Integrating Land Use and Transportation in a GIS ... - Semantic Scholar

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 Research Florida International University 10555 W Flagler Street Miami, Florida 33174 Phone: (305) 348-3821 Fax: (305) 348-2802 Email: [email protected] Jill Strube, Research Associate Metropolitan Center Florida International University 150 SE 2nd Ave., Suite 1201 Miami, Florida, 33131 Phone: (305) 349-1251 Fax: (305) 349-1271 Email: [email protected] Francisco Ordaz, Research Assistant School of Urban Planning Florida Atlantic University Fort Lauderdale, Florida

July 2001

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, was instrumental to shaping the concepts and framework of VOLUTI. David Korros became the project manager 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 data on Overtown and some important information about the historical and current economic conditions in Overtown. Ronald Finegold of the Miami-Dade County Information Technology Division assisted in the acquisition of land use and property tax databases, and answered numerous questions about the data. FDOT District 6 Planning Office provided the digital orthophotos and databases of the state highway system in the district. Frank Baron and Susan Schreiber of Miami-Dade Metropolitan Organization, David Dahlstrom of the South Florida Planning Council, and Fabian Cevallos of Broward Transit took out time from their busy schedule to review the software developed for this project and provided many useful suggestions, most of which have been adopted. Dr. Sydney Wong, the former Associate Director of the FAU/FIU Joint Center of Environmental Problems and now an Associate Professor at the University of Pennsylvania, offered insights in various aspects of Overtown. Lee-Fang Chow, Research Associate with the Lehman Center for Transportation at FIU, with the help of Soon Chung and Xin Li, research assistants from the Lehman Center for Transportation Research, coded most of the new GIS programs. Contributions from all the above individuals are appreciated, as well as those from many others who are 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4.1 Sustainable Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4.2 Accessibility to Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.3 Effect of Urban Forms on Travel Mode Choice . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.4 Neighborhood and Urban Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.5 Florida Sustainable Communities Network (FSCN) INDEX Software . . . . . . . 21 4.6 Land Use Planning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.7 Visualization Programs for Land Use and Transportation . . . . . . . . . . . . . . . . . 24

5.

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

6.

DATA COLLECTION AND PROCESSING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.1 Property Tax Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.2 Parcel GIS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 6.3 Zoning Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 6.4 Land Use Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 6.5 Employment data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 6.6 Water and Sewer Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 6.7 Public Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 6.8 Transportation Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 6.9 Traffic Analysis Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 6.10 Photographs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 ii

6.11 6.12 6.13

Aerial Photos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Demographic and Socioeconomic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Environmental Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

7.

DEVELOPMENT OF LAND USE INDICATORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 7.1 Land Use Mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 7.2 Job/Housing Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 7.3 Average Parcel Size by TAZ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 7.4 Open Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 7.5 Land Use Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 7.6 Changes in Population, Employment, and Dwelling Units by TAZs . . . . . . . . . 41 7.7 Tax Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

8.

ACCESSIBILITY AND MOBILITY EVALUATION . . . . . . . . . . . . . . . . . . . . . . . . . . 43 8.1 Regional Accessibility by Highway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 8.2 Regional Accessibility by Transit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 8.3 Local Accessibility to Essential Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 8.4 Contours of Highway Travel Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 8.5 Contours of Transit Travel Time by Transit Modes . . . . . . . . . . . . . . . . . . . . . . 45 8.6 Shortest Transit Travel Time Contour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 8.7 Transit Transfers Required . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 8.8 Difference of Transit and Highway Travel Time . . . . . . . . . . . . . . . . . . . . . . . . 46

9.

DEVELOPMENT OF LAND USE SCENARIOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 9.1 Development Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 9.2 Population Forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 9.2.1 Low, Medium, and High Projection Series . . . . . . . . . . . . . . . . . . . . . . . 49 9.2.2 Average Household Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 9.3 Vacant Land . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 9.4 Jobs and Commercial Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 9.5 Recreation and Open Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

10.

ASSESSMENT OF IMPACT OF LAND USE CHANGE AND TRANSPORTATION PROJECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 10.1 Overview of Site Impact Analysis in VOLUTI . . . . . . . . . . . . . . . . . . . . . . . . . . 56 10.2 Land Development Types and Intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 10.3 Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 10.4 Background Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 10.5 Trip Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 10.6 Estimation of ITE Vehicle Trips . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 10.7 Examination of Land Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 10.8 Trips Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 10.9 Creation of ZDATA3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 iii

10.10 Selected Zone Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 10.11 Results Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 11.

VOLUTI GRAPHIC USER INTERFACE DESIGN . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 11.1 Top Level Graphic User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 11.2 Land Use Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 11.3 Environment Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 11.4 Socioeconomic Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 11.5 Transportation Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 11.6 Accessibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 11.7 Site Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 11.8 Travel Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

12.

