizes this concept.

legal accessibility limitations or restrictions to accessibility by legal or regulatory rules(e.g. special permits issue to allow access certain area, one-waytraffic rules, and denial of access to the transportation system tocertain population groups).

Relative accessibility ease of travel between two points (e.g. a residential location and anemployment center) based on travel time or cost

Integral accessibility ease of travel between one point and multiple different points basedon travel time or cost

place-accessibility only spatial separation between one place and other places accountedfor

activity-accessibility activities at destinations accounted for explicitly

cumulative opportunity index number of opportunities (e.g. jobs) reachable from the origin withina predefined travel time or cost

gravity type measures sum of opportunities weighted by travel time or cost

logit model logsum term based on logit model; log sum of expected value of the maximumutility to be gained in destination choice situation

12

A eic B C

k

mk ik

��

�

�ln ( )

1

EX X

IY Y

J� � � �

3 3 3 3

LA B B B C C Co x y o ox oy xy� � � � � � �ln2

The logit model logsum term is given by:

where Ai is the accessibility index, Bk is the benefits gained by participating in activity at site k, Cikis the cost of travel between sites i and k, and c is a sensitivity coefficient. Richardson and Youngconsidered one major deficiency of the above measures of accessibility being that in the calculationof accessibility of a point within a region, it was assumed that all trips that contribute to theaccessibility of that point start from that single point. Instead they proposed that for linked trips, thechoice of a destination does not depend on the travel cost between that destination and the origin,but instead it depends on the travel cost of between that destination and the immediately precedingdestination, and so on. For a linked trip with two destinations, the linked accessibility of a site o isgiven by

where LAo is the linked accessibility of site o, Bi (i = x, y, o) is the benefit to be gained byparticipating in activity at site i, and Cij (i = o, x and j = x, y) is the travel cost between sites i andj. It was demonstrated that in the case of two-destination linked trips, accessibility calculated as thelogit model logsum term will be significantly underestimated when the origin is far from the centerpoint between the two destinations. In other words, as the distance between the destinations and theorigin increases, the linked accessibility will better reflect the benefit of making a linked trip, whichreduces the travel time as compared to two unlinked trips. One important implication is that theaccessibility of a suburban resident may be improved by linking trips and thus long distance fromthe urban core may not be as large a deterrent to urban sprawl as expected if unlinked accessibilityis used.

Allen et al. (1993) considered that the relative or integral accessibility in its original form ormodified forms was not able to reflect the overall accessibility in an area. Consequently, theydeveloped an area accessibility measure that was based on the average of the integral accessibilityof a set of random points to other points in the area, and showed that if a rectangular area ofdimensions of X and Y was divided such that there were I and J equally spaced internal points in therectangle, respectively, then the average accessibility, E, will be

When I and J become large, E may be approximated by (X + Y)/3. Using this accessibility, Allenet al. studied the employment growth rates in major U.S. metropolitan areas using regression, andargued based on the regression results that the accessibility index was significant at 0.02 level (p-value).

13

An application of gravity type accessibility measure to travel behaviors study is described in(Kockelman, 1997). The accessibility index was defined as the sum of all attractions (e.g.employment) weighted by friction terms that reflect the ease of travel between a location and activitycenters. Zonal attractiveness may be measured by total employment or commercial and serviceemployment. The friction term f(tij) often assumes an exponential form with coefficients estimatedby Levinson and Kumar (1995).

4.3 Effect of Urban Forms on Travel Mode Choice

The need to understand how urban forms may affect travel behavior has taken on an urgency due torecent policy initiatives at the federal, state, and local levels to look for ways to improve mobilityand reduce congestion without building new highways. These policy initiatives are motivated by theIntermodal Surface Transportation Efficiency Act of 1991 (ISTEA), which provided new fundingopportunities for transportation improvement projects not targeting single-occupancy-vehicle (SOV)mobility, the Transportation Equity Act for the 21st Century (TEA-21), which initiated a newsustainable development pilot program to help state and local governments planenvironmentally-friendly development, the Clean Air Act Amendments of 1990 (CAAA), which setsvehicle miles traveled (VMT) as a form of mitigation to meet air quality attainment, rising publicconcerns about petroleum consumption in the U.S. and global warming, and political pressure toreduce fuel consumption. One of the approaches to reduce VMT is to change travel behavior viapolicies such as taxation, pricing, and land use planning. The question is therefore whether land usepolicies that encourage “transit/pedestrian friendly” neighborhoods will be effective. Researchershave been attempting to answer this questions by looking into land use factors and their links totravel behaviors.

One of the most influential works may be that by Pushkarev and Zupan (1977). They investigatedthe impact of land use, spatial separation, and transit service quality on transit ridership. The landuse variables are the suburban residential housing unit density and central business district (CBD)floor space, which is used as a proxy of jobs. Spatial separation is measured by distance betweenthe CBD and the residential areas. By comparing different bus routes, the authors found that therewas a significant correlation (0.75) between transit use and density. There is a four percent increaseof workers using transit for every doubling of density. Their analysis results led to several interestingfindings: a density of seven to thirty dwelling units per acre is the threshold of significant transit use;“high residential density by itself does little for transit if there is no dominant place to go to.” Theypointed out, however, that the higher transit ridership was not induced by density per se, but due toincreased availability of employment and other opportunities, as well higher parking cost and morecongested roads that have limited capacity to accommodate automobiles.

In another study of the 1979 New York Urban Region survey data, Pushkarev and Zupan concludedthat “there is no statistically significant effect of income on driving once other variables (density,household size, number of adults, etc.) are held constant” (Holtzclaw, 1990).

14

entropy singlefamily singlefamily

multifamily multifamily

retail and services retail and services

office office

entertainment entertainment

institutional institutional

industrial/manufacturing industrial/manufacturing

� � �

� �

� �

� �

� �

� �

� �

log ( )

log ( )

log ( )

log ( )

log ( )

log ( )

log ( )

10

10

10

10

10

10

10

By simple regression, Newman and Kenworth (1989) also found high correlation betweenautomobile use (measured by petroleum consumption) and density by studying major cities aroundthe world. Specifically, they found a correlation of -0.74 between urban density and private car use,+0.74 between density and transit passenger trips, and -0.76 between density and auto ownership.The correlation between density in central business districts (CBDs) and private car use is, however,much lower at -0.14. In their study, however, other important factors such as culture, governmentpolicy, gasoline prices, transportation system, transit service level, income, etc., were not controlled.These factors vary significantly in different countries and may have an important influence on travelbehavior.

An empirical study was performed by Frank and Pivo (1994) to determine if density was a proxy ofother factors or itself caused a difference in mode choice, with the purpose of discovering ways toimplement urban forms that promote accessibility in urban areas. By analyzing mode choice forwork and shopping trips based on land use variables such as population density, employment density,and land use mix at census tract level, life-style variables such as age distribution within a surveyedhousehold and mean age of survey participants per census tract, and other non-urban-form variablesincluding proportions of survey participants with a driver’s license, mean number of vehicles forsurvey participants ending trips in a census tract, and proportions of transit trip ends made by surveyparticipants employed outside home, by those participants who had a bus pass, and by those who hadaccess to less than one vehicle, respectively. The land use mix was measured by an entropy indexdefined as follows:

Multivariate regression analyses showed that urban-form variables entered after including significantnon-urban-form variables in the models did contribute to mode choice, with positive impact ontransit use and walk and negative impact on SOV use, respectively. The analyses also suggested thatemployment density at both trips ends should be used to explain the variation in mode choice insteadof using the density at one trip end. Additionally, land use mix seemed to better explain the choiceof walk mode. The property of the functions that relate the urban-form variables to mode choice wasalso investigated. The authors suggested that such functions are non-linear in nature. Plot of modechoice versus gross employment per acre was created and from the plots the authors determined that

15

significant shifts from SOV to transit use and walking occur between an employment density of 20and 75 employees per acre and again when density exceeded 125.

Kockelman supported Pushkarev’s and Zupan’s conclusion in a study on the relative effect ofpopulation density and income on modal split (Kockelman, 1995) . She showed that density (orother factors proxied by density such as land prices, parking fees, transit service frequency, andcongested roadways), not income, was the influential factor on modal split. The study analyzed dataof three different levels covering 108 San Francisco Bay Area (SFBA) census tracts, 41 SFBA cities,and 35 U.S. metropolitan areas, respectively. Only work trips are studied due to data limitation. Byanalyzing census tract data using single variable regression, the percent of workers not driving alonewas found to be significantly related to density (correlation 0.891 and R2 0.794), but not so to incomelevel (correlation -0.289 and R2 0.084). Density and income are not significantly correlated. Inmultiple regression analyses, a destination index is used to serve as a coarse proxy for transit level-of-service to and at the workplace and the regional importance of that destination for employment.The index was constructed as the weighted sum of percent of workers that commute to differentcities. The weight for San Francisco is 10 while 3 is used for other cities (Berkeley, Palo Alto, andSan Jose). The multiple regression results again showed that density and destination index are moreimportant than income levels. The elasticity of percent of workers not driving alone is +0.35 withrespect to residential population density, -0.10 with respect to income, and 0.2 with respect to thedestination index. Not included in the model are working place parking policies, congestion alongtraveled routes, access to alternative modes, land use mix, trip length and cost, and transit servicesupply (destination index is a crude estimate of transit service availability), and non-work trips, allaffecting mode choice and overall impact of these factors on travel behavior.

Similar analyses performed at the city level for the San Francisco Bay Area include a dummyvariable for access to the BART system, the rail rapid transit. The regression models suggest anelasticity of +0.35 for density, -0.25 for income, and +0.17 for BART access, respectively. AlthoughBART access appears to have a significant impact on single vehicle occupancy, Kockelmanconceded that the measure at the city level was coarse and pointed to a study by Robert Cervero(1994) that suggested that workplace parking policies, destination relative to station locations, andvehicle ownership are important factors in determining the mode choice for residents near the BARTstations.

Kockelman (1997) investigated the link between urban form and travel behaviors and concluded inthat accessibility, land use mixing, and land use balance were all statistically significant andinfluential to travel behaviors, including mode choice. In addition to the accessibility indexdescribed in the accessibility section previously, other measurements used are briefly introducedbelow.

Entropy (Land Use Balance) EntropyP P

Jj j

j� �

�

�ln( )

ln( )where Pj is the proportion of land development of the jth type and J is the number of different typesof land development, which include residential, commercial, public, offices and research sites,industrial, and parks for analysis of work trips, and residential, commercial, public, and parks for

16

Accessibility e Y T T T�

� � �ln( ) . . .0 175 0 009 0 0000092 3

analysis of non-work trips. To avoid bias against small census tracts that do not have adequate areato allow a variety of land use types, a mean entropy is used:

Mean entropy

P PJ

K

jk jk

j

k� �

�

��

ln( )ln( )

where K is the number of actively developed hectares in a census tract, and Pjk the proportion of landuse type j within a 0.8-km radius of developed area surrounding the kth hectare.

Dissimilarity Index (Land Use Mix) mix indexK

Xk

iki

� � �1

88

where K is the number of actively developed hectares in the census tract, and Xik is a dummy variablethat assumes 1 if the central active hectares’ use type is different from that of a neighboring hectare,and 0 otherwise.

Linear regression models relating vehicle kilometers traveled (VKT) per household and different setspredictors showed that the inclusion of the accessibility, entropy, and land use mix indicatorssignificantly increased the R2 when compared with models that only included household size, incomeper household member, and auto ownership. In the logit mode choice model, the inclusion ofaccessibility, population density, and employment density (all measured at both the origin anddestination zones) also increased the psuedo-R2 compared to models that only had trip distance,gender, age, race, number of workers, number of drivers, number of professional workers, autoownership, household size, and member income as explanatory variables. Analysis of the elasticitiesof independent variables with respect to household VKT (total and non-work home-based) and modechoice shows that these variables are highly sensible to accessibility (e.g. with an elasticity of -0.35for non-work home-based VKT and 0.22 for walk/bike choice). Land use mix and mean entropy arealso influential. It is also concluded that accessibility is a far better predictor of VKT than density.While capable of identifying statistical correlation among travel behaviors and variables used in thisstudy, the limitations of simple regression or logit models in determining the direction of causationhave been recognized by the author, who contended that a structural model may be able to betterexplain the causation.

A similar study by Sun et al. (1998) also used a similar approach. Using the 1994 Portland TravelSurvey data, density (population, employment, dwelling units), land use mix, accessibility, annualhousehold income, household size, dwelling type, number of phone lines in a household, presenceof a car phone, auto ownership, home ownership, and year in current residence, number of activities,proximity to light rail are analyzed to determine their impact on household trip rates and VMT.Transit mode choice was not studied. The accessibility measure is given as:

17

and is computed for home-to-job and job-to-home trips, respectively. ANOVA, linear regression,and sensitivity analysis are the methods applied. The authors proved that dwelling type wasindependent of household income. To determine if density and land use mix is due to the choice bypeople with certain income levels, histograms were plotted. The authors claimed from thehistograms that low income households have a slightly higher concentration in high density areasand areas with better land use mix, there was no fundamental difference in household incomedistribution in different types of neighborhoods. Regression analysis showed that density and landuse balance make little difference in the number of daily trips but has a significant impact on houseVMT. High density and high entropy both contribute to a reduction of VMT (by 19 percent and 45percent, respectively).

In a study of Miami-Dade County in Florida, Messenger and Ewing (1996) established two sets ofsimultaneous equations by place of residence and by place of work. The first set relates transit shareby place of residence to zero or one automobile households, land use mix/balance, and bus peakfrequency; zero or one automobile households to household income, logarithm of residential density(residential and employment), morning peak bus run time to downtown; and logarithm of residentialdensity to zero or one automobile households, logarithm of overall density, a variable rating streetnetwork resemblance to a grid system, and a dummy variable indicating proximity to the rail rapidtransit, respectively. The second set relates transit share to morning peak bus run time to downtownand zonal average seven-hour parking cost; and the parking cost to logarithm of overall density, adummy variable indicating a zone is part of the downtown, and proportion of jobs in commercial andservice sectors, respectively. The equations are simultaneously estimated by a full-informationmaximum likelihood method. The first set of equations (based on place of residence) has a betterexplanatory power (R2's ranging from 0.34 to 0.49) than the second set (based on place of work) (R2'sranging from 0.11 to 0.38). From the estimated equations, it was decided that the density needed tosupport a 25-minute bus headway was 8.4 dwelling units per acre (1.4 higher than that proposed byPushkarev and Zupan) at the transit operator’s minimum productivity and 19.4 dwelling units peracre at the system wide average productivity. Additionally, different factors affect transit use atdifferent trip ends. Bus mode share at trip origins is primarily a function of low automobileownership, and secondarily of job-housing balance and transit service level, although job-housingbalance has a small effect. Street configuration is found to have no apparent effect on transit use.This is in disagreement with results from several other studies (Cervero and Gorham, 1995; Handy,1992; Hsiao et al., 1997; Kockelman, 1997). Bus mode share at trip destinations is primarily afunction of parking cost, overall density, and access to downtown. The models of trip end transitmode share only explain a small portion of the variation in the data, indicating that other factors needto be identified.

In an attempt to determine if land use truly has a causal relationship with travel behavior or whetherit is other socioeconomic, demographic, and transportation supply characteristics, which are alsoassociated with land use, that are the real determinants of travel behavior, Kitamura et al. (1997)conducted a household survey (including a three-day travel diary) in five neighborhoods in the SanFrancisco Bay Area (SFBA) and investigated the travel behavior variables and a wide array ofvariables that are objectively or subjectively measured. The five neighborhoods are approximately

18

one square mile in size. The medium zonal income was held relatively uniform to control the effectof income on travel while other characteristics such as land use density and mix are chosen as torepresent extreme conditions. The travel behavior is measured by number of trips, number of transittrips, and number of non-motorized trips per person per day, and the mode shares. Data about thesites were collected, which included street characteristics (width, sidewalk, bike lanes, speed limitsand other traffic control devices), public transit service (bus stops, service frequencies, etc.),location and type of commercial developments, parks and other public facilities, and generalneighborhood characteristics (for detail see (Kitamura et al., 1994)). Dummy variables were usedto represent access to rail transit, mixed land use, high density, presence of sidewalk, presence ofbike lanes, backyard, available parking space, house ownership, sex, homemaker, student,professional, low education level, college education, graduate degree, high and medium personalincome, respectively, apartment, single-family home, and responses to an array of questions relatedto reasons for staying in the area (no reason to move, streets pleasant to walk, cycling pleasant, goodlocal transit, enough parking, and congestion problem). Measured variables include distances tonearest bus stop, rail station, grocery store, gas station, and park, respectively, and household size,number of persons over age 16, number of vehicles, number of vehicles per persons over age 16,household income, age, driver’s license holding. Results of the regression models indicated that thevariables had weak power to explain mode choice, with R2's for all models smaller than 0.14.Nonetheless, these results led to the conclusions that have been generally agreed upon such asparking availability negatively impact total number of person trips, and high density, proximity toparks and bus stops, access to rail transit stations, and presence of sidewalks encourage non-motorized travel. Furthermore, attitudes (pro-environment, pro-transit, suburbanite, automotivemobility, time pressure, urban villager, TCM, and workaholic) were determined to have moresignificant impact on travel behavior than socioeconomic and land use characteristics, with land usecharacteristics being the weakest predictors. This is of particular interest because current modal splitmodels do not include them as determinants of mode choice. These variables may also account forthe some of the unexplained variability in transit mode choice since we know people are not alwaysas rational as assumed in logit models in which a trip maker is supposed to make a mode choice bymaximizing the utility of the trip, which involves comparing the generalized costs for a trip viadifferent means. On the other hand, it is impractical to include such attitudinal information in themodels as such information is difficult, if not entirely impossible, to forecast.