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

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

iv

LIST OF TABLES Table 4.1 Table 4.2 Table 4.3 Table 9.1 Table 9.2 Table 9.3 Table 9.4 Table 9.5 Table 9.6 Table 9.7 Table 9.8 Table 9.9 Table 10.1 Table 10.2 Table 10.3 Table 11.1

Summary of Accessibility Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Initial Indicators of FSCN INDEX Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Software Packages for 3D Modeling and Visualization (from McGaughey 1997) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Average Household Size by Census Tract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Change in Dwelling Unit Demand in Tract 30.01 . . . . . . . . . . . . . . . . . . . . . . . . 50 Change in Dwelling Unit Demand in Tract 31.00 . . . . . . . . . . . . . . . . . . . . . . . . 50 Change in Dwelling Unit Demand in Tract 34.00 . . . . . . . . . . . . . . . . . . . . . . . . 51 Change in Dwelling Unit Demand in Tract 36.01 . . . . . . . . . . . . . . . . . . . . . . . . 51 Maximum Capacity of Existing Vacant Lands and Future Demand for Dwelling Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Projection of Jobs by Census Tract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Projected Commercial Development in Sq-Feet . . . . . . . . . . . . . . . . . . . . . . . . . 54 Recreation and Open Space by Census Tract . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Codes, Types and Independent Variable Numbers for New Land Uses . . . . . . . 59 Variable Lookup Table for New Lane Uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Socioeconomic Variables Used to Identify Zones with the Same Land Use . . . 66 Zoning Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

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LIST OF FIGURES Figure 3.1 Figure 4.1 Figure 6.1 Figure 6.2 Figure 6.3 Figure 7.1 Figure 7.2 Figure 7.3 Figure 8.1 Figure 8.2 Figure 10.1 Figure 10.2 Figure 10.3 Figure 10.4 Figure 10.5 Figure 10.6 Figure 10.7 Figure 10.8 Figure 10.9 Figure 10.10 Figure 10.11 Figure 10.12 Figure 10.13 Figure 10.14 Figure 11.1 Figure 11.2 Figure 11.3 Figure 11.4 Figure 11.5 Figure 11.6 Figure 11.7 Figure 11.8 Figure 11.9

Miami-Dade County and Overtown Boundary . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 CAD Drawing Overlay on Photographs (from Jha and McCall 2001) . . . . . . . . 26 Detail of a Digital Orthophoto Quarter Quadrangle . . . . . . . . . . . . . . . . . . . . . . 36 One-Meter Color Infrared Digital Orthophoto from the USGS . . . . . . . . . . . . . . 37 One-Foot Digital Orthophoto from Miami-Dade County . . . . . . . . . . . . . . . . . . 37 Dialog Box for Displaying Land Use Change . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Change in Single Family Land Use (1994 - 1998) . . . . . . . . . . . . . . . . . . . . . . . 41 Change in Single Family Dwelling Units (1990 - 1999) . . . . . . . . . . . . . . . . . . . 41 Dialog Box for Comparison of Transit and Highway Travel Time . . . . . . . . . . . 46 Transit-Highway Travel Time Difference in Peak Hours with Penalties Applied to Transit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Interactions between VOLUTI and FSUTMS . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Structure of a Land Use Scenario with Three TAZs . . . . . . . . . . . . . . . . . . . . . . 57 Site Impact Analysis Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Initialization Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Building Trip Table Control File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Trip Generation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Procedure for Estimation of ITE Vehicle Trip . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Procedure for Examining Land Use Mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Trip Conversion Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Dialog Box for Editing Number of Trips . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Procedure for Creating ZDATA3 File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Sample ZDATA3 File for New Development . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Change in Traffic Volume for the Low Development Scenario (Scenario 101) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Change in Volume/Capacity for the Low Development Scenario (Scenario 101) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Top-Level Menu in VOLUTI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 The View Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Theme Manager Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Land Use Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Land Use Composition and Tax Base Make–Up . . . . . . . . . . . . . . . . . . . . . . . . . 78 Commercial, Office, and Industrial Building Stocks . . . . . . . . . . . . . . . . . . . . . . 78 Land Use Mix in Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Job/Housing Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Land Use Change Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 vi

Figure 11.10 Figure 11.11 Figure 11.12 Figure 11.13 Figure 11.14 Figure 11.15 Figure 11.16 Figure 11.17 Figure 11.18 Figure 11.19 Figure 11.20 Figure 11.21 Figure 11.22 Figure 11.23 Figure 11.24 Figure 11.25 Figure 11.26 Figure 11.27 Figure 11.28 Figure 11.29 Figure 11.30 Figure 11.31 Figure 11.32 Figure 11.33 Figure 11.34 Figure 11.35 Figure 11.36 Figure 11.37 Figure 11.38 Figure 11.39 Figure 11.40 Figure 11.41 Figure 11.42 Figure 11.43 Figure 11.44

Multifamily Land Use Change between 1994 and 1998 . . . . . . . . . . . . . . . . . . . 79 ZDATA Change Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Display of Sales Price History of One Property . . . . . . . . . . . . . . . . . . . . . . . . . 80 Assessed Values of One Property . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Dialog Box for Choosing the Type of Properties . . . . . . . . . . . . . . . . . . . . . . . . 81 Average Assessed Values in a Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Selected Public Facilities with a 2-Mile Radius . . . . . . . . . . . . . . . . . . . . . . . . . 82 Water and Sewer Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Environment Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Flood Zone Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Public Well Field Protection Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Socioeconomic and Demographic Data Menu . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Selection of Roadway Segments for Buffer Analysis . . . . . . . . . . . . . . . . . . . . . 85 Selecting a Variable for Buffer Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Entering Buffer Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Buffer Analysis Result Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Buffer Analysis Result as a Distribution Map . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Transportation Facility Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Selecting a Video Clip Demonstrating LOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Accessibility Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Congested Highway Travel Time Contours in Minutes . . . . . . . . . . . . . . . . . . . 88 Transit Travel Time Contours (All Modes with Penalties) . . . . . . . . . . . . . . . . . 89 Difference between Transit and Highway Travel Time . . . . . . . . . . . . . . . . . . . 89 Transit Transfers Needed to Travel between One Zone to All Other Zones . . . . 90 Regional Accessibility to Employment Opportunities by Car . . . . . . . . . . . . . . . 90 Regional Accessibility to Employment Opportunities by Transit . . . . . . . . . . . . 91 Local Accessibility Index for Miami-Dade County . . . . . . . . . . . . . . . . . . . . . . 91 Local Accessibility Index for the Overtown Area . . . . . . . . . . . . . . . . . . . . . . . . 92 Site Impact Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Land Use Scenario Input Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Adding Zonal Centroid and Connectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Selecting a Scenario to Edit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Deleting a Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Traffic Impact Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Traffic Volume Increase on Network Links Due to Development for the Low Development Scenario . . . . . . . . . . . . . . . . . . . . . . 96 Figure 11.45 V/C Ratio Increase and V/C Ratio of Network Links Due to the Low Development Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Figure 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 of Transportation, District VI called Transportation/Land Use Visualization Project in 1999 (York et al. 1999). The study investigated the best practices in integrating land use and transportation planning in Florida through a survey and analysis of the survey results. The survey results indicated that while most transportation and land use planning organizations recognized the importance of linking both planning fields, few had been able to incorporate this link into their practices. The study pointed out that visualization was a useful tool for communicating with communities to convince the public of the benefits of some of the changes. As part of the project, a prototype program named VOLUTI 1.0 (Visualization Of Land Use and Transportation Interactions) was developed, which offered 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 integrated GIS-based tool that includes more land use and accessibility measurements, and additional functions related to assessment of impacts of land use developments and transportation projects. The improvements involved incorporation of additional data sources, development of land use and accessibility indicators, development of land use scenarios, and a stronger linkage between VOLUTI and FSUTMS (Florida Standard Urban Travel Model Structure), the standard travel demand model in 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 County Empowerment 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-term prospects 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, such as those relating to the size of the overall community, housing, jobs, services and activities, include viii

guidelines relating to walkability, density, and diversity. Public space, open space, jobs-housing balance in number and variety, connectivity, and efficient and practical use of geography and passive solar energy are highly regarded concepts (Center for Livable Communities 1999). Public participation is one of the cornerstones of sustainability theory. Public participation in the decision-making process provides the foundation for implementing policies and developing strategies that promote sustainable communities. This requires that government agencies provide the public adequate, accessible, and timely information and requires understanding and respect for differing social and economic views, values, traditions, and aspirations. In this regard, VOLUTI will facilitate public involvement by providing information on land use and transportation in an innovative manner. Accessibility to Opportunities Accessibility has been recognized as one of the most important factors that affect both land use and travel behavior. Many definitions and measures of accessibility exist, which Richardson and Young (1982) classified into a spectrum of accessibility measures such as modal accessibility, legal accessibility, temporal accessibility, relative accessibility, and integrated accessibility. Of interest to this project is a gravity type accessibility measure 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 activity centers. Zonal attractiveness may be measured by total employment or commercial and service employment. Another accessibility measure is opportunities related to essential household daily activities such as shopping at grocery and drug stores. The proximity of such opportunities to residential neighborhoods reduce the need for 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 to recent policy initiatives at the federal, state, and local levels to look for ways to improve mobility and reduce congestion without building new highways. These policy initiatives are motivated by the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA), the Transportation Equity Act for the 21st Century (TEA-21), the Clean Air Act Amendments of 1990 (CAAA), rising public concerns about petroleum consumption in the U.S. and global warming, and political pressure to reduce fuel consumption. In particular, TEA-21 initiated a new sustainable development pilot program to help state and local governments plan environmentally-friendly development, including reducing vehicle miles traveled (VMT). One of the approaches to reduce VMT is to change travel behavior via policies such as taxation, pricing, and land use planning. Research evidences have suggested that a significant correlation between transit use and density exists 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 ix

regression analyses that urban-form variables did contribute to mode choice, with positive impact on transit use and walk and negative impact on SOV use, respectively. Additionally, land use mix seemed to better explain the choice of walk mode. The authors determined that significant shifts from SOV to transit use and walking occur between an employment density of 20 and 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 of population density and income on modal split (Kockelman, 1995). She showed that density (or other factors proxied by density such as land prices, parking fees, transit service frequency, and congested roadways), not income, was the influential factor on modal split. In another study (Kockelman 1997), Kockelman investigated the link between urban form and travel behaviors and concluded in that accessibility, land use mixing, and land use balance were all statistically significant and influential to travel behaviors, including mode choice. It is concluded that accessibility is a far better predictor of vehicle kilometers traveled (VKT) than density. A similar study by Sun et al. (1998) using the 1994 Portland Travel Survey data, density, land use mix, accessibility, annual household income, household size, dwelling type, number of phone lines in 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 household trip rates and VMT. Regression analysis showed that density and land use balance make little difference in the number of daily trips but has a significant impact on house VMT. High density and high entropy both contribute to a reduction of VMT. In a study of Miami-Dade County in Florida, Messenger and Ewing (1996) decided that the density needed to support a 25-minute bus headway was 8.4 dwelling units per acre (1.4 higher than that proposed by Pushkarev and Zupan) at the transit operator’s minimum productivity and 19.4 dwelling units per acre at the system wide average productivity. Bus mode share at trip origins is primarily a function of low automobile ownership, and secondarily of job-housing balance and transit service level, although job-housing balance has a small effect. Street configuration is found to have no apparent effect on transit use. Bus mode share at trip destinations is primarily a function of parking cost, overall density, and access to downtown. In an attempt to determine if land use truly has a causal relationship with travel behavior or whether it is other socioeconomic, demographic, and transportation supply characteristics, which are also associated 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 San Francisco Bay Area (SFBA) and investigated the travel behavior variables and a wide array of variables that are objectively or subjectively measured. Results of the analyses indicated that the variables had weak power to explain mode choice. Nonetheless, these results led to the conclusions that have been generally agreed upon such as parking availability negatively impact total number of person trips, and high density, proximity to parks and bus stops, access to rail transit stations, and presence of sidewalks encourage non-motorized travel. Furthermore, attitudes (pro-environment, pro-transit, suburbanite, automotive mobility, time pressure, urban villager, TCM, and workaholic) x