The many facets of the relationship between urban form and transit were re-examined, explained,evaluated, and documented in a TCRP project for the purpose of helping making effective publictransportation investment (Seskin 1996). The TCRP project attempt to answer the questions of howurban form influences the demand for light rail and commuter rail transit and how transit influencesland uses. Urban structure, employment and residential densities, land use mix and urban designwere found to influence transit use. However, although land use mix and urban design wassignificant in explaining transit use, individual land use and design was not. Also, density is morepowerful than land use mix and urban design in explaining transit use. On the other hand, theinfluences of transit on urban form were described by using the following four factors: propertyvalue, intensity of development, urban structure, and timing of development. First, accessibility torail transit typically results in higher residential and commercial property values and rents. Second,

19

although rail transit has impact on transit station areas where transit confers a distinct accessibilityadvantage on a location, the effects are varied among different networks. Third, both CBDs andsubregional centers have benefitted from station-area development. Finally, major rail investmentscan accelerate development in station areas.

4.4 Neighborhood and Urban Design

Neotraditional neighborhoods are characterized by a closely spaced street grid, high density, andlocation often near street car tracks. Such neighborhoods are often older and built before the end ofthe World War II. There has been much debate as whether urban design has any impact on transituse. Some argue that neotraditional neighborhood design encourages walking and transit use, whileothers disagree. Many studies have been conducted to determine the effect of urban design variables.

Handy (1992) studied shopping trips in the San Francisco Bay Area based on regional and localaccessibility indices. The indices are based on the gravity model and are proportional to local (orregional centers) attractions and inversely proportional to an exponential function of travel time.1980 data from the census and a regional travel survey of 7,235 households were aggregated atsuperdistrict (34 in total) level and used for analyses. Handy found that two to four more bicycleand walk trips were made by residents in two areas that closely resemble neotraditionalneighborhood than by those living in areas that are automobile oriented . She did not conclude ifthese trips by non-motorized modes actually replace some of the automobile trips or theneotraditional neighborhood simply encouraged more walk and bicycle trips. The approach ofanalysis based on accessibility indices have several weaknesses. Firstly, the use of superdistrictsmay mask the variability of accessibility in different parts of a zone. Secondly, the local accessibilityis easily affected by the choice of zonal boundaries, which are somewhat arbitrary. Anotherlimitation of the study is that the trip data did not distinguish convenience shopping (happeningmostly locally) and comparison shopping (often at regional centers). Therefore it is impossible toevaluate how local and regional accessibility affect the travel patterns individually. Additionally,other factors such as income are not controlled in the study. Furthermore, other factors that mayaffect travel patterns are not controlled in the study.

Cervero considered a fault of many comprehensive studies on the relationship between builtenvironment and travel behavior to be inadequate control of income and other extraneous factors.In his study of travel characteristics comparison using data from San Francisco Bay Area and LosAngeles, he carefully paired “transit neighborhoods” and “auto neighborhoods” by a set of selectioncriteria (Cervero, 1994). The “transit neighborhoods” are defined as initially built along street carlines or a rail station, primary grid street network, and built before 1945. The “auto neighborhoods”are those not designed for transit and have no transit services, primarily random street patterns (over50% of intersections being “T” intersections or cul-de-sac), and built after 1945. To match the autoneighborhoods with the transit neighborhoods, criteria controlling income, transit services,topography, and size are used. For an auto neighborhood to match a transit neighborhood, there canbe no more than ten percent variation of medium household income from that of the transitneighborhood; there should be transit services (type and density) comparable to the transit

20

neighborhood; it should have similar topographic and natural conditions; and it should be locatedno more than four miles from the transit neighborhood. Additionally, an auto neighborhood also hasto have a significantly lower percentage of four-way intersection cross roads and the net residentialdensity lower than or equal to that of the transit neighborhood. By applying these criteria, sevenneighborhood pairs in SFBA and six in Los Ageles were identified.

A comparison of the SFBA paired neighborhoods revealed that while other demographiccharacteristics (such as mean vehicles per household, percent of white households, and mean age ofresidents) of the neighborhood pairs do not differ significantly, most auto neighborhoods have ahigher auto ownership, produce much more drive-alone trips, have a lower transit use, and havemuch lower walk trip rates than transit neighborhoods, the latter being especially obvious. Onaverage, transit neighborhoods generate around 70 percent more transit trips and 120pedestrian/bicycle trips. This may be partly contributed to the fact that transit neighborhoods tendto have better transit service supplies (measured by daily VMT per acre). By comparison, the transitneighborhoods in Los Angeles do not demonstrate the same significant amount of transit use orreduction of single occupancy driving. Cervero contributed this phenomenon to the overall strongauto orientation in Los Angeles and believed that the positive effects of transit neighborhoods insuch an environment are limited. To take his conclusion one step further, however, one may arguethat the inconclusive relationship between transit neighborhoods and transit use in Los Angeles maybe a result of inadequacy of the transit services, which is affected by the built environment. InSFBA, transit services are much more concentrated and at a much higher level in transitneighborhoods than in Los Angeles, perhaps due to the higher percentage of neighborhoods thatqualify as transit neighborhoods. Not only does this attract people who desire to use transit to theseneighborhoods, but this also allows the transit providers to provide a good level of service in a largearea and increase the overall accessibility via transit. On the contrary, because of the dominant auto-oriented neighborhoods in Los Angeles, it is difficult to provide good transit services even to transitneighborhoods with the same efficiency and accessibility to opportunities.

Using data of the entire Los Angeles area, Cervero also regressed the percent of transit trips againstvariables including gross residential density (households per acre), natural logarithm of householdincome, neighborhood type (auto or transit), and density interaction (product of residential densityand neighborhood type), and achieved a R2 of 0.55. According to the model, all variables aresignificant at a significance level of < 0.001. In Los Angeles, everything else held constant, transitneighborhoods will generate 1.4 percent transit trips per every 1,000 households while those inSFBA will generate 5.1 percent transit trips. Another conclusion was that in Los Angeles, densitydoes more than neighborhood type in increasing transit use. Increasing density by one dwelling unitper acre will increase transit trips by two to four percent. The density-neighborhood type interactionterm has a stronger effect in the SFBA than in Los Angeles. Work trips by transit averaged 8 percentmore if density was 10 units per acre and 13.5 percent more when density was 30 units per acre.What is not controlled for, but may influence the mode choice, is congestion.

The inconclusive effects of various urban form variables on travel behaviors, particularly onreducing automobile dependency, were supported by Clifton and Handy (1998) in a study of six

21

Austin, Texas neighborhoods. The study explores the motivations for travel as well as the patternsof travel. Travel surveys and focus groups were used to study the travel choices of residents of thesix case study neighborhoods. The results suggest that the role of urban form plays in travelbehavior is not entirely straightforward, sometimes influencing travel choices directly, sometimesindirectly, sometimes influencing choices in the short term, sometimes in the long term, andsometimes not having any measurable influence on choices at all. In the end, it appears that certainland use policies can help to provide alternatives to driving, but that the reduction in driving is likelyto be small.

4.5 Florida Sustainable Communities Network (FSCN) INDEX Software

The Florida Sustainable Communities Network (FSCN) INDEX software is the result of thecollaboration between the Florida Department of Community Affairs (DCA) and CriterionPlanners/Engineers, Inc. (Criterion), available to city and county governments since February, 1999.Criterion designed the INDEX software for Florida Sustainable Communities Network (FSCN)utilizing GIS modeling to measure specific sustainability indicators. INDEX allows planners toestablish base-line information and to measure the impact that development projects will have ona community. City and county governments participating in the DCA’s Sustainable CommunitiesNetwork are encouraged to use this software as a tool to help them measure progress in their effortsto attain sustainability goals.

Indicator scores are calculated for any given community to review current conditions and to trackfuture changes and trends. Criterion's initial model includes 25 FSCN "Starter" indicators(communities are free to add indicators as they see necessary and as data collection allows) as shownin Table 4.2.

Table 4.2 Initial Indicators of FSCN INDEX SoftwareCommunity

ElementIndicator Definition

Land-Use

Urban area footprint Total community land area in acres per resident

Infill Percent of building permits issued annually onproperty platted more than five years prior tobuilding permitting

Use mix Dissimilarity among one-acre grid cells containingpredominant land use

Use balance Proportional balance of land area among uses

Land redeveloped Percent of designated land area redeveloped peryear

Jobs/housing balance Ration of jobs to dwelling units

CommunityElement

Indicator Definition

22

Conservation

Natural areas protection Percent of total land area protected as natural areaor equivalent

Agricultural land conversion Acres of agricultural land urbanized per year

Imperviousness Percent of total land area covered by impervioussurfaces

Open space protection Percent of total land area dedicated to open space

Housing

Density Dwelling units per net acre of land designated forresidential uses

Affordability Ratio of average house sale price versus an"affordable price" using 25% of average householdincome and conventional financing terms

Transit proximity Average travel distance from dwellings to closesttransit stop in feet

Employment

Density Number of employees per net acre of landdesignated for employment uses

Land supply Percent of employment-designated lands that arevacant or redevelopable

Transit proximity Average travel distance from businesses to closesttransit stop in feet

Transportation

Transit mode share Percent of all person trips made by transit modes*

Bicycle network coverage Percent of total street centerline miles withdesignated bike routes

Pedestrian network coverage Percent of total street centerline miles withimproved sidewalks

Street connectivity Ratio of street intersections versus intersections andcul-de-sacs

Transit service density Index of miles of transit routes multiplied bynumber of transit vehicles traveling those routeseach day, divided by total land area

Auto use Auto vehicle miles traveled per capita per day*

Walk/bike mode share Percent of all person-trips made by walk/bikemodes*

Facilities &Services

Water consumption Residential water use in gallons per capita per day

Park space availability Acres of park and school yards per 1,000 residents

* From FSUTMS

23

The model built on these data is meant to provide government agencies with periodiccommunity-wide indicator reports as well as to allow them to evaluate the incremental developmentthat may have occurred within each period. The model can be used for broad-scale, comprehensiveplanning on areas that are 500 acres or smaller, for an intermediate-scale neighborhood masterplanning, and for site-specific, permit-level analysis in site planning, from one to 200 acres. Baselineinformation provides benchmarks by which future trends and progress can be measured, andpotential impacts of alternative development projects on any given community can be illustrated.Results are displayed geographically to show the status of any given indicator, whether there hasbeen improvement or decline.

Both the Orlando and Tampa Planning Departments assessed the INDEX software favorably.Orlando, having worked with the model early on in the implementation of the FSCN, increased the26 "starter" indicators to 95. They are mainly interested in using the software at the citywide andneighborhood levels, and expect to be able to track individual changes in land-use and transportationpolicies in terms of the sum of their effects on the city. Overall, Tampa also gave the software highratings. However, they found that the investment in data collection, the limitations with respect tothe platform (the Department uses MapInfo for their GIS needs but INDEX only works in ArcView),the inconsistencies in the data produced by INDEX that could not be accounted for, and the lack ofmeaningful results for eight of the 26 indicators were substantial difficulties in the program. Despitethese problems, they found that "the performance of the INDEX Template provided significantbenefits that should reduce costs and time requirements over the long-run. It is a worthwhileplanning tool" (Tampa Planning & Management Department, November 1999: page ii). Ultimately,they found that the maps generated by INDEX were valuable in that they communicated the ideasconcisely and identified several areas that warranted further examination.

Criterion is a private company that modified this tool specifically for Florida; however, the tool hasbeen developed for use across the nation. While there are many similarities between INDEX andVOLUTI in terms of land use indicators used and being GIS based, there are several main differencesbetween the two: (1) INDEX is a customized planning tool developed for individual communities.To use INDEX, Criterion’s service is required to set up the program and develop the applications.VOLUTI, on the other hand, is designed as a somewhat generic tool that may be applied by anyone,given that the necessary data are available; (2) INDEX is designed for area or community planningwith area size ranging from specific sites to 500 acres while VOLUTI is design for both small andlarge areas; (3) While INDEX may be linked to a travel demand model, e.g. it uses model output todisplay mode shares and per capita VMT, its focus is on land use planning. VOLUTI emphasizeslinkage between land use and transportation and therefore travel demand models have a muchstronger role.

4.6 Land Use Planning Models

There have been many land use models developed for land use forecasting purposes. Oryani et al.(1998) classified land use models into four groups: Lowry and Lowry Derivative Models,optimization models, econometric-regression models, and economically-based land use market

24

models. The Lowry and Lowry Derivative Models are based on the theory proposed by Lowry inhis work “Model of Metropolis” (1964). The basis of the models is the assumption that, everythingelse being equal, place of employment determines place of residence. Constrained by regionalemployment and population totals, the model will allocate residence population close to non-servicetype of work places then allocates service employment to serve the population, which in turn requiresthe allocation of more residence for the service employees. TOMM model (Time OrientedMetropolitan Model), MEPLAN, and DRAM model (Disaggregated Residential Allocation Model)all belong to this group.

The optimization models are based on the idea that urban developments on new lands occur with the“goal” of minimizing transportation costs and development costs. The models employ mathematicalprogramming techniques for optimization of these costs. Examples of models include TOPAZ(Technique for Optimum Placement of Activities into Zones) (Oryani 1987), Herbert-Stevens’ model(1960), and models developed by Boyce (see Putman 1979).

The econometric regression models are built upon econometric models. EMPIRIC, developed byHill et al. (see Putman 1979) combined regression analysis with simultaneous equations to estimatemodel coefficients using existing land uses.

The last group of models are based on economics and markets. These models emphasize the locationof housing and trade-offs between travel distance, density, and amenities. The National Bureau ofEconomic Research (NBER) model belongs to this group of models.

A land use model that has been adopted by a number of metropolitan planning organizations inFlorida is ULAM (Urban Land use Allocation Model) developed by Transportation PlanningServices, Inc., 1998, at the direction of an advisory group of representatives from five counties inFDOT District IV service area. ULAM uses county-wide population and employment control totalsand concurrency requirements to constrain growth, and automatically allocate growth to trafficanalysis zones (TAZs) based on availability of vacant land, developable land, maximum allowablegrowth in each zone, allowable density, historical growth trend, etc. The link to transportation inULAM is a travel factor determined for each TAZ based on free-flow travel time from FSUTMS.With perhaps some enhancement, ULAM will be able to offer the possibility to explore policies thathelp shape land uses through transportation investments.

4.7 Visualization Programs for Land Use and Transportation

Envision Sustainable Tools developed a software called QuestTM (http://www.envisiontools.com)for the purpose of supporting sustainable development through education. It is designed as aneducational program to illustrate what sustainability is and how to achieve it. Six aspects or relevantperspectives of sustainability are examined:

1. World view: it may be set to one of the four: pessimistic, technology fix, optimistic, and socialchange.

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2. Politics: different political setting including undecided, command and control, market based, andvoluntary may be chosen, and emphasis on three political approaches (carrots, sticks,information) may be specified.

3. Priorities: ecological priorities, social priorities, and economic priorities may be specified, andconsequences will be displayed

4. Population goals and targets: future population growth may be set to high growth, current trend,leveling off, decrease, and custom.

5. Economic goals and targets: high growth, current trend, leveling off, decrease, and custom arefive goals of economic growth, while GDP and annual growth rate are used as growth targets.

6. Land use: the following scenarios may be chosen. They are suburban expansion, urbandensification, mixed growth, and no change.

Different scenarios will be evaluated and results of qualitative and quantitative analyses are said tobe displayed.

Harrelson et al. (1998) developed a visualization tool for the purpose of evaluating redevelopmentstrategies for the Myrtle Beach Air Force Base. The GIS data were created from one-meterresolution satellite imagery, with ground truthing using GPS and in situ data. The visualization toolis built with World Construction Set (WCS) Version 4, a proprietary software package by QuestarProduction (http://www.3dnature.com/index.html) in Brighton, Colorado. The software is capableof creating photorealistic terrain modeling, rendering, and animation. The application can renderGIS features such as roads, wetland boundaries, forested wetlands, and vegetation, and can populateterrains with sparse trees, tree stands, or dense woods.

While WCS is a powerful 3D modeling tool, it is proprietary and requires a great deal of skill to use.It is suitable for small area applications such as site development projects. The cost will be high ifit is to be applied to a large area or at a regional scale.

Table 4.3 lists some 3D visualization software packages, most are designed for forestry andlandscape applications.

An alternative approach to virtual reality modeling is to combine geometric models withphotographs, which eliminates the need to produce realistic surfaces and material rendering. TheUrban Simulation team at the University of Los Angeles is in the process of creating a virtual modelof the entire Los Angeles basin (http://www.aud.ucla.edu/proj/usim.htm).

Jha and McCall (2001) described various states-of-the-art of visualization technologies including2D overlay of orthophotos on maps, 3D visualization with geometric models, 4D visualization withanimated geometric models, surface and terrain models, drape of orthophoto onto terrain,photo-simulation that uses photographs instead of 3D geometric models and rendering, animationof a series of image frames, and real-time virtual reality and simulation. The authors pointed out that3D geometric modeling of a simple street scene could take 2-3 months of work and would involveintensive computation, while painting photographs over simple 3D models will reduce the work to

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Figure 4.1 CAD Drawing Overlay on Photographs(from Jha and McCall 2001)

2 weeks. 3D modeling effort may be reduced by using predefined 2D and 3D geometric objectscreated in CAD software. Figure 4.1 illustrates a scene created using photo-rendering. This kindof visualization will be tremendously helpful with public involvement.