were determined to have more significant impact on travel behavior than socioeconomic and land use 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 questions of how urban form influences the demand for light rail and commuter rail transit and how transit influences land uses. Urban structure, employment and residential densities, land use mix and urban design were found to influence transit use. However, although land use mix and urban design was significant in explaining transit use, individual land use and design was not. Also, density is more powerful than land use mix and urban design in explaining transit use. On the other hand, the influences of transit on urban form were described by using the following four factors: property value, 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, and location often near street car tracks. Such neighborhoods are often older and built before the end of the World War II. There has been much debate as whether urban design has any impact on transit use. Some argue that neotraditional neighborhood design encourages walking and transit use, while others 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 local accessibility indices and found that two to four more bicycle and walk trips were made by residents in two areas that closely resemble neotraditional neighborhood than by those living in areas that are automobile oriented. She did not conclude if these trips by non-motorized modes actually replace some of the automobile trips or the neotraditional neighborhood simply encouraged more walk and bicycle trips. In a study of travel characteristics comparison using data from San Francisco Bay Area and Los Angeles, Cervero carefully paired “transit neighborhoods” and “auto neighborhoods” by a set of selection criteria (Cervero, 1994). The “transit neighborhoods” are defined as initially built along street car lines 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, primary random street patterns (over 50% of intersections being “T” intersections or cul-de-sac), and built after 1945. A comparison of the SFBA paired neighborhoods revealed that while other demographic characteristics of the neighborhood pairs do not differ significantly, most auto neighborhoods have a higher auto ownership, produce much more drive-alone trips, have a lower transit use, and have much lower walk trip rates than transit neighborhoods, the latter being especially obvious. On average, transit neighborhoods generate around 70 percent more transit trips and 120 pedestrian/bicycle trips. This may be partially contributed to the fact that transit neighborhoods tend to have better transit service supplies (measured by daily VMT per acre). By comparison, the transit neighborhoods in Los Angeles do not demonstrate the same significant amount of transit use or reduction of single occupancy driving. Cervero contributed this phenomenon to the overall strong auto orientation in xi

Los Angeles and believed that the positive effects of transit neighborhoods in such an environment are limited. Using data of the entire Los Angeles area, Cervero also regressed the percent of transit trips against variables including gross residential density (households per acre), natural logarithm of household income, neighborhood type (auto or transit), and density interaction (product of residential density and 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 every 1,000 households while those in SFBA will generate 5.1 percent transit trips. Another conclusion was 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 Los Angeles. Work trips by transit averaged 8 percent more 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 on reducing automobile dependency, were supported by Clifton and Handy (1998) in a study of six Austin, Texas neighborhoods. The results suggest that the role of urban form plays in travel behavior is not entirely straightforward, sometimes influencing travel choices directly, sometimes indirectly, sometimes influencing choices in the short term, sometimes in the long term, and sometimes not having any measurable influence on choices at all. In the end, it appears that certain land use policies can help to provide alternatives to driving, but that the reduction in driving is likely to be small. Florida Sustainable Communities Network (FSCN) INDEX Software The Florida Sustainable Communities Network (FSCN) INDEX software is the result of the collaboration between the Florida Department of Community Affairs (DCA) and Criterion Planners/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 calculated for 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 add indicators 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 used and being GIS based, there are several main differences between 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 planning with area size ranging from specific sites to xii

500 acres while VOLUTI is design for both small and large areas; (3) While INDEX may be linked to a travel demand model, e.g. it uses model output to display mode shares and per capita VMT, its focus is on land use planning. VOLUTI emphasizes linkage between land use and transportation and therefore 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 market models. The basis of the Lowry and Lowry Derivative Models is the assumption that, everything else being equal, place of employment determines place of residence. Constrained by regional employment and population totals, the model will allocate residence population close to non-service type of work places then allocates service employment to serve the population, which in turn requires the allocation of more residence for the service employees. 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 econometric regression models are built upon econometric models. The last group of models are based on economics and markets. These models emphasize the 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 in Florida is ULAM (Urban Land use Allocation Model) developed by Transportation Planning Services, Inc., in 1998. ULAM is a land use forecast model that generates data for the transportation demand model FSUTMS. The link to transportation in ULAM is a travel factor determined for each TAZ 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 including 2D overlay of orthophotos on maps, 3D visualization with geometric models, 4D visualization with animated geometric models, surface and terrain models, drape of orthophoto onto terrain, photo-simulation that uses photographs instead of 3D geometric models and rendering, animation of a series of image frames, and real-time virtual reality and simulation. The authors pointed out that 3D geometric modeling of a simple street scene could take 2-3 months of work and would involve intensive computation, while painting photographs over simple 3D models will reduce the work to 2 weeks. 3D modeling effort may be reduced by using predefined 2D and 3D geometric objects created in CAD software. This kind of visualization will be tremendously helpful with public involvement. These techniques have been applied in various applications. Envision Sustainable Tools developed an educational software called QuestTM (http://www.envisiontools.com) for the purpose of supporting sustainable development through education to illustrate what sustainability is and how to achieve it. Six aspects or relevant perspectives of sustainability are examined: world view, politics, xiii

priorities, population goals and targets, economic goals and targets, and land use (suburban expansion, urban densification, mixed growth, and no change). Harrelson et al. (1998) developed a visualization tool for the purpose of evaluating redevelopment strategies for the Myrtle Beach Air Force Base. The visualization tool is built with World Construction Set (WCS) Version 4, a proprietary software package by Questar Production (http://www.3dnature.com/index.html) in Brighton, Colorado. The application can render GIS features such as roads, wetland boundaries, forested wetlands, and vegetation, and can populate terrains 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 with photographs, which eliminates the need to produce realistic surfaces and material rendering. The Urban Simulation team at the University of Los Angeles is in the process of creating a virtual model of 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 indicating single land use and 1 indicating good mix. Its computation involves dividing a zone into grid cells and averaging the entropy indices of the center cell and the cells surrounding it within a certain distance. In VOLUTI, the grid cell size is 448 feet (or 1/8 of a mile) and nine cells are used for averaging 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 ratio indicates a predominantly residential area. A large ratio greater than 1.5 may indicate a predominately nonresidential area. Average parcel size is an indicator of land use development intensity and potential for further development. In urban areas where high density development has occurred, the parcel sizes tend to be small. Large parcel sizes are an indicator that land use may be intensified by further subdividing the parcels therefore increasing the density. Open space measure is the park acreage per 1,000 residents by TAZ. The City of Miami defines the acceptable level of service standard with regards to recreation and open space as a minimum of 1.3 acres 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 use categories: agriculture, airports/ports, cemeteries, communications, utilities, terminals, plants, industrial institutional, 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 or decrease of the total area of a particular land use in each zone. xiv

Changes in total population, total employment, single family dwelling units, and multi-family dwelling 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 accessibility by highway and transit modes, respectively, measures the accessibility to opportunities in a region assuming driving as the travel mode. The opportunities may be employment or population (labor force). Accessibility has been recognized as one of the most important factors that affect both land use 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 land use mix and of potential demand on transportation facilities as none or little service availability means that people will have to travel far to meet their needs instead of possibly walking or bicycling to these destinations. Local accessibility to essential services is defined as a zonal index, computed as the ratio of the total employment in businesses that provide “essential services” in a zone to the zonal population. Mobility is measured by travel times. In VOLUTI, a user may display a contour map of highway and transit travel times for any selected zone. The travel time data are produced from the 1990 MiamiDade 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 transit travel times may be by mode or for all modes. Additionally, the differences in highway and transit travel times may be compared to identify areas where transit services are weak. 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 land development). The contour maps provide a general 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 an important measure of transit service quality. Transfers have negative impact on service quality as well as on ridership because of the inconvenience and delay involved. Information on transfers is useful 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 transit services may be identified and possible improvements can be investigated. The number of transfers is obtained by finding the shortest path between a zone pair considering the penalty applied and then determining 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 three scenarios include low, medium, and high projection series. The population and housing projections xv

were made based on the 1990 census data and population and dwelling unit projection series at the census tract level prepared by the Miami-Dade County Department of Planning. Jobs and commercial development projections are made based on assumed jobs-to-housing ratios. Recreation and open space projections are made according to the acceptable level of service standard for the City 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 land development projects on the transportation system and vice versa. This improvement is made in two ways. First the user can select and define property parcels for development and specify land use intensities, then evaluate the impact of the development in terms of increased traffic volumes, the volume over capacity ratio (V/C) in the transportation network FSUTMS, and the accessibility measures. The second approach allows the user to modify the transportation system and evaluate the system performance. Since VOLUTI is not an integrated model for transportation and land use planning, the interactions between land use and transportation cannot be fully captured. The interaction 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 is typically referred to DRI analysis, or analysis of Development of Regional Impact. The methodology used 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 are preliminary in nature, and can not be taken as a DRI analysis normally conducted by engineering firms. An actual DRI analysis will require much more detailed information. Information about transportation improvement projects, either having occurred since the last FSUTMS model update, having been committed, or being anticipated, must be collected and the transportation network edited accordingly to reflect the conditions of the transportation system at the expected time of the land use project. Similarly, land use changes must also be accounted for to reflect the land use conditions at the expected time of the land use project. To perform DRI analysis in VOLUTI, land development projects must first be defined. A development scenario is defined as projects located in a number of new TAZs, each of a single land use such as single-family residential, multi-family residential, shopping center, etc. VOLUTI currently 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 in FDOT 1997 Site Impact Handbook. The analysis involves three tasks: estimation of trips generated by 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 model output 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 option in VOLUTI. The results of a site impact analysis are given as maps showing changes in traffic volumes by network link and changes in volume over capacity ratios by link. Because the model is outdated, the results 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 with limited knowledge of ArcView or GIS to use VOLUTI, it is designed as a menu driven program, in which all queries may be made by selecting from the menus. Some customized tools are added to allow the user to interact with a map, such as selecting a TAZ or a network link. The standard ArcView menu is customized with additional menus and menu selections. They are: View menu includes a theme (layer) manager, image background control, Overtown boundary display, geocode one address, clearing matched address, set and show default display area, redrawing maps, 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 dwelling units, and multifamily dwelling units, single family vacant dwelling units and multifamily vacant dwelling units dwelling units, dwelling units per acre. This is a measure of density or land use intensity, 1998 land use, land use composition in a region, building stock, land use mix and job/housing balance, zdata change (1990/1999), average parcel size and park acreage, sales price history (one property), assessed value (one property), assessed value (region), public facilities near a 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 well field 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 by census block group. The data that can be displayed by TAZ include population density, population of age 16 and younger, population between the ages of 16 - 65, population aged over 65, singlefamily population, multi-family population, commercial employment, service employment, industrial employment, total employment, employment density, and school enrollment. The data available at census block group level include population, population density, number of housing units, vacant housing units, median rent. Buffer analyses can also be performed on these data. Transportation Facilities menu supports queries related to types of transportation facilities available and selected attributes of roadways. The types of transportation facilities include public transit facilities including bus routes and bus stops, limited access highways, principal arterials, collectors, xvii