Another study of using 3D modeling techniques to produce images of roads and bridges for publicinvolvement is reported in Wallsgrove and Barlow (2001). They created street scenes using CADsuch as MicroStation and 3D Studio.

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Table 4.3 Software Packages for 3D Modeling and Visualization (from McGaughey 1997)Software Package Visualization Technique Scale OS Cost Additional Information Reference

Standard Visaulization System (SVS) geometric modeling plot DOS Free

UTOOLS and UVIEW geometric modeling stand orlandscape

DOS Free

SmartForest geometric modeling stand orlandscape

UNIX Free

Landscape Management Systems (LMS) geometric modeling all scales Windows Free

Gnu Image Manipulation Program (GIMP) video imaging all scales UNIX Free

USFS, Southern Research Stationvisualization system

geometric modeling stand orlandscape

UNIX Free

Persistence of vision rayytacer (POV-RAY)

geometric modeling all scales manyplatforms

Free

VisualFX geometric modeling stand orlandscape

DOS $$

CLRView geometric modeling stand orlandscape

IRIX Free

TrueFlile image draping landscape Windows Free, $$

Visual Explorer geometric modeling andimage draping

landscape Windows Free, $$

VsitaPro3 geometric modeling andimage draping

landscape Windows,Mac

$$

World Construction Set geometric modeling andimage draping

all scales Windows,Mac

$$

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5. USER FEEDBACK OF THE PROTOTYPE VOLUTI

To collect feedback on the first VOLUTI implementation attempt, a number of urban ortransportation planners were interviewed. Presentations have also been made at a number ofconferences, including the 1999 September Annual Meeting of the South Florida Section ofAmerican Planning Association held at Miami Beach, Florida, the GIS showcase sponsored by theFIU library GIS lab in February 2001, the Florida Model Application Conference held in May 2000in Clearwater, Florida, the Symposium of GIS-T 2001 held in April 2001 in Crystal City, Virginia,and the TRB 8th Conference on Application of Planning Methods held in Corpus Christi, Texas inApril 2001. The responses have been all positive and enthusiastic. Many planners requested for thepresentation materials and papers. Many commented that the tool filled a gap and would be usefulfor them. A number of people also requested for the software.

Locally, private demonstrations have been made to Mr. Fabian Cevallos of Broward Transit, FrankBaron and Susan Schreiber of Miami-Dade County Metropolitan Planning Organization, and Mr.David Dahlstrom of the South Florida Planning Council. Many useful suggestions are made and arebriefly summarized below.

Aggregate vacant lands by ownership. It has been suggested to examine the ownership of vacantlands so that land that is divided into small parcels but owned by a single entity may be identifiedfor large development projects. This suggestion has been implemented.

Simultaneous consideration of existing land use, zoning, and accessibility to transportation facilitiesto determine the potential of development projects. An example of this is that if the zoning of aparcel is estate residential and the parcel is near a principal arterial, it may be considered asinappropriately zoned resulting in an inefficient use of the land. In current VOLUTI implementation,a comprehensive evaluation of land use development potential is not supported. Instead, all theinformation necessary for this purpose is available in VOLUTI but the user needs to obtain the factsthrough several queries. Future effort may include providing more integrated and comprehensiveanalysis tools.

Evaluation of transportation project impact on land use by conducting a before-and-after study ofa major transportation improvement project. Such studies not only will be beneficial from the pointview of understanding land use and transportation interaction, but also are necessary to improve theaccuracy of site impact analysis procedures. In this implementation of VOLUTI, traffic volumes andratios of volume to capacity before and after a land development project can be generated using theFSUTMS model and displayed. However, the real impact of development projects have not beenstudied extensively or in detail.

Providing additional information on tax base along with land use composition. Since increasing taxbase is one of the objectives of many public officials involved in making land developmentdecisions, this information may be helpful in identifying and evaluating alternatives for increasing

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tax revenue. This information may also be used to promote mixed land use where land use is mostlyresidential. This information has been added in VOLUTI.

Providing information on availability of office floor space, retail floor space, and industrial floorspace. Such information is a more accurate measure of market supply. Current VOLUTIimplementation utilizes the property tax database and provides floor space information for office,commercial, and industrial uses.

Addition of water and sewer information to public facility queries. Existence of water and sewerinfrastructure affects decisions on land development since they represent a significant costcomponent. The water and sewer line information has been obtained from the Miami-Dade CountyWater and Sewer Department, and the information has been incorporated into VOLUTI.

Display of ease of access from other locations such as downtown to a specific location or area. Thishas been implemented in VOLUTI. The information includes travel times by different modesbetween TAZs as well as a measure of accessibility of different locations based on a combinationof ease of travel and opportunities represented as employment size.

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6. DATA COLLECTION AND PROCESSING

The data collection effort has been much more extensive than that in the previous VOLUTIimplementation. Beyond what have been collected for the first implementation of VOLUTI,additional data are collected and a great deal of processing is involved. This section describe thecollection and processing of these data. Due to the amount of information, a complete descriptionof the data used in VOLUTI 2.0 is given in file named metadata.doc on the VOLUTI distributionCD.

6.1 Property Tax Databases

The property tax appraiser’s database was obtained from the Information Technology Division (ITD)of Miami-Dade County. It is updated annually. First, the new fiscal year’s assessments and otherchanges in properties such as new sales are added to the database, which results in its containingassessed values for three years (current year or new fiscal year, the previous year, and the last year,which is the year before the “previous year”). The database is then submitted to Tallahassee forapproval. Upon approval, the information on assessed values for the last year is removed from thedatabase. Therefore, the final version of the updated database contains only two years of assessmentinformation. Therefore, there is a time window in which the database contains three years ofassessment data. Although at the time when the database was acquired, the current year assessmentdata are pending approval and are subject to modifications, the potential changes will be insignificantaccording to the Public Access Section of ITD.

The property tax appraiser’s database used in VOLUTI 2.0 has been obtained form ITD and containsonly two years of assessment data. The information utilized includes the following:

Folio number a unique property record identifier of 13 digits.Current land value 2000-2001 assessed land value. Note that this value is not the market

value, but reflects the market value.Current building value 2000-2001 assessed building value. Current total value 2000-2001 assessed total value. This value may not be the sum of the

land and building values, since some properties such as condos do nothave land values.

Previous land value 1999-2000 assessed land value.Previous building value 1999-2000 assessed building value. Previous total value 1999-2000 assessed total value. Previous sales price the selling price of the most recent sale of the property.Previous sales year the year in which the property was last sold.County land use code a numerical code indicating the land uses. Some examples of

possible land uses are single family, duplex, apartments, lightindustrial, office, commercial, etc.

Lot size the lot size in square feet. The numbers are expressed as integerswith the last two digits being the fractions. A new column is

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therefore created that expresses the values as the actual squarefootage using floating point numbers.

Building square footage the building area in square feet.

Data definition for the property tax appraiser’s database does not currently exist. This lack ofmetadata results in a significant amount of effort in determining the database structure and themeanings of the fields, especially since the original data were in ASCII file format and must beconverted to a database base format.

6.2 Parcel GIS Data

The parcel GIS data were obtained from the City of Miami Planning Department. The data do notinclude all the parcels, but most parcels in the Overtown area are in the data set. The GIS parcel datacreated and owned by Florida Power and Light (FP&L) are both spatially actual and complete ingeographic coverage. However, since it is proprietary and unavailable to the city or FDOT, the datamust be purchased and the cost was found to be too high for this project.

The parcel data provided by the City of Miami include information on portfolio number, 1994 landuse code, and a condition code of A (excellent), B (good), C (fair), V (vacant), VSB (vacant withstructure in good condition), VSC (vacant with structure in fair condition), VSD (vacant withstructure in poor condition), or PL. The rating was assigned by the City of Miami staff based onfield visits to and visual inspection of the properties. However, not all properties have a rating.

6.3 Zoning Data

The zoning map is obtained from the City of Miami Planning Department. It include zonesrepresented as polygons and land use code, atlas sheet number, zoning district number, and specialdistrict number. The zoning codes used by the city are different from those used by the county.There are 17 zoning codes:

Code Zoning Type0 SP 1 R-1 (single family)2 R-2 (duplex)3 R-3 (multifamily low density)4 R-4 (multifamily high density)11 C-1 (restricted commercial)12 C-2 (liberal commercial)13 CBD (central business district)25 O (office)35 G/I (government/institutional)45 I (industrial)55 RT (Rapid Transit)

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81 PR (parks/recreation)82 CS (conservation)97 EXP (expressway)98 RR (rail road)99 Not defined

6.4 Land Use Data

Land use data are obtained from ITD. The data are created from the FP&L’s parcel maps based onthe land use code. Contiguous parcels of the same land use codes are aggregated to arrive atpolygons that have a single land use. The land use data are dated 1994 and 1998, respectively. Theland uses represented include the following:

Code Land Use Type10, 11, 13 single family12 townhouses20 two-family (duplexes)30, 35, 50 migrant camps61 mobile home parks101, 110, 112, 113, 115 shopping centers, commercial, office,

stadiums, tracks200 hotels and motels310, 320, 339, 340 359, 370 industrial411, 412, 420, 430, 440, 450, 451, 460, 470 institutional510, 516 ~ 519, 527, 530, 550, 560 ~ 562580 parks570 water conservation area540 cemeteries610 ~ 612 airports/ports640 ~ 642 streets/roads, expressways, ramps613 ~ 637, 650 ~ 670 communications, utilities, terminals, plants710 ~ 790 agriculture801 ~ 805 vacant911 ~ 936 water

Multiple codes for the same type of general land use type indicate subcategories that provide moredetails on the actual land uses. For instance, single family has two subcategories reflecting differentdensities, and institutional has ten subcategories such as public and private schools, colleges anduniversities, cultural centers, hospitals, nursing homes, government and administration, military,prison, etc.

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6.5 Employment data

In VOLUTI, employment data that include employment type and employment size by businesslocation were utilized. The data were purchased from InfoUSA in 1999. The data are used increating local accessibility to essential service indices. The original data were in ASCII format andwere converted to dBase 4 format and geocoded based on the Miami-Dade County 1999 street map.The database contains information on the establishment’s name, street address, SIC code, andemployment size at the address. The data are not included in VOLUTI due to a copyright agreement.

6.6 Water and Sewer Information

In the last several years, the Miami-Dade County Water and Sewer Department has collected dataon water and sewer infrastructure using aerial photography and global positioning system (GPS)technologies. The project is not yet completed at the time of this report. While data collection hasbeen finished, creation of GIS maps and quality control and quality assurance are still underway.One of the fields is the diameter of the water/sewer mains in inches. This information may be usefulwhen determining if water and sewer lines are adequate for a proposed land use. Metadata are notavailable.

6.7 Public Facilities

In addition to water and sewer information, information on other public facilities is also included.This includes libraries, public schools, private schools, colleges and universities, daycare centers,hospitals, nursing homes, hurricane shelters, fire stations, and parks. The information includes thelocation and type of facility. All data are obtained from Miami-Dade County ITD.

6.8 Transportation Facilities

Streets and Roadways

Two roadway GIS databases from Miami-Dade County are used. One includes only major roads,mainly all the expressways, arterials, and collectors. The main attribute information that is usefulis the functional classifications of roadways. The functional classifications reflect the hierarchicalnature of the roadway system, with some collecting traffic from the local area and some serving asmajor through routes. The functional classes of the roadways in this map include limited accesshighways, major arterials, minor arterials, and collectors.

The other GIS data set is a detailed street map that includes all roadways including local streets. Themajor road GIS layer serves as a background in VOLUTI to orient the user, and are also used inbuffer analysis (see Section 11.4). The detailed street map is not shown but may be displayed. It isalso used for geocoding purpose such as locating a place by its address (see Section 11.2).

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State Road Inventory Data

FDOT District 6 completed a GIS pilot project in 1999. The project involved the implementationof a GIS that includes most of the planning data such as the Roadway Condition Inventory (RCI)data, base map, transportation projects, etc. The following data have been received form FDOTDistrict 6 Planning and Statistic Administration Office:

• Base map. This map contains all the federal and state roads in FDOT District 6, which hasthe jurisdiction over Miami-Dade County and Leon County. In the study area, there arerelatively few state roads.

• 1999 Average Annual Daily Traffic (AADT). AADT is defined as the total number ofvehicles traveled on a road during one year divided by the number of days in that year. It iseither obtained from permanent traffic counters or estimated based on short period countssuch as 24-hour or 48-hour counts. When AADT is estimated, variations in traffic volumesduring a day, during a week, and in different seasons are accounted for. AADT is computedfor both directions and is a good indicator of traffic volume on a road.

• Roadway levels of service. Level of service is a qualitative assessment of road users'perceptions of the quality of traffic flow on a roadway. Letters A through F are used todesignate the six levels of service. A generally represents the most favorable conditions, andF the least favorable conditions. Levels of service are determined according to the FDOTLevel of Service Handbook, last updated in 1998 and in effective since March 1999. Themethods used are based on the 1997 update Highway Capacity Manual and a computerprogram is used to determine LOS for all state roads. Prior to March 1999, 1995 Level ofService Handbook was used as the basis for determining LOS, which also employs themethodology from the Highway Capacity Manual. The LOS data used for this project is forthe year of 1999.

• Number of lanes. The number of lanes of a roadway is for both directions. It is an importantfactor that determines the roadway capacity.

All the data except the base map are created using the dynamic segmentation function of Arc/Info.Each set of data is stored as a database table with each record containing information about thebeginning mile post, the ending mile post, and the attribute data. An event theme can then begenerated in ArcView, which has the same appearance of the base map but the attribute values canbe properly displayed.

Transit Services

The transit routes, bus stops, and rail stations were obtained from ITD as part of the public accessGIS CD. However, the bus route and bus stop data have been outdated. According to the Miami-Dade Transit Agency (MDTA), many errors exist in the bus stop database. These databases are still

35

used in the current VOLUTI version, but should be replaced when the updated databases becomeavailable. MDTA has been seeking to update the bus stop data but there has not been a set date, yet.

Railroad Tracks

Railroad tracks are obtained from a CD produced for the Florida High-Speed Rail EnvironmentalEvaluation.

6.9 Traffic Analysis Zones

VOLUTI uses two traffic analysis zone (TAZ) structures, one of 1990 and the other of 2000. TAZsare used as the basic geographic units for analyses related to traffic and transportation, as well as inthe FSUTMS models. The TAZ structures are used in VOLUTI to display FSUTMS model relateddemographic, socioeconomic, and land use information. The 1990 and 2000 TAZ structures aredifferent. Therefore to display changes in, e.g., population, will require the establishment of thecorrespondence between the zones in the two structures. For this project, the correspondence isestablished only for the study area as that for the entire county will require a significant effort.

6.10 Photographs

Site photographs are collected for this project. For different locations, multiple photographs maybe collected, some of which have been used to construct 360° panoramas using a shareware, whichis free for government and nonprofit organizations, by PixAround.com(http://www.PixAround.com).

6.11 Aerial Photos

Two types of aerial photographs are used in VOLUTI, the one-meter resolution false-color infrareddigital orthophotos from the United State Geological Survey (USGS), and the one-foot resolutionblack-and-white digital orthophotos from Miami-Dade County.

A conventional aerial photograph contains image displacements caused by camera lens distortion,camera tip and tilt, terrain relief, and scale. An aerial photograph does not have a uniform scale,therefore, is not a map. A digital orthophoto is a computer generated image of an aerial photographin which displacements caused by camera orientation and terrain have been removed through arectification process. It is a uniform scale photographic image and can be considered a photographicmap. The uniform scale of a digital orthophoto makes it possible to determine map measurementsor to overlay information, using the digital orthophoto as a base map. Features are represented intheir true ground position, making direct measurement of distance, areas, angles, and positionspossible. This makes the digital orthophoto valuable as a layer in a GIS or as a tool for revision ofother map materials such as base maps and topographic maps.

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Figure 6.1 Detail of a Digital Orthophoto QuarterQuadrangle

The one-meter resolution digital orthophotos are the product of the National Digital OrthophotoProgram (NDOP). The USGS, the U.S. Department of Agriculture's Farm Service Agency, the U.S.Department of Agriculture's Natural Resources Conservation Service, and the U.S. Forest Serviceare partners in this program. The primary goal of the program is to ensure the public domainavailability of digital orthophoto quadrangle (DOQ) data for the Nation.

The primary imagery source for digital orthophotos is the NAPP photographs and NAPP-likephotography. The NAPP photography is quarter-quadrangle centered (3.75 minutes of latitude by3.75 minutes of longitude in geographic extent) and taken at an aircraft altitude of approximately20,000 feet above mean terrain, using a 152-millimeter focal-length camera. The NAPP photographyis approximately 1:40,000 scale. The NAPP photographs are used in the production of one-meterDOQs. National High Altitude Photography (NHAP) program black-and-white photography(1:80,000 scale) or NHAP-like photography is used in production of two-meter DOQs (7.5 minutesof latitude by 7.5 minutes of longitude in geographic extent).