and railroad tracks. The roadway attributes include number of lanes, 1996 average annual daily traffic (or AADT) on state roads, traffic volume from the 1990 FSUTMS model, and 1996 level of service (LOS) on state roads. Accessibility menu displays travel time contours (highway and transit), transit-highway travel time differences, transfers required for traveling between zones by transit, regional accessibility to employment 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 impact information. 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 sense of the number of opportunities and transportation system conditions. A DRI analysis tool has been implemented to perform quick and preliminary assessment of impacts of land development projects on the transportation network as well as accessibility. To further enhance the tool and make it easily adapted for other localities, the following issues need attentions and in some cases improvements are recommended. 1.

GIS Data Maintenance and Availability. GIS applications are data intensive. Not only a significant amount of data must be available initially, they need to be updated continually if VOLUTI is to be useful a few years after its initial installation. There are several problems that will hinder the data maintenance effort. There is a fragmentation of data sources and a lack 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 and municipalities, respectively, and close coordination between the county and the local governments to make arrangements on data collection, maintenance, and sharing. This will be a long process, and will require some changes in the business processes. The advancement in information technology in recent years is moving the businesses in that direction with more data sharing. For instance, more data are becoming available on the Internet. However, a true enterprise GIS database will take a long term effort and a great deal 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 once it is calibrated. The 2025 model should also be added. The transit mode needs to be included to evaluate at system level the development impact on transit ridership and to investigate 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 all the possible land uses, which should be added. Another issue to be investigated is the interpolation between the base year and future year models. Land use projects are typically planned with a time frame of several years to over ten years, which are unlikely to occur in the model base year or the future year. It is necessary, therefore, to reflect the conditions at the project implementation time. Such conditions include demographic (e.g. population, household size, dwelling units, etc.), socioeconomic (mainly employment information), and transportation system (roadway changes, additional transit services, tolls, parking fees, etc.). Some of the information is not readily available in digital format at present, and some does not exist. To perform such an estimate 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 model has also adopted a lifestyle trip generation model, which consider such variables as presence of children in households and number of workers in households as the basis of determining the number of trips produce by households for different trip purposes. Methods for estimating 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 be easily forecast, such as fuel costs. Information on transportation improvement projects that have been carried out or expected to be completed around the time of the development projects to be modeled may be continually collected and a database constructed, which may be used in model network update. The database should be spatiotemporal in nature, i.e., both project location information and specifics about the projects need to be coded. Programs may be developed to automatically take information from the database and the model network may be updated for any given time. 3.

Evaluation of Scenarios. Procedures and tools should be developed to allow different scenarios 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 as ULAM. This link will allow a better understanding of the impact of transportation on growth, that is how transportation improvements will affect growth in population and jobs in different areas.

5.

Accessibility Measures. Population and employment resulted from new developments should be added to existing TAZs before accessibility measures are update to reflect the improved accessibility due to new developments.

6.

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

providing more analysis functions. Examples of queries and analyses that support decision making may be to evaluate potentials of land for development, identify land development opportunities for a given goal or objective, determine adverse factors that may make a development project questionable or increase the cost significantly, and perform transportation equity analysis. 7.

Visualization. While virtual reality remains to be an expensive technology and is unlikely to be practical on a large regional scale, the visualization may be further enhanced. One potential type of data that can be used for visualization is the video logs that FDOT routinely collects on all the state roads. Presently, the LOS measures and display of operating conditions are only available for state roads. The possibility of adding the capability of showing 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 require more detailed information that is not currently available in VOLUTI. A simplified algorithm that gives a preliminary evaluation of LOS may be developed. Additionally, it is possible to develop a methodology to categorize the local highway operating conditions based on typical roadway configurations, intersection configurations, signalizations, and traffic volumes to display video clips for different operating conditions. This will make it much easier for elected officials and the public to understand how the transportation system is functioning or what impact development projects will have on the roadways. For developments at a scale smaller than regional ones, three-dimensional models of buildings and roadways may be useful for visualizing the aesthetic effects of highway or development projects. This may also be achieved with two-dimensional graphics. For instance, 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-oriented ArcView program, released in May 2001 by the Environment System Research Institute (ESRI). Although for the foreseeable future, Arcview 3.X versions will continue to be supported by its vendor due to the large number of existing ArcView applications, ArcView 8 will certainly gradually replace ArcView 3.X versions in the future. To make VOLUTI portable to different localities that use different databases, a mechanism to 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 different databases and their structures, and determine what functions should be available or how the functions 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 free distribution 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 of Transportation, District VI called Transportation/Land Use Visualization Project in 1999 (York et al. 1999). The project offers a significant advance in transportation/land use visualization programs based on the best practices of transportation planning organizations around the State of Florida. The study investigated the best practices in integrating land use and transportation planning in Florida through a survey and analysis of the survey results. The survey results indicated that while most transportation and land use planning organizations recognized the importance of linking both planning fields, few had been able to incorporate this link into their practices. This was due to several reasons including, e.g., lack of institutional coordination between the transportation and land use planning organizations, difficulty in building consensus among many municipalities affected by large 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 that visualization was a useful tool for communicating with communities to convince the public of the benefits of some of the changes. In fact, maps, aerial photographs, digital photographs, Internet, and even 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-D animation was considered an attractive technology because it had the ability to create realistic 3-D scenes and animation effects. However, even though the technology is mature, its use is still rather expensive and is not affordable for the project. To study the feasibility, technological options, and design and implementation issues, a prototype software program called VOLUTI (Visualization of Land 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 such as digital photography, and dynamically generated maps to help decision-makers and the public understand 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 from several 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 knowledge of 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 from FSUTMS models (Florida Standard Urban Transportation Model Structure), its capabilities were limited 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 described in this report.

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2.

RESEARCH OBJECTIVES

Building upon the previous research findings and tools developed for the prototype version of VOLUTI, this project continued to develop an integrated GIS-based tool that includes more land use and accessibility measurements, and additional functions related to assessment of impacts of land use developments and transportation projects. These improvements involved incorporation of additional data sources, development of land use and accessibility indicators, development of land use scenarios, and a stronger linkage between VOLUTI and FSUTMS (Florida Standard Urban Travel Model Structure), the standard travel demand model in 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 County Empowerment Zone neighborhoods, was chosen for the project for demonstration purposes. The project attempted to achieve the following objectives: (1) (2) (3) (4) (5)

Identify and collect additional useful data for the study area that allow the enhancement of VOLUTI capabilities; Identify potential sources of historical data that may be used to build temporal GIS and databases to support longitudinal analysis of land use and transportation; Enhance visualization capabilities in VOLUTI; Develop a set of measurements for the evaluation of land use and transportation; and Improve the link between VOLUTI and FSUTMS.

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3.

BACKGROUND - THE OVERTOWN STUDY AREA

Overtown is a small community of less than one square mile at about 470 acres. Figure 3.1 shows the boundary of the overtown area. Despise its size, it has a rich history within the context of the City of Miami. In the late 1800s as Miami was being built, "Colored Town" was established on the west side of the Florida East Coast (FEC) railroad tracks. Specifically restricted through segregation statutes, Overtown was the only area in which blacks were allowed to purchase properties. In about 1940, though the official designation of the area was the Central Negro District and the historical references were to Colored Town, the neighborhood's popular name became "Overtown," which developed 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 (Dunn 1997).

Figure 3.1 Miami-Dade County and Overtown Boundary

The neighborhood developed its own subculture and many businesses, and individuals thrived in the area 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 by public interventions, have served to debilitate the community since its heyday during the 1920s to 1940s. Three clear periods of decline were identified in the 1998 Transportation Impacts Study by the FIU Institute of Government (IOG). Throughout these phases, rumors and threats of freeway and downtown 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 of population 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 as many as 30 small wooden "shotgun shacks" to an acre in Overtown. During this period, the community experienced a gradual rate of destabilization and business decline, mainly due to the forces of integration, school desegregation, increased opportunities for housing in the suburbs, and aggressive code enforcement; many homeowners sold their properties to slumlords who constructed the "concrete monsters" and created an increase in apartment living. Additionally, from 1950 to 1956, the State Road Board drafted a proposal for an elevated highway that would run into the downtown area to alleviate the traffic problems there. Residents and businesses were displaced and relocated 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 most consequential of which were the construction of the I-95/I-395/SR836 highway interchange and Urban Renewal. Both projects displaced the residents and commercial activities, weakening the economic base and employment centers within and near Overtown. While in the first phase the most affluent and economically active population moved away, opening the community to economic vulnerability, 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 population destabilized the social cohesion of the community. Absentee landlords and speculative land owners put 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. The freeway divided the community into four quadrants, separating a primarily residential area from the business 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 the population 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 passed before new structures were built, resulting in vast amounts of vacant tracts of land that were eventually 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 had mitigation and reinvestment taken place immediately after the impacts were apparent or had strategies been developed in response to the problems that they caused. However, no public intervention 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 lack of revitalization that has created a fragmented, impoverished, distressed neighborhood. Public efforts to improve the neighborhood, due to their sporadic, inconsistent, and uncoordinated character, have fallen far short of attracting the level of private capital necessary to help the community recover. An increasing amount of land has been acquired by churches, community based organizations, and the government through housing development efforts, condemnation through code enforcement, and taking properties that had liens against them. Several community leaders living in exile desire to 4

re-create the community they knew in the prosperous pre-War era but have no faith in the existing government structures or public decision-makers. Especially in the latter half of this phase, a great many economic development strategies have been developed and implemented with no significant impact on the community and no private, non-subsidized investment. Successful economic development efforts may continue to be elusive in this community until open communication among trusting 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, mainly consisting of low- to medium-density multi-family housing including a large proportion of public and cooperative housing units. A larger percentage of land is publicly owned and therefore off the tax rolls (City of Miami 1993). In addition, there are many "concrete monsters" remaining from the apartment 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 establishments in 1949 to only 31 in 1989. There are two important commercial corridors, including the length of NW 3rd Avenue, and NW 3rd Street between NW 3rd Avenue and Miami Avenue. Other commercial properties are scattered throughout the residential sections. There is also an important industrial 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 on this latter phase (City of Miami 1993). It states that the overwhelming need in the Overtown community has attracted a great deal of attention and money during this time, with little to show for the efforts. Economic development projects have had minimal success; for example, the Southeast Overtown Park West Redevelopment Area was expected to provide financial spillover due to the investment 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 and substandard housing for both single and multifamily something, a lack of resident homeowners and an abundance of transients and renters, overcrowding, and absentee landlord ownership. The PB&Z calls for stringent code enforcement, demolition and replacement of substandard multifamily housing 5