Aerial photos have been produced for the lower 48 states as part of NAPP and will be continuallyupdated. The NAPP program has had three cycles during which various states were flown over tobe photographed. Cycle 1 covered 1987 to 1991. Florida was not included in the NAPP during thiscycle, therefore no DOQs were available for Florida for this period of time. Cycle 2 of the programcovered the period of 1992 to 1996. Florida was flown over in 1994. The third cycle covers theperiod of 1997 to 2003. Florida is scheduled for 2000 for a new update. According to USGS, after2003, the aerial photos will be updated on a10-year cycle for most areas, and on a 5-yearcycle in areas where land use change is morerapid. For more information on the NAPP,readers are referred to the USGS web site athttp://edcwww.cr.usgs.gov/dsprod/prod.html.

The one-meter DOQs have a groundresolution of one meter or 3.28 feet. It canshow great detail of features includingbuildings, sidewalks, roads, cars, etc. Figure6.1 illustrates the details contained in thephotograph. A DOQ covers one-fourth of aUSGS 7.5 minute quadrangle area. Sincethe size of USGS quadrangles vary withlatitude, the size of DOQs also changeaccordingly. The DOQs obtained for thisproject cover approximately an area of 6mile by 6 mile. The DOQs overlap eachother near the edge.

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Figure 6.2 One-Meter Color InfraredDigital Orthophoto from the USGS

Figure 6.3 One-Foot Digital Orthophotofrom Miami-Dade County

The DOQs are large data sets. The infrared DOQ files used for this project have a size of about 150megabytes each (quarter quadrangle). To speed up the display, each digital orthophotos quarterquadrangle is divided into 16 smaller patches and converted to the JPEG format, resulting in areduction of the size of the data sets by 57 times. An image catalog is then constructed to specifythe coordinates of each of the small patches. ArcView si able to use the information from the imagecatalog to reconstruct a seamless imagery depending on the scale of the display.

Color infrared DOQs may be obtained directly from USGS, which is the lead federal agency for thecollection and distribution of digital cartographic data. The costs include a $45 base charge, witha $15.00 charge for each DOQ. Black-and white DOQs cost $7.50 but they are not available forFlorida. FDOT District 6 has obtained 1994 DOQs for the entire county.

The one-foot resolution black-and-white aerial photos produced by the Miami-Dade County werealso digital orthophotos. The photographs are taken in 1998 and 1999. Although they are black-and-white, their high resolution makes them highly desirable. Figures 6.2 and 6.3 illustrate the differencein the resolution in the two types of aerial photographs, respectively.

6.12 Demographic and Socioeconomic Data

The demographic and socioeconomic data come from two sources: the 1990 census data and the1999 FSUTMS ZDATA1 and ZDATA2 files. The census data included in VOLUTI are population,housing units, vacant housing units, medium rent, and population density. The 1990 census blockgroup polygons are used to displayed the census data. The block group polygons are available fromthe Census Bureau.

The data from the FSUTMS ZDATA1 and ZDATA2 files are estimated by the Miami-Dade CountyPlanning Department for the year of 1999 and obtained through the Miami-Dade County MPO, who

38

is responsible for updating FSUTMS model. The data that may be displayed include population,population under the age of 16, population between the age of 16 and 65, population over the ageof 65, single-family population, multi-family population, service employment, commercialemployment, industrial employment, and school enrollment. Population and employment densitiesare derived from the two data sets, respectively. The data may be displayed by 2000 TAZ.

The same types of data from 1990 are also available, and are used in VOLUTI to measure changesin these variables between the year of 1990 and 1999. Such longitudinal data are useful forexamining the trends in demographic and socioeconomic changes.

6.13 Environmental Data

Environmental data were obtained from ITD and the Miami-Dade County Department ofEnvironment and Resources Management (DERM). They include lakes, canals, shorelines, floodzones, public well protection areas, trash centers and land fills, solid waste facilities, petroleumcontamination sites, toxic release points, national priority list sites, and Florida State funded wastesites.

39

Cell Entropyp p

Ln Jj j

j

J

� �

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�ln( )

( )1

Cell mean entropy

P PJ

K

jk jk

j

k� �

�

��

ln( )ln( )

Zonal mean entropycell entropy of zone i

Ni

N

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7. DEVELOPMENT OF LAND USE INDICATORS

A number of land use indicators are developed. They include land use mix, and job/housing balance.These indicators are described here.

7.1 Land Use Mix

The entropy measure introduced in Chapter 4 may be influenced by the zone size if an index is tobe generated for zones such as TAZs. In VOLUTI, the mean entropy is used to measure the land usemix. It is defined similar to that in (Kockelman 1997), which involves dividing a zone into grid cellsand averaging the entropy indices of the center cell and the cells surrounding it within a certaindistance. Kockelman used one-hectare (328 feet by 328 feet) cells and averaged cell entropy indiceswithin a 0.8-kilometer (or 0.5-mile) radius to calculate the mean entropy of a zone. In VOLUTI, thegrid cell size is 448 feet (or 1/8 of a mile) and nine cells are used for averaging the entropy indicesto arrive at one for the center cell (see illustration right below). This is based on the idea that mixedland uses tend to encourage more walk trips, which more often occur for short distance travels. Themean entropy of each TAZ is then computed by averaging the entropy values of all the cells withinthe zone:

where Pj = the proportion of land development of the jth type J = the number of different types of land development, which include residential,

commercial, public, offices and research sites, industrial, and parks. J = 6 inVOLUTI.

K = the number of actively developed cells in a TAZ. Pjk = the proportion of land use type j within a 0.8-km radius of developed area

surrounding the kth hectare.N = the number of cells in a TAZ.

The land use data used in the calculation are the 1998 land use.

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7.2 Job/Housing Balance

This is the ratio of total employment by total households in each TAZ. A low ratio indicates apredominantly residential area. A large ratio greater than 1.5 may indicate a predominatelynonresidential area. The data used to calculate this index are the 1999 TAZ-based employment andhousing data developed as part of the ongoing FSUTMS model update effort.

7.3 Average Parcel Size by TAZ

Average parcel size is an indicator of land use development intensity and potential for furtherdevelopment. In urban areas where high density development has occurred, the parcel sizes tend tobe small. Large parcel sizes are an indicator that land use may be intensified by further subdividingthe parcels therefore increasing the density. The average parcel size is calculated using the parcelGIS data provided by the City of Miami and the 2000 Miami-Dade County Property Tax database.

7.4 Open Space

This measure is the park acreage per 1,000 residents by TAZ. The City of Miami defines theacceptable level of service standard with regards to recreation and open space as a minimum of 1.3acres of public park space per 1,000 residents (City of Miami Planning Department 1993). Thismeasure is computed by dividing the acreage of park areas by the population (in thousands) in aTAZ, and reflects the adequacy of recreational space. The population data are the 1999 populationestimate for 2000 TAZ structure. The park information is from the 1998 land use data by Miami-Dade County ITD. However, the park data include only county parks.

7.5 Land Use Change

Land use changes are calculated for each TAZ between 1994 and 1998 by the following 15 land usecategories:

AgricultureAirports/PortsCemeteriesCommunications, Utilities, Terminals, PlantsIndustrialInstitutionalMulti-FamilyOfficeParks (Including Preserves & Conservation)Shopping Centers, Commercial, Stadiums, TracksSingle-FamilyStreets/Roads, Expressways, RampsTransient-Residential (Hotels/Motels)VacantWater

41

Figure 7.1 Dialog Box forDisplaying Land Use Change

Figure 7.2 Change in Single Family Land Use (1994 -1998)

Figure 7.3 Change in Single Family Dwelling Units(1990 - 1999)

Both 1994 and 1998 land use data are obtained from the Miami-Dade County ITD. For any userselected land use type (see Figure 7.1), VOLUTI will display the percentage increase or decrease ofthe total area of that particular land use in each zone. Figure 7.2 indicates that between 1994 and1998, there has been a slight increase in single family uses in the Overtown area, although in twoadjacent zones there have a decline in single family uses. This increase may not necessarily meanan increase in new single family housing construction, however. It may have reflected changes inzoning. This is better understood if the actual change in dwelling units is examined (see the nextsubsection).

7.6 Changes in Population, Employment, and Dwelling Units by TAZs

Changes in total population, total employment, single family dwelling units, and multi-familydwelling units for each TAZ between 1990 and 1999 may be queried. The data sources are the 1990and 1999 socioeconomic data for the travel demand models - Miami-Dade County FSUTMS models,estimated by the Miami-Dade County Planning Department.

Figure 7.3 shows the dwelling unitchange in the Overtown and vicinityarea. Although it appears that there hasbeen an increase in single family landuse according to the map in Figure 7.3,Figure 7.3 actually indicates a declinein single family dwelling units in thearea.

Because the display of changes indwelling units involves the use of both1990 and 2000 TAZ structures, and thetwo TAZ structure are different, arelationship must be established

42

between the two. The 2000 TAZ structure has added about 200 zones bringing the total to 1423zones. While the majority of the zone boundaries remained the same, the boundaries of many havebeen modified. Additionally, because of a further refinement of the coordinates to reflect moreaccurate boundaries, some of the boundary lines are also changed. As a result, establishing thecorrespondence between the two TAZ structures became more complicated due to the “slivers” fromsimply overlaying the two TAZ structures, rendering an automated process for determining if a zonehas been split or combined with another by examining changes in its boundaries inadequate.Therefore, in this implementation of VOLUTI, only the 1990 and 2000 TAZs in the study area arematched. Matching the TAZ structures over the entire county will require considerable effort, andis not accomplished for this demonstration project.

7.7 Tax Base

Queries on land use composition for any user defined area, specified by the user by drawing arectangle on the map, result in a pie chart showing the percentages of different land uses in thespecified area. Along with the land use composition, another pie chart displays the share of taxcontributions from different land uses. Such information is often used by public elected officials inconsideration of approval of new development projects with the goal of increasing the tax base.

43

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8. ACCESSIBILITY AND MOBILITY EVALUATION

Several indices have been developed to measure accessibility and mobility. The difference betweenthe concepts of accessibility and mobility is that the former considers travel as a derived need for thepurpose of carrying out economic and social activities and the location of activity centers will affecttravel demand, while the latter treats travel as an activity in itself without considering why it occurs.The accessibility concept implies that if opportunities such as shopping and employment are withineasy reach by, for example, non-motorized modes, then good mobility on highways becomesirrelevant. This concept therefore supports the theory of mixed land uses as a means to reduce traveldemand. Mobility, on the other hand, disregards the land use effects on travel demand and simplyconsiders the ability to move vehicles as a measure of accessibility.

In the current version of VOLUTI, two sets of measurements have been developed. The accessibilitymeasures include regional accessibility by highway and transit travel modes, respectively, and localaccessibility to essential services. The mobility measures include highway travel time contours,transit travel time contours by transit modes, transit-highway travel time difference, transit traveltime contours based on all modes, and transit transfers. These measures are described in detail inthe subsequent subsections.

8.1 Regional Accessibility by Highway

Regional accessibility by highway mode measures the accessibility to opportunities in a regionassuming driving as the travel mode. The opportunities may be employment or population (laborforce). Accessibility has been recognized as one of the most important factors that affect both landuse and travel behavior. In VOLUTI, the regional accessibility to employment and population byautomobile is similarly defined as relative indices expressed as a number between 0 and 100:

where RAEHi = index of regional accessibility to employment by highway travel for zone iRAPHi = index of regional accessibility to population by highway travel for zone iEmpj = total employment in zone jPopj = total population in zone jtij = shortest congested highway travel time between zone i and zone jN = the number of zones

The travel times are obtained from the 1990 Miami-Dade County FSUTMS model, which includesboth highway and transit modes. The travel times are not actual travel times that occurred on the

44

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roadways. However, the indices are a relative measurements and the use of accurate actual traveltimes is not critical.

The coefficients in the formulae are obtained by fitting the highway travel times from the 1999 SouthEast Florida Travel Characteristics Study data set based on exponential functions. The employmentdata are from the FSUTMS ZDATA2.

8.2 Regional Accessibility by Transit

The indices of regional accessibility to employment and population, respectively, by transit aresimilarly defined as for the highway travel mode. The formulae are:

where RAETi = index of regional accessibility to employment by transit mode for zone iRAPTi = index of regional accessibility to population by transit mode for zone iEmpj = total employment in zone jPopj = total population in zone jtij = shortest congested transit travel time between zone i and zone jN = the number of zones

The coefficient in the exponential function is determined by fitting an exponential curve to transittrip length frequency distribution obtained from the household survey of the 1999 Southeast FloridaTravel Characteristic Study.

8.3 Local Accessibility to Essential Services

In VOLUTI, local accessibility is considered a measure of accessibility to “essential services.” These “essential services” include grocery stores, supermarkets, convenience stores (e.g., Seven-Eleven), bakeries, and drug stores. Availability of such essential services is both an indication oflocal land use mix and of potential demand on transportation facilities as none or little serviceavailability means that people will have to travel far to meet their needs instead of possibly walkingor bicycling to these destinations.

45

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Local accessibility to essential services is defined as a zonal index, computed as the ratio of the totalemployment in businesses that provide “essential services” in a zone to the zonal population, or

This measure does not consider the size of a zone (which affects the travel need) or the ease of travelwithin the zone (e.g., adequate internal roadways, and friendly pedestrian and bicycle facilities).Moreover, it ignores possible uneven distribution of households and population and the fact thatopportunities in an adjacent zone might be available to the residents.

8.4 Contours of Highway Travel Time

In VOLUTI, a user may display a contour map of highway travel time, which is produced from the1990 Miami-Dade County FSUTMS model. The model considers both highway and transit modes,and the results are the congested travel time based on the shortest paths.

The map may be updated after the user makes changes to either the transportation network (e.g.,changing roadway attributes such as number of lanes or facility types) or to land uses (through landdevelopment). However, because the current VOLUTI implementation for site impact analysis doesnot consider transit modes in the FSUTMS model, the model output of the highway travel times willbe rather different than those from a model that considers transit modes. Therefore, comparison oftravel time change between the base year conditions and after a DRI analysis is done will not beaccurate. The contour map will only provide a general sense of the relative ease of travel by cars.However, comparison of travel times between different scenarios will be consistent. This limitationalso applies to other accessibility measures discussed in this section.

8.5 Contours of Transit Travel Time by Transit Modes

Maps that display the transit travel time contour are also produced from the 1990 FSUTMS transitmodel. The travel time displayed is for individual modes in the 1990 Miami-Dade County FSUTMSmodel, which include metrorail and metromover, express buses, local buses, jitneys, HOV, anddemand responsive services. The user may choose to use the transit travel time with or withoutpenalties. Penalties are applied to reflect the cost of slower travel speed of transit, walk time, accesstime, wait time, and transfers. Because site impact analysis in VOLUTI currently does not supportthe transit model, the transit travel times cannot be updated for new land developments at present.

8.6 Shortest Transit Travel Time Contour

The shortest transit travel time is the time to traverse the shortest path between any two given zonesconsidering all transit modes. The shortest path is found for an origin zone and a destination zoneby comparing link travel times of all transit modes. The user may choose to include penalties in thecalculation, which may be applied to out-of-vehicle time (wait time, walk time, etc.) and transfers.The link travel times by different modes are from the output of the FSUTMS model.

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Figure 8.1 Dialog Box for Comparison of Transit andHighway Travel Time

8.7 Transit Transfers Required

The number of transfers required for traveling by transit is an important measure of transit servicequality. Transfers have negative impact on service quality as well as on ridership because of theinconvenience and delay involved. According to the transit onboard survey, which was a part of the1999 Southeast Florida Travel Characteristics Study, more than half of the surveyed users had oneor more transfers. Information on transfers is useful to determine areas where travel by transit isinconvenient because of transfers required. Combined with transit travel time map andsocioeconomic data, areas with inadequate transit services may be identified and possibleimprovements can be investigated.

The number of transfers is obtained by finding the shortest path between a zone pair considering thepenalty applied and then determining how many transfers have been involved.

8.8 Difference of Transit and Highway Travel Time

To evaluate transit services and identify needs for service improvements, transit and highway traveltime may be compared in a number of ways. Figure 8.1 shows the dialog box that allows a user tochoose how a comparison can beperformed. Figure 8.2 illustrates amap showing the differencebetween transit and highway traveltimes by buses during peak hourswith penalties applied to out-of-vehicle time and transfers.Apparently, transit travel time isgenerally much longer thanhighway travel time. Thedifferences in transit and highwaytravel times range between 0 to 327minutes. Large differences areusually the result of longer distancebetween two zones, long headwayof buses, and transfers required.

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Figure 8.2 Transit-Highway Travel Time Difference in Peak Hours with PenaltiesApplied to Transit

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9. DEVELOPMENT OF LAND USE SCENARIOS

In this section, the creation of land development scenarios is described. The purpose of developingthese scenarios is to test VOLUTI’s capability of evaluating the impact of land use changes on thetransportation system.

9.1 Development Models

Over the years, planners have used four general models for development in Overtown. Model 1focused on the creation of public housing and the implementation of urban renewal initiatives asdirected by federal agencies. Model 2 encouraged locals to develop their own plans with federalfinancial support in the form of Community Development Block Grants. Model 3 strived to preservethe cultural heritage of the community by restoring key older historical structures in order to attracttourism and reinvestment. Model 4 featured the construction of megastructure complex thatincluded the Miami Arena, the Metrorail station and middle-income apartment towers to stimulateeconomic spillover (Gale 1999).

Model 1 is obsolete. Model 4 failed to create jobs or encourage development (the Miami Heat gameswere relocated from the Miami Arena to the new American Airlines Arena on Biscayne Bay inMiami). Models 2 and 3 are still in effect but their efficacy depend largely on the availability offederal funding (Miami-Dade County Empowerment Trust 2000).

During the fiscal year 1999-2000 ended June 20th, 2000, the Empowerment Zone (EZ) Trustcompleted its first phase planning efforts on the Town Park Housing New Markets project inOvertown. The EZ Trust has been awarded $10 million from the Miami-Dade County HousingAgency to support this project. The Trust’s objective is to develop a mixed income, single detachedand semi-gated community in Overtown (Miami-Dade County Empowerment Trust 2000).

The Overtown Neighborhood Assembly has made a clear commitment to economic developmentby being the first Assembly to pledge 100 percent of its funds ($200,000) to the Empowerment TrustMicro-Loan Fund (ETML Fund). Applications for funding were released on July 26, 2000. Privateinvestment pledges to the ETML Fund and the number of new jobs to be created by this program hasnot been determined (Miami-Dade County Empowerment Trust 2000).

In July 1999, the Overtown Advisory Board, Eastward Ho! (which encourages redevelopment ininterior neighborhoods rather than westward) and other agencies sponsored a design charrette inOvertown. The charrette was a formal week-long brainstorming session where residents workedwith designers, town planners, and government officials to design a new Overtown. The OvertownRedevelopment Area Design Charrette Report published in March 2000 proposed the creation of acenter for Overtown and the redevelopment of commercial and entertainment districts among otherdevelopments. However, no funding was identified to make the citizen’s vision a reality (TreasureCoast Regional Planning 2000).

The scenario developments presented here are therefore not based on any specific anticipation offederal funding, but are based on methodologies generally accepted by the planning community.

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The area for which the scenarios are developed consists of census tracts 30.01, 31, 34, and 36.01,which are collectively defined by the City of Miami as the Overtown neighborhood (Gay 2000).

9.2 Population Forecast

Future land use scenarios show the projected demand for housing based on population forecasts.These forecasts are based on the net effect of low, medium, and high population projection seriesfor the years 2005, 2010, 2015, and 2020 with the year 1999 as the base year.

9.2.1 Low, Medium, and High Projection Series

The medium population projection series at the census tract level was prepared by the Miami-DadeCounty Department of Planning and Zoning. The estimates for the year 1999 were derived primarilyfrom housing unit counts from the Property Tax file. Additions were made for mobile home units,some public housing units, and other units not well accounted for by the tax file. The projectionswere based on logistic curves which were calculated for the 32 Minor Statistical Areas.

Data from the past three decennial censuses, the 1998 estimates, and the projected residentialcapacity of each area were used. Capacity estimates included some capacity outside the urbandevelopment boundary and occasional adjustments were made for redevelopment activities.Preliminary projections for each projection year were controlled to county totals that wereestablished using a component method (births, deaths, and migration flows). Census tractprojections were made by allocating the Minor Statistical Area projections. In the central areas thiswas done by means of a shift-share technique. In the suburbs logistic curves were used for eachtract.

The tract level population projections were converted to housing projections and these werecompared with Traffic Analysis Zone data developed for transportation planning purposes.Adjustments to the projections were made as needed. No special analysis was done in the Overtownarea. A careful review of those areas where the initial projections showed significant declines inhousing was done and redevelopment plans and prospects for these areas were reviewed. No areashowed large losses of housing. The stability of the urban development boundary line coupled withthe continuing influx of immigrants from the Caribbean and Central and South America suggests thatthere will be increased demand for housing in all areas (Kerr 2000a).

The Miami-Dade County Department of Planning and Zoning estimates low and high populationprojections to be within five percent from the medium population projection for the years 2005 and2010 and within ten percent for the years 2015, and 2020 (Kerr 2000b).

9.2.2 Average Household Size

The simplest household variable is household size which represents the number of persons livingin a housing unit. The most widely used descriptor related to households is average household size,the mean number of people living in each housing unit in a locality. This is calculated simply as the

50

ratio of total persons living in households to the total number of occupied housing units (Myers1992).

Based on the 1990 US Census (Census Bureau 1990) the average household size by census tract isgiven in Table 9.1.

Table 9.1 Average Household Size by Census Tract

Census tract Average household size

30.01 3.05

31.00 3.14

34.00 2.66

36.01 2.32

Population divided by average household size equals the number of occupied units. This relationshipis commonly used by real estate analysts to predict the demand for housing in local areas (Myers1992). The projected demand for housing based on a low, medium, and high population series isshown in Tables 9.2, 9.3, 9.4, and 9.5, respectively.

Table 9.2 Change in Dwelling Unit Demand in Tract 30.01

Census Tract YearDemand for Future Dwelling Units

Low Medium High30.01 2005 (44) (46) (48)30.01 2010 21 22 2330.01 2015 36 95 15430.01 2020 133 148 163

Net Change 146 219 292

Table 9.3 Change in Dwelling Unit Demand in Tract 31.00

Census Tract YearDemand for Future Dwelling Units

Low Medium High31.00 2005 416 438 46031.00 2010 69 73 7631.00 2015 60 176 29231.00 2020 231 257 283

Net Change 776 944 1,111

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Table 9.4 Change in Dwelling Unit Demand in Tract 34.00

Census Tract YearDemand for Future Dwelling Units

Low Medium High34.00 2005 (100) (105) (110)34.00 2010 12 13 1334.00 2015 24 81 13834.00 2020 109 121 133

Net Change 45 110 174

Table 9.5 Change in Dwelling Unit Demand in Tract 36.01

Census Tract YearDemand for Future Dwelling Units

Low Medium High36.01 2005 275 289 30436.01 2010 67 71 7536.01 2015 52 133 21536.01 2020 171 190 209

Net Change 565 683 803

9.3 Vacant Land

This section documents vacant land available for new construction and determines the developmentcapacity of this land supply based on current zoning. It identifies where future growth can beaccommodated based on census tracts.

Overall, current vacant land supply is sufficient to accommodate future demand for housing unitsbased on low, medium and high population series. Current zoning allows up to a maximum of 4,348additional dwelling units to be built on current vacant land within the study area. The maximumcapacity within each census tract is determined by multiplying the acres of vacant land available bythe maximum dwelling units allowed per acre for the R-1, R-2, R-3 and R-4 zoning designations.

The low population growth scenario would require 1,532 dwelling units. The medium and highpopulation series would require 1,956 and 2,380 dwelling units, respectively.

A comparison between current maximum allowable capacity of vacant land and the demand of landfor future dwelling units as a net change from 1999 to 2020 is as follows:

1R-1 is Single Family Residential designation. Up to 9 dwelling units per acre areallowed according to the Miami Comprehensive Neighborhood Plan 1989-2000 and ZoningOrdinance Number 11000.

2R-2 is Duplex Residential designation. Up to 18 dwelling units per acre are allowedaccording to the Miami Comprehensive Neighborhood Plan 1989-2000 and Zoning OrdinanceNumber 11000.

3R-3 is Medium Density Multifamily Residential designation. Up to 65 dwelling unitsper acre are allowed according to the Miami Comprehensive Neighborhood Plan 1989-2000 andZoning Ordinance Number 11000.

4R-4 is High Density Multifamily Residential designation. Up to 150 dwelling units peracre are allowed according to the Miami Comprehensive Neighborhood Plan 1989-2000 andZoning Ordinance Number 11000.

5This represents the additional future dwelling units required in Census Tract 31.00.

6This represents that additional future dwelling units required in Census Tract 31.00.

7This represents the additional future dwelling units required in Census Tract 36.01.

8This represents the additional future dwelling units required in Census Tract 36.01.

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Table 9.6 Maximum Capacity of Existing Vacant Lands and Future Demand for Dwelling Units

Census Tract ZoningMaximum Units

AllowedFuture Demand for Dwelling Units

Low Medium High30.01 R-11 11 11 11 1130.01 R-22 26 26 26 2630.01 R-33 336 109 182 25530.01 R-44 855 — 1505 3176

31.00 R-3 794 776 794 79434.00 R-3 675 45 110 17434.00 R-4 1,081 — 1137 2338

36.01 R-3 45 45 45 4536.01 R-4 525 520 525 525

Total 4,348 1,532 1,956 2,380

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9.4 Jobs and Commercial Development

A jobs-to-housing ratio is used to determine the number of jobs to be created within the study area.For planning purposes, a ratio of one job created per dwelling unit is established for areas with atleast 2,100 dwelling units. A jobs-to-housing ratio for each census tract is determined in proportionto the total number of dwelling units expected due to the low, medium and high population series.For example, according to the low population growth scenario the study area would require 1,532dwelling units to meet future demand. At the 1,532 dwelling units level the jobs-to-housing ratiois calculated to be 0.73 jobs per dwelling unit. The total number of jobs to be created is calculatedby multiplying 0.73 jobs by 1,532 dwelling units. This calculation yields a total of 1,118 jobs to becreated according to the low population growth scenario. A projection of future jobs for each censustract based on low, medium and high population series is as follows (Nelessen 1994):

Table 9.7 Projection of Jobs by Census Tract

Census TractFuture Jobs

Low Medium High30.01 107 204 33131.00 566 879 1,25934.00 33 102 19736.01 412 637 910Total 1,118 1,822 2,697

Each census tract must have a minimum amount of local commercial facilities. For planningpurposes, a ratio of 52 square feet of commercial development per dwelling unit is established forareas with at least 2,100 dwelling units adjusted for the jobs-to-housing ratio (Miami-Dade CountyEmpowerment Trust 2000). A commercial development-to-housing ratio for each census tract isdetermined in proportion to the total number of dwelling units expected due to the low, medium andhigh population series.

For example, according to the low population growth scenario the study area would require 1,532dwelling units to meet future demand. At the 1,532 dwelling units level the commercialdevelopment-to-housing ratio is calculated to be 28 square feet of commercial development perdwelling unit. The total square feet of commercial development to be created is calculated bymultiplying 28 square feet of commercial development by 1,532 dwelling units by the jobs-to-housing ratio of 0.73. This calculation yields a total of 30,930 square feet of commercialdevelopment to be created according to the low population growth scenario. A projection of futurecommercial development is as follows (Miami-Dade County Empowerment Trust 2000):

9 C-1 is Restricted Commercial designation.

10 C-2 is General Commercial designation

11 This represents the additional square feet required in Census Tract 31.00.

12 PR is recreation designation.

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Table 9.8 Projected Commercial Development in Sq-Feet

Census Tract ZoningVacantLand

(sq. ft.)

Demand for Future Commercial Development (sq. ft.)

Low Medium High

30.01 C-19 191,299 2,948 9,202 22,10330.01 C-210 81,156 — — 19,00911

31.00 C-1 35,558 15,667 35,558 35,55831.00 C-2 29,532 --- 4,109 29,53234.00 C-1 202,443 909 4,622 13,17134.00 C-2 148,322 — — —36.01 C-1 89,474 11,406 28,699 60,78536.01 C-2 151,135 — — —

Total 928,919 30,930 82,190 180,158

9.5 Recreation and Open Space

The acceptable level of service standard for the City of Miami with regards to recreation and openspace is a minimum of 1.3 acres of public park space per 1,000 residents (City of Miami 1999).Based on the low, medium and high population series the demand for recreation and open space isas follows:

Table 9.9 Recreation and Open Space by Census Tract

Census Tract ZoningOpen SpaceAvailable

(acres)

Future Demand for Open Space

Low Medium High

30.01 PR12 0.6 0.57 0.85 1.1531.00 PR 4.0 3.17 3.84 4.5334.00 PR 8.4 0.15 0.37 0.5936.01 PR 0.0 1.7 2.06 2.41

Total 13 5.59 7.12 8.68

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Overall, current vacant land supply is sufficient to accommodate future demand for recreation andopen space based on low, medium and high population series.

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Figure 10.1 Interactions between VOLUTI and FSUTMS

10. ASSESSMENT OF IMPACT OF LAND USE CHANGE AND TRANSPORTATIONPROJECTS

One of the major improvements in VOLUTI is the capability of evaluating the impact of landdevelopment projects on the transportation system and vice versa. This improvement is made in twoways. First the user can select and define property parcels for development and specify land useintensities, then evaluate the impact of the development in terms of increased traffic volumes, thevolume over capacity ratio (V/C) in the transportation network FSUTMS, and the accessibilitymeasures. The second way is to allow the user to modify the transportation system and evaluate thesystem performance. Since VOLUTI is not an integrated model for transportation and land useplanning, the interactions between land use and transportation cannot be fully captured. Theinteraction is only modeled through accessibility. The figure below illustrates the interactionbetween VOLUTI and FSUTMS in the current implementation.

The impact of land use on transportation is direct as new land developments will result in changesin the transportation network in terms of traffic volume. The converse, however, is not true. In otherwords, changes in accessibility will not immediately produce results in land use patterns. This hasbeen recognized in literature and is still a subject of research. In this section, the methodologyemployed for evaluating the impact of land developments on the transportation system is described.

10.1 Overview of Site Impact Analysis in VOLUTI

Site impact analysis is the study of the impact of land use developments on transportation facilities,usually in terms of changes in traffic volumes and in roadway level of service. The analysis istypically referred to DRI analysis, or analysis of Development of Regional Impact. The methodologyused for this analysis in VOLUTI is based on the procedure described in Site Impact Handbook(FDOT 1997). A statement needs to be made here that the DRI analyses performed in VOLUTI arepreliminary in nature, and can not be taken as a DRI analysis normally conducted by engineering

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Figure 10.2 Structure of a Land Use Scenario withThree TAZs

firms. An actual DRI analysis will require much more detailed information. Information abouttransportation improvement projects, either having occurred since the last FSUTMS model update,having been committed, or being anticipated, must be collected and the transportation network editedaccordingly to reflect the conditions of the transportation system at the expected time of the land useproject. Similarly, land use changes must also be accounted for to reflect the land use conditions atthe expected time of the land use project.

To perform DRI analysis in VOLUTI, land development projects must first be defined. Theprocedure as well as the graphic user interface will be explained in Chapter 11. Here only the basicconcepts of a development scenario will be briefly introduced to facilitate the understanding of themethodology.

A development scenario is defined as projects located in a number of new TAZs, each of a singleland use such as single-family residential, multi-family residential, shopping center, etc. Creatingnew TAZs for development projects instead of adding the new land use (in the forms of populationand employment) is based on convenience consideration. Since currently only vacant land is usedfor developments, adding new developments in new TAZs will not affect the existing TAZs in termsof the zonal socioeconomic and demographic characteristics. VOLUTI currently does not have thecapability to modify the land use in a zone, which will be discussed later. The reason for limitingthe land use within a new TAZ is to avoid complication from the need to deal with “internalcapture.” Internal capture means that if a TAZ has mixed use, some of the trips may occur internallythus will not affect the network traffic volume. Internal capture is a complicated issue and requiresmuch detailed information about the specific uses within a zone, therefore is not considered atpresent.

Each new TAZ may contain a number of parcels of the same land use type. The hierarchicalstructure of a scenario is illustrated in Figure 10.2, in which N is the number of existing TAZs in theFSUTMS model. Any newly added TAZs are sequentially numbered beginning at N+1.

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Two basic methodologies are described in the FDOT 1997 Site Impact Handbook for site impactanalysis: manual method and model method. The model method refers to the use of FSUTMS forforecasting future traffic volumes, which has gained wider acceptance in recent years. FSUTMS isa district-approved analysis process, which can assist in determining trip distribution, internalcapture, mode split, and the assignment of trips. The advantages of the modeling method include:

• Consideration of extensive street systems and numerous traffic analysis zones in the analysis.• Consideration of the effects of development on diversions or shifts in travel behavior

patterns.

Due to the complex nature of the DRI analysis, reasonable assumptions are made to simplify theprocess in order to allow quick visualization of overall traffic impacts from new developments. Thefollowing sections describe the determination of trip generation, select zone analysis in FSUTMS,and interpretation of analysis results.

10.2 Land Development Types and Intensity

Table 10.1 shows the land use codes in ITE’s Trip Generation and their associated variable numbersfor development intensity in VOLUTI. ITE’s Trip Generation is the most intensive collection ofavailable trip generation data for different land uses throughout the United States and Canada sincethe 1960s. The document is recommended in the FDOT Site Impact Handbook for estimating traveldemand from new development. Table 10.2 illustrates the lookup table for independent land usevariables. In VOLUTI, the most appropriate variable for trip generation is left to the user to choosesince no specific guidelines for variable selection are provided in Site Impact Handbook. A databasein MS-Access format (ITE.MDB) is created to save the related trip generation information for eachland use and variable listed in Tables 10.1 and 10.2. The ITE.MDB file stores the data collectedfrom the PM peak hour of adjacent street traffic (usually between 4:00 and 6:00) for most of the landuses. It is assumed that this is the time period that the development peak will most likely occur.However, due to a lack of information in ITE’s Trip Generation, “Weekday”, “Weekday, P.M. PeakHour”, and “Weekday, P.M. Peak Hour of Adjacent Street Traffic” are applied for the land uses ofcity park, general office building, and walk-in bank, respectively.

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Table 10.1 Codes, Types and Independent Variable Numbers for New Land Uses

Land Use Code Land Use Type Variable Number

110210220310320411710820565834852870911

General Light IndustrialSingle-Family Detached HousingApartmentHotelMotelCity ParkGeneral Office BuildingShipping CenterDay Care CenterFast-Food Restaurant with Drive-Through WindowConvenience Market (Open 15-16 Hours)Apparel StoreWalk-in Bank

4, 5, 61, 2, 3, 61, 2, 34, 7, 84, 7, 86, 94, 5134, 5, 105, 11, 12554, 5

Table 10.2 Variable Lookup Table for New Lane Uses

Variable Number Variable Name

12345678910111213

Dwelling UnitsPersonsVehiclesEmployees1000 Sq. Feet Gross Floor AreaAcresOccupied RoomsRoomsPicnic SitesStudentsSeatsPM Peak Hour Traffic on Adjacent Street1000 Sq. Feet Gross Leasable Area

The SITEIMPACT.EXE file, a VOLUTI component written in the Visual Basic (VB) computerlanguage, is developed to simultaneously enumerate the traffic impacts from as many as 208 newdevelopment sites, each with one or more vacant parcels specified. The program is designed totransport data between the modeling modules of the urban transportation planning process in

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Background Traffic

Trip Generation

Create ZDATA3

Selected Zone Analysis

Initialization

FSUTMS and the GIS environment in VOLUTI. To simplify the problem, the transit service in thetransportation network is ignored, which leaves the highway-only analysis the only travel demandmodeling option in VOLUTI. Figure 10.3 illustrates the site impact procedures applied inSITEIMPACT.EXE. The following sections depict the tasks performed in each procedure in moredetail.

Figure 10.3 Site Impact Analysis Process

10.3 Initialization

Figure 10.4 illustrates the basic tasks performed in the initialization procedure. It starts withretrieving the name of the computer system directory where VOLUTI.INI is saved when it is firstinstalled. Next, the VOLUTI project directory can be identified by retrieving the program parametersfrom the VOLUTI2.INI file that is stored in the system directory. The SITEIMPACT.exe programthen verifies the existence of the FSUTMS software for version 5.3 or higher. This is achieved bysearching the system registry for the program location. After the required program directories areobtained, the next task is to read the new land uses that the user has specified in the GISenvironment, followed by creating a new PROFILE.MAS file to update the directories for storingthe FSUTMS programs and the input data for the study area. If the program detects any abnormaltermination of the program execution during initialization, an error message will be generated andthe application will be terminated.

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Start

Get System Directory

Get VOLUTI Directory

Get FSUTMS Directory

Get Net Site Information

Create New PROFILE.MAS

End

Figure 10.4 Initialization Procedure

10.4 Background Traffic

Background traffic is the base condition in determining the impacts of a development on thetransportation system. In VOLUTI version 2.0, the 1990 Miami Transportation Planning Model(MTPM) is used for the study area and its base year travel demand estimated by FSUTMS is takenas the background traffic. In this procedure, the input files that are needed for executing thehighway-only model of FSUTMS are copied from “F_dade90\Original” to “F_dade90\Modified”under the VOLUTI directory. Note that the updated XY.90A and LINKS.90A files have alreadybeen created and stored by VOLUTI in the “Modified” subdirectory. Thus, they are not duplicatedin this procedure.

In the original Miami-Dade network data, traffic analysis zones (TAZs) 1 to 1179 and 1180 to 1200are specified as the internal and external zones, respectively. Among the internal TAZs, 11 zones,zones 1167 to 1169 and 1172 to 1179, are coded as dummy zones that can be utilized as the newTAZ for developments. To accommodate more development sites to be analyzed simultaneously,the structure of the traffic analysis zones are modified. The internal and external TAZ numbers inthe modified network are now ranging from 1 to 1379 and 1380 to 1400, respectively. The zonenumbers between 1180 and 1379 are thus available for new development sites. In order toimplement the new TAZ structure in the site impact analysis process, the following FSUTMS inputfiles are manually modified and stored in the “F_dade90\Original” directory: PROFILE.MAS,LINKS.90A, XY.90A, EETRIPS.90A, ZDATA4.90A, and IEEIEE.A90.

62

In FSUTMS, the PROFILE.MAS parameter file is referenced during program execution to identifyparameter settings used in each step of the model. The file contains replacement values for each "&"parameter reference. The changes made to the PROFILE.MAS file include:

• Change of &ZONESI from 1179 to 1379;• Change of &ZONESA from 1200 to 1400; and• Change of &SELDEST from 1-1179 to 1-1139.

The LINKS.90A file contains information on characteristics of 1990 Miami-Dade highway networkon a link-by-link basis. It is one of the network data files required for the Highway NetworkBuilding (HNET) model in FSUTMS. The changes made in the LINKS.90A file include:

• Update of centroid numbers for external TAZs; and• Addition of centroid connector links for new internal TAZs.

Same as LINKS.90A, the XY.90A file is a data file required for HNET, which contains X and Ycoordinates for each node in the highway network. The following changes are made to the originalXY.90A file:

• Update of centroids numbers for external TAZs; and• Addition of nodes for new centroid connector links.

The EETRIPS.90A file is the input data set needed by the External (EXT) model in FSUTMS. TheEXT model estimates trips traveling through the study area, between entry and exit pointsrepresented by external TAZs. The changes are made to update the external TAZ numbers in theEETRIPS.90a file.

The ZDATA4.90A file is one of the zone-based FSUTMS input data sets for the Trip Generation(GEN) model. The GEN model estimates travel demand by considering the area’s characteristicssuch as land use, population, employment, and other economic activity measures. The ZDATA4 filecontains internal-external trip productions for each external traffic analysis zone. Internal-externaltrips are those trips with one trip end inside the study area and one trip end outside the study area.The external TAZ numbers in the ZDATA4.90A file are changed.

The IEEIEE.A90 file stores the following trip tables in TRANPLAN file format: I-E trips (trip table1), E-I trips (trip table 2), and E-E trips (trip table 3). The trip tables 1 and 2 comprise what theFSUTMS model considers as I-E trips. The following procedures modify the external TAZ numbersin the IEEIEE.A90 file without altering the data:

• Run the TRANPLAN utility program, TPCARD.EXE, to retrieve the trip data from the threetrip tables in the original IEEIEE.A90 file;

63

$BUILD TRIP TABLE$FILESINPUT FILE = SRVDATA, USER ID = $ieeiee.out$OUTPUT FILE = VOLUME, USER ID = $ieeiee.a90$$HEADERS BUILD TRIP TABLE -- 1990 Miami-Dade$OPTION Print TRIP ENDS SIMPLE$PARAMETERS NUMBER OF PURPOSES = 3 NUMBER OF ZONES = 1400$END TP FUNCTION

Figure 10.5 Building Trip Table Control File

• Follow the simple format of the Build Trip Table program in TRANPLAN to create a newtrip table file with the external zone numbers being updated; and

• Run the Build Trip Table program to create a new IEEIEE.A90 file.

The control file for running the Build Trip Table program is shown in Figure 10.5. TheIEEIEE.OUT file in Figure 10.5 refers to the updated trip table created according to the simpleformat of the Build Trip Table program.

10.5 Trip Generation

The trip generation step is the most critical procedure in the site impact analysis since it estimatesthe amount of travel associated with each proposed land use. According to FDOT Site ImpactHandbook, pass-by and diverted trips can be ignored when the model method is applied. In addition,the intrazonal trips estimated by FSUTMS is acceptable as an estimate of internal capture. Thus,pass-by, diverted, and internal capture trips are not estimated in the SITEIMPACT program. Figure10.6 gives the steps that is applied to replicate trip generation, in which n refers to the developmentsite that is currently in the process of travel demand estimation and m is the total number of newdevelopment zones specified in the VOLUTI GIS environment.

As shown in Figure 10.7, the first major task in the trip generation process is to estimate traveldemand in terms of vehicle trips for each new development using ITE’s Trip Generation. Thesecond task is to examine the study area in order to locate the TAZs that may have similar land useswith the new developments. The information, e.g., the percentage of trips by purpose, for TAZsfound in the second task is applied in the third task to convert the total number of vehicle trips toproduction and attraction in person trips by different trip purposes. The process will continue untilthe travel demand for all of the new land uses is estimated. The following sections describe theprocedures performed in each step of trip generation procedure in detail.

64

Set n = 1

Set m = Number of New Sites

Estimate ITE Vehicle Trips

For Site n

Convert Vehicle Trips to Person Trips by Purpose

Examine Land Use

n = n + 1

n > m ?

Yes

No

End

Start

Figure 10.6 Trip Generation Process

10.6 Estimation of ITE Vehicle Trips

The following information from ITE’s Trip Generation is first retrieved from the ITE.MDB file:

• statistical data on the number of samples taken,• the average value of the dependent variable for the measured land uses,• an average trip generation rate,• a range of trip generation rates, and• the standard deviation of sampled data.

The SITEIMPACT program then calculates the travel demand by the average trip rate and regressionequation coded in the program. Since the resulted total number of trips from different approachescan vary substantially, the method for selecting average trip generation rates or regression equationdescribed in Site Impact Handbook is applied to find a better estimate of trip generation. Figure 10.7shows the tasks performed in this procedure.

65

Start

Get ITE GEN Parameters

Estimate Trips by

Average Trip Rate

Estimate Trips by

Regression Equation

Determine Trips from New Development

End

Figure 10.7 Procedure for Estimation of ITE Vehicle Trip

Selecting an appropriate method for estimating trips requires engineering judgment since one methodmay provide better estimation than the other under certain conditions. If the trip generationequations are not given in ITE’s Trip Generation, the average trip rate is assumed in theSITEIMPACT program to estimate the total site traffic for each land use. Otherwise, the followingmethod is applied to obtain the final trip generation estimate:

• Compare the forecasted trips using both the regression equation and the average trip rate.If the difference is less than one standard deviation times the calculated trips from theaverage trip rate, use the equation. If the condition is not met, consider the next criterion.

• Use the equation if there are at least 20 data points for regression equation. If not, considerthe next criterion.

• If the standard deviation is less than 110 percent of the average rate or the correlationcoefficient (R2) is higher than 0.75, use the equation. If none of the above criteria are met,use the average trip rate.

10.7 Examination of Land Use

Figure 10.8 illustrates the steps for examining the land uses. The ITE’s Trip Generation estimatesnew travel demand in vehicle trips, which need to be converted and apportioned to production andattraction person trips among the FSUTMS trip purposes (home-based work, home-based shopping,

66

Start

Read PRODS & ATTRS

Get Area-wide

Trip Purpose Percentages

End

home-based social/recreation, home-based other, and non home-based). In the procedure ofexamining land uses, the TAZs with the same land use are identified. As shown in Figure 10.8, thezone-specific productions and attractions for background traffic that are stored in the PRODS.A90and ATTRS.A90 files in the “F_dade90\Original” directory are first retrieved. The socioeconomicdata files, ZDATA1 and ZDATA2, are then examined to locate the zones with similar land uses.The approach for identifying the zones with similar land uses is straightforward. For example, landuse 110 is for general light industrial development. Thus, the average trip purpose percentages forzones with non-zero industrial employment in the ZDATA2 file are applied to apportion trips todifferent purposes. Table 10.3 gives the lookup table for the socioeconomic variables used toidentify zones with the same land use.

Figure 10.8 Procedure for Examining Land Use Mix

Table 10.3 Socioeconomic Variables Used to Identify Zones with the Same Land Use

Land Use Code Socioeconomic Data File Variable

110210220310320565710820834852870911

ZDATA2ZDATA1ZDATA1ZDATA1ZDATA1ZDATA2ZDATA2ZDATA2ZDATA2ZDATA2ZDATA2ZDATA2

Industrial EmploymentSingle Family PopulationMultiple Family Population Hotel/Motel UnitsHotel/Motel UnitsService EmploymentCommercial EmploymentCommercial EmploymentService EmploymentCommercial EmploymentCommercial EmploymentService Employment

67

Start

Convert to Daily Trips

Convert to P&A by Purpose

End

10.8 Trips Conversion

Figure 10.9 lists the tasks performed in the trip conversion procedure. The SITEIMPACT programfirst converts the peak hour adjacent street traffic (one hour between 4 and 6 PM) to daily vehicletrips by a factor of 0.15. The daily vehicle trips for new development sites are then converted andapportion into productions and attractions in person trips by purpose according to its land use type.Four categories of land uses are created in the SITEIMPACT program for trip conversion. The firstcategory includes land uses for residential uses such as land use 210 (single-family detachedhousing) and 220 (apartment). The second category is for hotel and model land uses. The thirdcategory includes the city park land use, which is assumed to generate HBSR trips only. The lastcategory includes the other land uses provided in VOLUTI that generate attraction trips only.

Figure 10.9 Trip Conversion Procedure

Residential category. For residential land uses, the production and attraction vehicle trips areestimated based on its development intensity. The number of attraction vehicle trips is first assumedto equal to the intensity multiplied by NHB auto occupancy factor, i.e., AOFAC(5). The number ofproductions in vehicle trips is then obtained by subtracting the daily vehicle trips with the estimatedattraction vehicle trips. The following equations are applied to estimate attractions in person tripsby different purposes.

A(i, 3) = Int(0.5 * intensity(i) + 0.5)A(i, 4) = Int(0.2 * intensity(i) + 0.5)A(i, 5) = Int(0.3 * intensity(i) / 2 + 0.5)

where:i = the new development site i that is currently under processing;A(i, n) = attractions in person trips for site i for purpose n: 3 for HBSR, 4 for

HBO, and 5 for NHB;intensity(i) = the intensity associated with development site i; andInt = the function to obtain the integer portion of a number.

68

P i n Int

P trips i P vehicle nTot P veh P vehicle

AOFAC n( , )

_ ( ) _ ( )_ _ _ (5)

( ).�

�

��

�

�

����

�

05

P trips i IntITE i Tot P veh

Tot P veh Tot A veh_ ( )

( ) _ __ _ _ _

.��

��

�

��

�

�05

A trips i ITE i P trips i_ ( ) ( ) _ ( )� �

A i Int

A trips i A vehicleTot A vehAOFAC

( , ) .

_ ( ) _ ( )_ _

( ).5 05

5

505� �

�

�

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����

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The following equation is applied to estimate the production trips for each new residential land use.Note that the P_vehicle(i) and Tot_P_veh variables are both obtained from the zones identified inthe examine land use procedure.

where:P(i, n) = productions in person trips for site i for purpose n: 1 for

HBW, 2 for HBS, 3 for HBSR, and 4 for HBO;P_trips(i) = estimated productions in vehicle trips for site i;P_vehicle(n) = total productions in vehicle trips for purpose n;Tot_P_veh = total productions in vehicle trips from all purposes; andAOFAC(n) = auto occupancy factor for purpose n.

Hotel/motel category. For the hotel and motel category, the SITEIMPAXT program first estimatesthe productions and attractions in vehicle trips by the following equations:

The A_trips(i) and Tot_A_veh variables are the estimated attractions for site i and the total attractionsof the background traffic, both in vehicle trips. The ITE(i) variable is the number of estimated dailyITE vehicle trips. The number of NHB attractions in person trips is then estimated as follows:

The number of home-based vehicle trips for both productions (HBP_Trips) and attractions(HBA_Trips) for each development site i are estimated by the following equations:

HBP_Trips(i) = Int(P_trips(i) - P(i, 5) * AOFAC(5) + 0.5)HBA_Trips(i) = Int(A_trips(i) - A(i, 5) * AOFAC(5) + 0.5) (Eq. 10.1)

69

P i n Int

HBP Trips i P vehicle nTot P veh P vehicle

AOFAC n( , )

_ ( ) _ ( )_ _ _ ( )

( ).�

�

��

�

�

����

�

5 0 5

A i n Int

HBA Trips i A vehicle nTot A veh A vehicle

AOFAC n( , )

_ ( ) _ ( )_ _ _ ( )

( ).�

�

��

�

�

����

�

5 0 5

A i Int

ITE i A vehicleTot A vehAOFAC

( , ) .

( ) _ (5)_ _

(5).5 05 05� �

�

�

�

�

����

�

�

The following equations are then applied to estimate the number of productions and attractions inperson trips for each home-based purpose where A_vehicle(n) is equal to the total attractions invehicle trips for purpose n and all the other variables are previously defined.

(Eq. 10.2)

Social/recreation category. All of the trips are assumed to be HBSR trips for city parkdevelopment. The associated ITE vehicle trips are converted to person trips via AOFAC(3).

Other attraction only category. For the development sites that only generate attraction trips, thenumber of NHB person trips is first estimated by the following equation:

Equation 10.1 is applied again to calculate the value of HBA_Trips by substituting ITE(i) forA_trips(i). The number of attraction trips for each purpose is then calculated using Equation 10.2.

The SITEIMPACT program displays the calculated person trips by purpose at the end of theprocedure via the dialog box illustrated in Figure 10.10. The user selects the development zonenumber in the box at the upper left corner of the dialog box to browse the associated trip informationfor each new land development. The user can also alter the number of trips by purpose to theirdesired values. Clicking the Default Trips button will display the original trip values calculated bythe program. Note that adjustments to the trip generation rates provided by ITE may be necessaryto reflect the unique demographic combination and the influence of tourism on travel in Florida.This can be performed by either modifying the values stored in Table ITE_TG of the ITE.mdb fileto replace the trip generation data in ITE's Trip Generation for a specific land use or entering newnumber of trips by purpose in the dialog box for each new development site.

70

Figure 10.10 Dialog Box for Editing Number of Trips

Start

Get Original Attractions

Balance Attractions

Print Trips

End

10.9 Creation of ZDATA3

The tasks in the creation of ZDATA3 file is shown in Figure 10.11. In this procedure, the newdevelopment trips are added into the ZDATA3 file. Since the total number of attractions areadjusted to the total number of productions at the end of trip generation in FSUTMS, the attractiontrips from the SITEIMPACT program are balanced in advance to take the adjustment effect intoaccount before the ZDATA3 file is updated.

Figure 10.11 Procedure for Creating ZDATA3 File

71

Total A i newA i Total A iTotal P i

Total P i

_ ( ) ( ) _ ( )_ ( )

_ ( )

��

3 1173P+ 356100 New 210 development3 1173P+ 170 100 New 210 development3 1173P+ 175 100 New 210 development3 1173P+ 313 100 New 210 development3 1173P+ 0 100 New 210 development3 1173A+ 0100 New 210 development3 1173A+ 0 100 New 210 development3 1173A+ 104 100 New 210 development3 1173A+ 29 100 New 210 development3 1173A+ 15 100 New 210 development

Figure 10.12 Sample ZDATA3 File for New Development

The program first calculates the unadjusted attractions based on the trip attraction equations inFSUTMS. The trips, except for NHB trips, are adjusted by multiplying them with the followingfactor:

(Eq. 10.3)

where:i = trip purpose i, excluding NHB;newA(i) = total attractions for trip purpose i from all of the new

development;Total_A(i) = total unadjusted attraction for trip purpose i; andTotal_P(i) = total productions for trip purpose i.

After the trips are balanced by the factor calculated using Equation 10.3, they are added into theZDATA3 file. Figure 10.12 illustrates a partial ZDATA3 file created by the SITEIMPACT programfor a new single-family detached housing development. Note that the number of NHB productionsis zero since FSUTMS will automatically generate NHB productions equal to the ZDATA3 estimate.

10.10 Selected Zone Analysis

In this procedure, the highway only analysis in FSUTMS is performed. A single assignment is madethat tracks the total trips as one purpose and development trips as a separate purpose. This isachieved by providing two new script files to execute the modeling programs in FSUTMS.Appendices A and B illustrate, respectively, the scripts stored in “F_dade90\Script” directory for themode split and highway assignment modules in FSUMTS. After trips are assigned to the networkwith the EQUILIBRIUM HIGHWAY LOAD routine, the development trips in purpose 2 areretrieved and then displayed in the VOLUTI GIS environment to allow the visualization of the trafficimpacts of new developments.

72

Figure 10.13 Change in Traffic Volume for the LowDevelopment Scenario (Scenario 101)

10.11 Results Implementation

The model output volumes from FSUTMS represent the peak season weekday average daily traffic(PSWADT) volumes that represent the average of the 13 highest week, weekday traffic volumes.Conversions are needed if AADT volumes are desired.

As an example, the results of a site impact analysis for the residential development from the lowprojection scenario are shown in Figures 10.13, and 10.14, respectively. Because the model isoutdated, the results cannot be considered reliable and are provided only for illustration purposes.Figure 10.13 shows the change in traffic volume on individual links. The legend indicates the changein traffic volume in percentages. The numbers on the links are the volumes after the development,and those in the parentheses are the volumes before the development. Figure 10.14 shows changesin volume over capacity (v/c) ratios. The legend indicates the absolute changes in v/c ratios. Thenumbers on the links are the v/c ratios after the development, and those in parentheses are the v/cratios before the development.

Detailed information about the three scenarios described in Chapter 9 may be found in the VOLUTIprogram. Note that the results are not accurate due to the outdated model.

73

Figure 10.14 Change in Volume/Capacity for the LowDevelopment Scenario (Scenario 101)

74

Figure 11.2 TheView Menu

Figure 11.1 Top-Level Menu in VOLUTI

11. VOLUTI GRAPHIC USER INTERFACE DESIGN

VOLUTI is developed within ArcView®, an Environmental System Research Institute product,customized with Avenue, the ArcView script language, and VisualBasic®. To allow people withlimited knowledge of ArcView or GIS to use VOLUTI, it is designed as a menu driven program, inwhich all queries may be made by selecting from the menus. Some customized tools are added toallow the user to interact with a map, such as selecting a TAZ or a network link. In this chapter, thedesign of the user interface is described.

11.1 Top Level Graphic User Interface

The top level menu in VOLUTI is illustrated in Figure 11.1. The menunames in uppercase letters are customized menus that were not part of thestandard ArcView menu. The View menu is original to ArcView, but hasbeen substantially expanded (see Figure 11.2). The functions added to thismenu are as follows:

(1) Theme (Layer) Manager. Because all themes in the project viewwill have their legends displayed in the table of content area (spaceto the left of the map area), when more than a few themes aredisplayed, the table of contents becomes too long and examininga legend may require scrolling the table of content. To avoid acrowded table of contents, all themes that are not relevant to thecurrent query are moved to a hidden view, which may be revealedby the user using the Theme Manager (see Figure 11.3). Selectinga theme from the hidden layer and turning it on will result inremoving it from the hidden layer and placing it in the projectview so it can be viewed.

(2) Image background control. Image backgrounds (one-footresolution digital orthophotos or one-meter resolution colorinfrared digital orthophotos) may be toggled on and off.

(3) Overtown boundary display. This menu selection will display thetheme that shows the boundary of Overtown.

75

Figure 11.3 Theme Manager Dialog Box

(4) Geocode one address. This function will require the user to type in a street address (streetnumber and street name). This address is then geocoded and a point will be drawn on themap to indicate the location. This allows the user to quickly locate an area without searchfor street names.

(5) Clearing matched address. This clears the graphics representing the geocoded address fromthe map display.

(6) Default display area. The user may define a default display area by drawing a rectangle onthe map display. When query results are displayed, VOLUTI will automatically zoom to thedefault display area.

(7) Redrawing maps. This function refreshes the map display.

(8) Clearing all queries. While each group of queries has its own house cleaning function, thisone will clear all the queries results.

Beside the menus, some tool buttons are created. Tool buttons are necessary when input needs tobe given interactively by the user on the map by pointing and clicking or by drawing geometricshapes. In most cases, the user does not need to know the existence or the functions of the buttons.However, it will be helpful to understand the functions of these tool buttons, especially whenVOLUTI is expecting an input from the map and the user decides to perform another task beforereturning to the current task. In other words, if a query requires interactive input from the user bypointing and clicking in the map display area, then a corresponding tool button associated with thisquery must be depressed. These tool buttons will be described in the following sections when theircorresponding menu choices are discussed.

76

Figure 11.4 Land UseMenu

11.2 Land Use Menu

The land use menu provides a group of query choices related to land uses. Figure 11.4 shows theLand Use menu. There are 26 items on this menu, which will be briefly described below.

(1) View Site Photos. Site photos have been collected and “hot linked” to the map. Whena user chooses this menu entry, a map will be displayed indicating all sites that havephotos attached. At this point the tool button (shown at right) will be depressed and theuser may begin to click on the points on the map. Either static photos or a panorama will bedisplayed. If a series of static photographs are available, a dialog box will appear and theuser can browse through the photographs by clicking the “Previous” and “Next” buttons.

(2) Zoning. This selection will display the zoning map of selectedzoning types. The types of zoning are given in Table 11.1. Thesource of the information is the City of Miami PlanningDepartment.

Table 11.1 Zoning Codes

Database Code Zoning Code Description

1 R1 Single family

2 R2 Duplex

3 R3 Multifamily (low density)

4 R4 Multifamily (high density)

11 C1 Restricted commercial

12 C2 Liberal Commercial

13 CBD Central business district

25 O Office

35 G/I Government/Institutional

45 I Industrial

55 RT Rapid transit

81 PR Parks/recreation

82 CS Conservation

97 EXP Expressway

98 RR Rail road

99 Not defined

77

(3) Vacant Land. For this query, the parcel map is used and all parcels designated as vacant byits county land use code are highlighted in a light blue color.

(4) Vacant Land of Given Size. For this query, the user may specify a desirable size of thevacant land in acres. VOLUTI will first find all vacant parcels and then aggregate thosevacant parcels that are adjacent and owned by the same entity. The result is a new mapdisplaying all pieces of land that satisfy the size requirement.

(5) Underdeveloped Land. If a parcel is non-vacant but the ratio of the building footage to thelot size is less than 0.1 (or 10%), it is considered to be underdeveloped and has the potentialfor expansions.

(6) Underdeveloped Land of Given Size. This is similar to the query about vacant land of givensize. However, no new theme will be created. All parcels that are deemed underdevelopedand satisfy the size criterion will be displayed in a highlight color.

(7) Total Dwelling Units, Single Family Dwelling Units, and Multifamily Dwelling Units.These queries display maps showing the number of such dwelling units by TAZ.

(8) Single Family Vacant Dwelling Units and Multifamily Vacant Dwelling Units DwellingUnits. These queries display maps showing the number of vacant single-family andmultifamily dwelling units by TAZ.

(9) Dwelling units per acre. This is a measure of density or land use intensity. It is arrived bydividing the total dwelling units in a TAZ by the area of a TAZ.

(10) 1998 Land Use. This selection displays the 1998 land use map. For a description of the landuse types, see Section 6.4.

(11) Land Use Composition in a Region. Land use composition can be displayed in termsof percentages of different land uses in a region defined by the user. The selection ofthis menu item will depress the tool button shown at the right. At this point the regionmay be specified by drawing a polygon on the map. An example of this query is given inFigure 11.5. It may be seen that tax income in this area comes mainly from residential andcommercial uses.

(12) Building Stock. Commercial, industrial, and office building stocks in square feet areindicative of the adequacy of the infrastructure necessary to support land developments forthe respective uses. The building stock information is derived from the property database.Once the user draws a polygon on the map to indicate an area of interest, the property recordsof commercial, industrial, and office uses in the area are retrieved and the building squarefootage of the individual properties is summed up, respectively. Figure 11.6 shows anexample of this query.

78

Figure 11.6 Commercial, Office, and Industrial Building Stocks

Figure 11.5 Land Use Composition and Tax Base Make–Up

79

Figure 11.8 Job/Housing Balance

Figure 11.11 ZDATAChange Dialog Box

Figure 11.7 Land Use Mix in Study Area

Figure 11.9 Land Use Change Dialog Box

Figure 11.10 Multifamily Land Use Changebetween 1994 and 1998

(13) Land Use Mix and Job/Housing Balance. Their definitions have been given in Section 7.1.Figures 11.7 and 11.8 show these two measurements in the study area, respectively. It maybe noted that the land use mix indices in the study area are in the middle of the spectrumwhile the job/housing are not well balanced in the Overtown area with either low number ofjob per capita in some areas or no residential population in other areas.

(14) Land Use Change (1994/1998). Two snap shots of land uses are available from 1994 and1998. This query requires the user to specify one particular land use via a dialog box, asshown in Figure 11.9, and will display the percentage increase or decrease of the total areaof that land use type by TAZ (see Figure 11.10).

(15) ZDATA Change (1990/1999). ZDATA files are the inputfiles of demographic and socioeconomic data for theFSUTMS model. Four types of data may be displayed: totalpopulation, single-family dwelling units, multifamilydwelling units, and total employment by TAZ. The userneeds to specify a particular type of information using thedialog box shown in Figure 11.11.

80

Figure 11.12 Display of Sales Price History of OneProperty

(16) Average Parcel Size and Park Acreage. The definitions are given in Sections 7.3 and 7.4,respectively. Selecting these menu entries will result in maps with the required informationby TAZ.

(17) Sales Price History (One Property). The tool menu shown to the right consists of fourtool buttons. They are used for queries on sales price(s) of a property, the assessedvalues of a property, the average assessed values for a selected group of properties,or building stocks, respectively (the last two queries are described in the next twoparagraphs). The appropriate button must be depressed for a particular query in thisgroup. Property sales prices reflect the real estate market condition in an area and areconsidered in VOLUTI as an indicator of the health of a community. The data used in thisquery are the parcel data and the property tax database. When this menu selection is made,the tool button shown at the right (it is the first of a group of three tool buttons. A group oftool buttons is referred to as a tool menu) is depressed and the parcel map is displayed. Theuser then selects a parcel and the sales prices for the last three sales, if available, will bedisplayed as a bar chart as shown in Figure 11.12.

(18) Assessed Value (One Property). Another measure of real estate market conditions is theassessed values of a property. This query works in a similar manner as the sales pricequery. The active tool button for this query is the second one in the tool menu. Figure11.13 illustrates the result of one such query.

(19) Assessed Value (Region). This menu entry allows the user to examine the average assessedvalue of a given type of properties. The dialog box that lets the user to select the type ofproperties is shown in Figure 11.14, while the result of a query is shown in Figure 11.15.

81

Figure 11.13 Assessed Values of One Property

Figure 11.15 Average Assessed Values in a Region

Figure 11.14 Dialog Box for Choosingthe Type of Properties

82

Figure 11.16 Selected Public Facilities with a 2-Mile Radius

(20) Public Facilities Near a Site. The public facilities currently considered include colleges,universities, schools, daycare centers, libraries, hospitals, nursing homes, parks, fire stations,water and sewer facilities. Existing public facilities near a development site are importantconsiderations, especially if smart growth policies are established. An example is the recentdebate in the Florida legislature to require that a resident development be approved only ifthe public schools in the area have additional capacity to accommodate the anticipatedincrease in student enrollment due to the development. This query will search for all thepublic facilities in an area of a given radius surrounding a point specified by the user bypointing and clicking on the map. The tool button used by this query is the top one inthe tool menu shown to the right. The tool button at the bottom is used by the nextquery, Public Facilities in a Region. The result is a summary of the number offacilities of each type found within the given radius and new themes, each showing onetype of facility. Figure 11.16 illustrates the results of this query.

(21) Public Facilities in a Region. This is similar to the previous query but instead of searchingin an area around a user specified location, the search is done in a area defined by a polygondrawn by the user on the map. The results are presented in the same manner.

(22) Set Search Radius for Site Search. Search radius in miles may be set by the user using thisoption.

(23) Water Lines and Sewer Lines. These two menu entries display the water and sewer lines asshown in Figure 11.17. The thickness of the lines indicate the diameter of the pipelines. Fordifferent land uses, the required pipeline diameters will vary.

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Figure 11.19 Flood Zone Map

Figure 11.17 Water and Sewer Lines

Figure 11.18 Environment Menu

11.3 Environment Menu

This menu provides information on environmental concerns such aswater bodies, flood zones, pollution, etc. Figure 11.18 shows thismenu. The functions provided by this menu are described below.

(1) Shorelines, Lakes and Canals. Three themes are displayedshowing the shorelines, lakes, and canals, respectively.

(2) Flood Zones. The flood zone map is compile based on theFederal Emergency Management Agency flood model. Theflood zone map is illustrated fro the Overtown area in Figure11.19. The high risk areas are near the Atlantic coast and inthe Miami River basin.

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Figure 11.20 Public Well Field Protection Areas

Figure 11.21 Socioeconomicand Demographic Data Menu

(3) Public Well Field Protection Area. When a development is situated in a well field protectionarea, care must be taken to assure that there will be no adverse impact on the public drinkingwater from the developments. The legend in Figure 11.19 shows the time it takes forpollutants to travel to the pumping stations in different surrounding areas.

(4) Trash Centers and Land Fills. A map will display the trashcenters and land fills. Currently, VOLUTI does notprovide the user any information about if a particulardevelopment is located too close to a land fill.

(5) Hazardous Waste Sites. The hazardous waste sites aredisplayed as points. They are also a consideration indetermining suitability of development projects. Theymay potential impose additional cleanup costs or pose athreat to public health.

11.4 Socioeconomic Menu

The socioeconomic and demographic data that may be displayedin VOLUTI include the TAZ structure and data associated withTAZ, which are typically estimated (or obtained from census) fordemand model purposes, and the census block group boundarieswith the associated census data. The census block groupboundaries and the census data used in the current VOLUTIversion are from 1990 since the new census data are not yetavailable at tract or block group level. The TAZ based data arethe 1999 estimates, which are used in the 1999 FSUTMS model

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calibration. Figure 11.21 shows the queries that may be performed on socioeconomic anddemographic data. Display of these data are briefly described here.

1. TAZ based demographic and socioeconomic data. The data that can be displayed include:(1) population density, which is the TAZ population divided by the TAZ area(2) population of age 16 and younger(3) population between the ages of 16 - 65, which represents the labor force(4) population aged over 65, which represents the retired population(5) single-family population(6) multi-family population(7) commercial employment(8) service employment(9) industrial employment(10) total employment(11) employment density(12) school enrollment

2. The census block group based data include:(1) population(2) population density(3) number of housing units(4) vacant housing units(5) median rent

There are many different types of census data that might be of interest to planners, such as racialmakeup of the population, education attainment of the population, etc. If more census data are tobe made available, it will necessary to change the menu structure and treat the choices of census datain a separate dialog box to avoid having a menu that is too long to display.

3. Buffer analysis. Buffer analysis is a commonly used GIS method and has many applicationssuch as transportation project impact analysis, transit service area analysis, and land usestudy. In VOLUTI, a buffer analysis begins with the user selecting one or more linesrepresenting roadways (see Figure 11.22). VOLUTI will then ask the user to specify a buffersize and a particular type of information such as population or dwelling units by differentsize to be analyzed (see Figures 11.23 and 11.24). The result of the analysis will besummarized as statistics in a message box (see Figure 11.24) and a map displaying thedistribution of the specified variable by TAZ within the buffer as shown in Figure 11.26.

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Figure 11.22 Selection of Roadway Segments for Buffer Analysis

Figure 11.23 Selecting a Variable for Buffer Analysis

Figure 11.24 Entering Buffer Size

Figure 11.25 Buffer AnalysisResult Summary

13 The data are from 1992. Metro-Dade Transit Agency has a plan to update theinformation in the future.

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Figure 11.27 TransportationFacility Menu

Figure 11.28 Selecting a VideoClip Demonstrating LOS

Figure 11.26 Buffer Analysis Result as a Distribution Map

11.5 Transportation Facilities

This menu (see Figure 11.27) supports queries related to typesof transportation facilities available and selected attributes ofroadways. The types of transportation facilities include publictransit facilities including bus routes and bus stops13, limitedaccess highways, principal arterials, collectors, and railroadtracks. These facilities may be individually displayed or bedisplayed as a subset defined by the user (with the ShowSelected Facilities option). The data are from the ITD majorroads data.

The roadway attributes include number of lanes, 1996 averageannual daily traffic (or AADT) on state roads, traffic volumefrom the 1990 FSUTMS model, and 1996 level of service(LOS) on state roads.

In addition to a map that shows the LOS of state roads, a selectpanel will appear to allow the user to choose a particular levelof service (see Figure 11.28) to see a video clip of anexpressway operating at that LOS.

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Figure 11.30 Congested Highway Travel Time Contours in Minutes

Figure 11.29 Accessibility Menu

11.6 Accessibility

Accessibility measures have been discussed inChapter 6. The menu is shown in Figure 11.29. Theoriginal regional highway accessibility refers to theaccessibility computed using the 1990 FSUTMSmodel. The regional accessibility with newdevelopment will update the accessibility indices byincluding the traffic impact from the newdevelopment. The update will be for the scenario forwhich the FSUTMS model is run most recently. Theregional accessibility by transit cannot be updated forscenarios currently because the problems associatedwith performing select zone analysis with transitmode. A more detailed discussion may be found inChapter 8.

Figures 11.30 through 11.37 illustrate the maps showing the various accessibility measures.

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Figure 11.32 Difference between Transit and Highway Travel Time

Figure 11.31 Transit Travel Time Contours (All Modes withPenalties)

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Figure 11.34 Regional Accessibility to Employment Opportunities byCar

Figure 11.33 Transit Transfers Needed to Travel between One Zone toAll Other Zones

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Figure 11.36 Local Accessibility Index for Miami-Dade County

Figure 11.35 Regional Accessibility to Employment Opportunities byTransit

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Figure 11.37 Local Accessibility Index for the Overtown Area

Figure 11.32 shows the difference in the transit and highway travel time for zone 610. The timedifferences range from less than 5 minutes to two hours. There appear that in some corridors, thetravel time difference is smaller than in other corridors, meaning that transit service is morecompetitive in some areas.

Figure 11.33 illustrates the transfers required to travel from zone 610 to other zones. Apparently,in the rail corridors and the I-95 corridor there are excellent direct services. This kind of informationwill be useful to study job access issues for the Overtown residents, many of whom have no othertransportation means other than transit.

Regional accessibility to employment opportunities by car and by transit is shown in Figures 11.34and 11.35, respectively. While the regional accessibility by car resembles concentric circles, a resultof the strong downtown employment center, that by transit appears more irregular in shape,indicating that accessibility is dependent on availability and quality of transit services. However,these measures do not consider if better accessibility in a particular corridor actually offers an area’sresidents advantages, since they do not account for the match or mismatch of the residents’ skills andthe types of jobs. Nonetheless, these measures will be useful in evaluating job opportunities fordifferent communities when other data such as employer information and analysis tools are used.

The local accessibility to essential services (see Section 8.4 for the definition) in the Miami-DadeCounty and in Overtown is shown in Figures 11.36 and 11.37, respectively. It appears that theOvertown area has a similar local accessibility index as the majority of other areas in the county.More detailed data may be needed to verify if there is a lack of services in the area.

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Figure 11.38 SiteImpact Menu

11.7 Site Development

For this implementation of VOLUTI, the user is able to select vacantparcels for new development projects. The Site Development menuoffers the user three choices: creating a new development scenario,editing an existing scenario, or deleting an existing scenario (see Figure11.38). These three choices are described below.

1. Creating New Scenarios. When the user selects from the menu“New Scenario,” the parcel map will be displayed and vacantparcels in the default display area are highlighted. A new themesaving the land development project information is created and named as Scenario<number>. This <number> is the scenario sequence number, and is the last scenarionumber increased by one. The scenario name and sequence number is shown in the upperleft corner of the dialog box as shown in Figure 11.39. The user may then define a new TAZ,select parcels to add into the new TAZ, specify land use type and land use intensity for theparcels, define the zonal centroid and the necessary network connectors for the TAZ usingthe same dialog box. As has been discussed in Chapter 10, a land development scenario mayconsist of one or more TAZs. Each TAZ, in turn, may include one or more parcels. Landuse type and intensity are specified for the entire TAZ, which means that all parcels addedto the TAZ will have the same land use type, or TAZs are of single land use. The basic stepsfor creating a new land development scenario is as follows:

(a) Select a new TAZ number from “Zone Number” list box to create a new TAZ.(b) Select vacant parcels, which are indicated by the highlight color, to add to the TAZ

by pointing and clicking on the parcel map. This results in the parcels placed intothe newly created TAZ. The total square footage from all the parcels in the TAZ isdisplayed in the Total Parcel Lot Size box. As an example, Figure 11.39 showsparcel 1639 has been added to TAZ 1172.

(c) Select an anticipated zoning type for the selected parcel. Because zoning code maybe changed, VOLUTI does not restrict the user from speculating what zoning mightbe in the future for a particular parcel. The selection should not, however, bearbitrary, and should follow the guidelines of sustainable development or smartgrowth. In the example shown in Figure 11.39, Ru_th or townhouse district ischosen as the anticipated zoning type.

(d) Once the zoning type is chosen, a list of permissible land uses will appear in theChoose Land Use Type list box, choose one from the list. In Figure 11.39, the twopossible land use types, single-family detached and apartment are the possiblechoices and apartment is chosen.

(e) Once a land use type is chosen for the parcel, a list of land use intensity variables isdisplayed in the Select Land Use Independent Variable list box. Choose one fromthe list. Again, as shown in Figure 11.39, the intensity variables for apartment useinclude number of dwelling units, number of residents, and number of vehicles. Thenumber of dwelling units is selected as the variable to use.

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Figure 11.39 Land Use Scenario Input Dialog Box

(f) Enter an integer in the Intensity box. In the example, 100 is entered.(g) The zonal centroid for the TAZ may be defined automatically by the program or

manually by the user. To manually define the centroid location, the user first choosethe User option in the Define Centroid by area at the upper right corner of the dialogbox, then simply points and clicks on the scenario map within the TAZ to indicatethe location of the centroid.

(h) Define network connectors. The connectors are simplified representation of accessroads to the transportation network in FSUTMS. All trips originating from orentering the TAZ will reach the network via these connectors. To define aconnector, the user first clicks the Define Connectors button located at the right sideof the dialog box. Then the user simply clicks on a nearby network link as shownin Figure 11.40. Note that a connector editing tool box will be displayed whileconnectors are being edited. The first button allows a connector to be added. Thesecond allows a connector to be deleted.

(i) Repeat from Steps (a) through (h) until no TAZs are to be added to the scenario.When the dialog box is closed, if the user confirms that the scenario is to be saved,the data are written into text files, which are then used to create a new set ofFSUTMS input files.

During the above process, the user may choose at any time to delete or add parcels to a TAZ,redefine the land use type or intensity, change the zonal centroid location, or edit the centroidconnectors. Information on a particular parcel that have been defined as part of a TAZ maybe displayed by selecting the parcel number from the Parcel ID list box. The selected parcelwill flash on the map and a dialog box will display the information related to the parcel.

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Figure 11.40 Adding Zonal Centroid and Connectors

Figure 11.41 Selecting a Scenario to Edit

Figure 11.42 Deleting a Scenario

Every time a TAZ number is clicked, a parcel number is clicked, or a parcel is selected fromthe map, the input will be saved.

The result of saving a new scenario is the creation of number of input files for the FSUTMS model.These files include the XY file, the Link file, and a file that provides the land use and TAZinformation.

2. Editing an Existing Scenario. Theuser may choose to open an existingscenario to edit. With this choice, adialog box will appear showing allscenarios that exist. Once a scenariois selected, the dialog box shown inFigure 11.39 will appear with theinformation of the chosen scenariodisplayed. The user may thenproceed to make the changes to thescenario.

3. Deleting an existing scenarioinvolves the user specifying onescenario to delete (see Figure 11.42)and will cause VOLUTI to remove notonly the themes related to the selectedscenario (the scenario theme and theconnector theme), but also all therelated FSUMTS input files.

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Figure 11.43 Traffic Impact Menu

Figure 11.44 Traffic Volume Increase on Network Links Due to Development for theLow Development Scenario

11.8 Travel Demand

From the Traffic Impact menu (see Figure 11.43), theuser may choose to view the impact of a landdevelopment scenario on the transportation network.The impact may be shown as the traffic volume onnetwork links and percent of increase from when thereis no new development, or the V/C ratio and changes inV/C ratio caused by the development. The volumes andV/C ratios may be displayed for each link for onedirection or both directions. Examples of the output fortraffic volume and V/C ratios are given in Figures 11.44and 11.45, respectively. The scenarios used are theresidential projects defined as part of the low growth scenario described in Chapter 9. Figure 11.44shows the additional traffic volume due to the developments on network in both directions (totalvolume from both directions). The absolution increase in the volumes are indicated by the legend.The percentage change of network link volume is shown as a label on the network link. Links withchanges smaller than one percent are not labeled. Figure 11.45 shows the V/C ratio increase on thenetwork. The legend indicates the change in V/C ratio, while the labels on the network links are theV/C ratios after development projects are implemented.

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Figure 11.45 V/C Ratio Increase and V/C Ratio of Network Links Due to the LowDevelopment Scenario

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12. CONCLUSIONS AND RECOMMENDATIONS

This project has expanded significantly an earlier version of VOLUTI, with many additional data,queries, and analysis capabilities. Accessibility measures have been added to give a regional senseof the number of opportunities and transportation system conditions. A DRI analysis tool has beenimplemented to perform quick and preliminary assessment of impacts of land development projectson the transportation network as well as accessibility.

The project has been presented at several occasions and responses from planners have been positive,with many commenting that VOLUTI would be a useful tool for planning purposes, and somerequesting for the completed software.

To further enhance the tool and make it easily adapted for other localities, the following issues needattention and in some cases improvements are recommended.

1. GIS Data Maintenance and Availability. GIS applications are data intensive. Not only asignificant amount of data must be available initially, they need to be updated continually ifVOLUTI is to be useful a few years after its initial installation. There are several problemsthat will hinder the data maintenance effort. First there is a fragmentation of data sources.The data used in VOLUTI are mainly from three sources: FDOT, Miami-Dade County, andCity of Miami. FDOT may be considered as a single source of data. The data from Miami-Dade County, on the other hand, came from multiple agencies, including ITD, Departmentof Water and Sewer, Miami-Dade Transit Agency, Metropolitan Planning Organization, andDepartment of Environmental and Resource Management. If additional data are to be used,more departments may be involved. These data are maintained in some instances by the ITD,and in others by the departments that use them. There is also a lack of metadata, ordocumentation on the data in many cases. This happens more often when the data aremaintained by county individual departments other than ITD.

The solution to this problem is to establish an enterprise GIS database within the county andmunicipalities, respectively, and close coordination between the county and the localgovernments to make arrangements on data collection, maintenance, and sharing. This willbe a long process, and will require some changes in the business processes. Theadvancement in information technology in recent years is moving the businesses in thatdirection with more data sharing. For instance, more data are becoming available on theInternet. However, a true enterprise GIS database will take a long term effort and a greatdeal of work toward inter- and intra-agency collaboration and coordination.

2. Site Impact Analysis. An immediate need is to update the FSUTMS to the 1999 model onceit is calibrated. The 2025 model should also be added. The transit mode needs to beincluded to evaluate at system level the development impact on transit ridership and toinvestigate land use alternatives and transportation programs that promotes public transit andreduce single-occupancy car use.The current VOLUTI implementation does not include all the possible land uses, whichshould be added.

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Another issue to be investigated is the interpolation between the base year and future yearmodels. Land use projects are typically planned with a time frame of several years to overten years, which are unlikely to occur in the model base year or the future year. It isnecessary, therefore, to reflect the conditions at the project implementation time. Suchconditions include demographic (e.g. population, household size, dwelling units, etc.),socioeconomic (mainly employment information), and transportation system (roadwaychanges, additional transit services, tolls, parking fees, etc.). Some of the information is notreadily available in digital format at present, and some does not exist. For instance, anaccurate estimation of population for any year between the model base year and future yearis not likely available. To perform such an estimate may involve much work. Employmentestimation by zone is another challenge, regardless of the year for which it is needed. The1999 Miami-Dade County FSUTMS model has also adopted a lifestyle trip generationmodel, which consider such variables as presence of children in households and number ofworkers in households as the basis of determining the number of trips produce by householdsfor different trip purposes. Methods for estimating these variables are being developed bythe county Planning Department.

The transportation network update involves reflecting all the changes in the roadways, transitservices, toll, parking costs, fuel costs, etc., in the model. Some data may not be easilyforecast, such as fuel costs. Information on transportation improvement projects that havebeen carried out or expected to be completed around the time of the development projectsto be modeled may be continually collected and a database constructed, which may be usedin model network update. The database should be spatiotemporal in nature, i.e., both projectlocation information and specifics about the projects need to be coded. Programs may bedeveloped to automatically take information from the database and the model network maybe updated for any given time.

3. Evaluation of Scenarios. Procedures and tools should be developed to allow differentscenarios to be evaluated. The evaluation may involve comparison of density, land use mix,vehicle miles traveled (VMT), travel time, trip length, etc. between two or more scenarios.

4. Link to a Land Use Model. VOLUTI may be linked to a land use forecast model such asULAM. This link will allow a better understanding of the impact of transportation ongrowth, that is how transportation improvements will affect growth in population and jobsin different areas.

5. Accessibility Measures. Current accessibility measures may be improved based on moreempirical research on their link to the travel behaviors. Additionally, population andemployment resulted from new developments should be added to existing TAZs beforeaccessibility measures are update to reflect the improved accessibility due to newdevelopments.

6. Decision Support. Current VOLUTI implementation has limited capability of decisionsupport. A better capability may be arrived at by supporting more sophisticated queries andproviding more analysis functions. Examples of queries and analyses that support decisionmaking may be to evaluate potentials of land for development, identify land developmentopportunities for a given goal or objective, determining adverse factors that may make a

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development project questionable or increase the costs significantly, and performtransportation equity analysis.

7. Visualization. While virtual reality remains to be an expensive technology and is unlikelyto be practical on a large regional scale, the visualization may be further enhanced. Onepotential type of data that can be used for visualization is the video logs that FDOT routinelycollects on all the state roads. Presently, the LOS measures and display of operatingconditions are only available for state roads. The possibility of adding the capability ofshowing the user the LOS or operating conditions on local highways should be investigated.While FDOT does have the software to calculate LOS for local highways, it may requiremore detailed information that is not currently available in VOLUTI. A simplified algorithmthat gives a preliminary evaluation of LOS may be developed. Additionally, it is possibleto develop a methodology to categorize the local highway operating conditions based ontypical roadway configurations, intersection configurations, signalizations, and trafficvolumes to display video clips for different operating conditions. This will make it mucheasier for elected officials and the public to understand how the transportation system isfunctioning or what impact development projects will have on the roadways.

For developments at a scale smaller than regional ones, three-dimensional models ofbuildings and roadways may be useful for visualizing the aesthetic effects of highway ordevelopment projects. This may also be achieved with two-dimensional graphics. Forinstance, AutoCad and 3D-Studio may be used to create the graphics, which may then be“painted” on the three-dimensional models in ArcView.

8. Software. VOLUTI needs to be rewritten for ArcView 8, which is a new object-orientedArcView program, released in May 2001 by the Environment System Research Institute(ESRI). Although for the foreseeable future, Arcview 3.X versions will continue to besupported by its vendor due to the large number of existing ArcView applications, ArcView8 will certainly gradually replace ArcView 3.X versions in the future. In ArcView 8.0, theprogramming environment is Visual Basic, which provides more functionality and permitsbetter flexibility in integrating ArcView application with other window based softwarecomponents and programs.

Other features that may be added to VOLUTI include map production functions and on-linehelp documents.

To make VOLUTI portable to different localities that use different databases, a mechanismto automatically configure the program for different databases and database setups is needed.The setup program will guide the user through installation, check the presence of differentdatabases and their structures, and determine what functions should be available or how thefunctions should be modified to accommodate the given data.

In addition to software improvements, VOLUTI needs to be marketed to planners in the state,including the planners working for public entities and private sectors. This may be done by freedistribution of the software and workshops held in various parts of the state.

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Figure 12.1 Complexity of Functional Linkage in Urban Systems Dynamics(Southworth, 1995)

Although the interactions between land use and transportation are complicated and certainly not asimple cause-effect relationship, planners and policy makers have realized that transportationprojects do have impacts on land uses and vice versa. Figure 12.1 illustrates all the forces, somepolitical, some cultural, and some economic, that affect the land use. With tools such as VOLUTI,it is hoped that we will be able to make these forces to work together to produce the right conditionsto achieve sustainable or smart growth and invest wisely in transportation to facilitate this goal.

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