and the construction of new, affordable multifamily housing, and incentive programs designed both to encourage developers to construct new multifamily housing under the mixed-use paradigm as well as to attract younger and more affluent families to the area. Some of the problems that commercial enterprises face with respect to economic development include 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, the development of a strong center of specialty shops (like the Grove Village or the French Quarter in New Orleans) through the development of the Overtown Folklife Village District to improve the commercial aspect of economic development. In addition, there is a great need to build the skill-level of the current local residents; the PB&Z suggests developing technical and vocational training facilities, simplification of the enterprise zone application process, year-round employment for youths and a "head-hunter" service to improve residents' chances of getting and maintaining gainful employment. Economic development in Overtown will have to overcome a number of serious obstacles. A 1992 study determined that there were no unassigned vacant sites available for development around the Overtown Metrorail station, and that the several vacant sites near the Culmer Metrorail station were encumbered due to the private market financing interests (FCUDR 1992). Private investment has been unavailable because historically there has been no governmental priority for community preservation 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 outside investment. In addition, the lack of private ownership and wealth in the area inhibit traditional investment opportunities. About 40% of the property is owned by the government (most of it housing) and off the tax rolls-most of the multi-family housing is government owned and semi-privately managed and operated. 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 Miami 1993). Especially relevant to this project, the PB&Z assessed public transportation as providing excellent accessibility by Metrobus and Metrorail, as well as by the availability of other public and private services (City of Miami 1993). However, they found that the community could greatly benefit from a decrease in the price of fares and from the construction of the additional East/West and North/South Metrorail corridors. The welfare-to-work study (Dluhy and Topinka 1998) indicates that access to the Airport and Miami Beach via an East/West line and to North Dade County and the North Miami Beach/Aventura areas via a North/South line will help ensure that jobs located in these areas are available to Overtown residents on a practical level, because a substantial problem for 6

Overtown residents is the amount of time needed to travel to these areas, particularly for those who are able to find employment in those centers and for shopping. Street improvements and beautification are the second aspect of transportation that the PB&Z identified. Problems here included 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 transit for all their mobility needs to a greater extent than do residents in the rest of the county. A study conducted by the FIU Institute of Government found that the Overtown/Liberty City study area was the neighborhood which was least likely to drive alone and most likely to use transit, and that the Overtown/Liberty City study area has the highest percentage of people who must commute more than five miles to their places of employment (Dluhy and Topinka 1998). A high proportion of residents rely on public subsidies, and this population has very specific transportation needs. For example, few participants of the Work and Gain Economic Self-Sufficiency (WAGES) program own a personal automobile, many need to make multiple trips, and most need to take very long trips due to the lack of commercial activity in the neighborhoods near their homes. None have the resources to spend much money on their transportation needs, and many need public transit services that operate 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 parks to large-scale, active community parks are in need of renovation and rehabilitation. At that time, all of the parks needed lights for nighttime games and activities to also help reduce possibilities of crime and violence in the area. Coordinating the community use of school open space and recreational facilities after school and on evenings and weekends were also recommended. In addition, the PB&Z called for the removal of Range Park No. 1 from underneath I-95 to be redeveloped for a different use. Several other public utilities needs were also addressed by the PB&Z report. Problems with regard to water, sewer, drainage and lighting facilities included the inadequacy of sanitary sewers (Pump Station No. 5 in particular), localized storm drainage (which are improved as the streets are upgraded), and inadequate lighting in the parks and on several streets. In terms of solid waste, there was found to be a need to control illegal dumping of rubber tires and construction debris, auto abandonment, excessive accumulation of garbage due to overcrowding of units, litter along sidewalks 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, aesthetics and urban design, and quality of life issues. Forums that have allowed for public participation bear this out. For example, the South Florida Regional Planning Council held a charrette in the Overtown area in July 1999. Their mission was "to engage the entire community in creating a unified vision for the residential and commercial renaissance of Overtown. The vision aims to restore Overtown as a destination and to higher levels of self-sufficiency and economic and social viability" (SFRPC 1999: 1). They found that the local participants are very interested in immediately implementing physical improvements based on historical, aesthetic, and quality of life concerns. 7

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 Town Park Housing New Markets project in Overtown. The EZ Trust has been awarded $10 million from the Miami-Dade County Housing Agency to support this project. The Trust’s objective is to develop a mixed income, single detached and semi-gated community in Overtown (Dade County/Miami Empowerment Trust 2000). The Overtown Neighborhood Assembly has made a clear commitment to economic development by being the first Assembly to pledge 100 percent of its funds ($200,000) to the Empowerment Trust Micro-Loan Fund (ETML Fund). Applications for funding were released on July 26, 2000. Private investment pledges to the ETML Fund and the number of new jobs to be created by this program has not been determined (Ibid). In July 1999, the Overtown Advisory Board, Eastward Ho! (which encourages redevelopment in interior neighborhoods rather than westward) and other agencies sponsored a design charrette in Overtown. The charrette was a formal week-long brainstorming session where residents worked with designers, town planners, and government officials to design a new Overtown. The Overtown Redevelopment Area Design Charrette Report published in March 2000 proposed the creation of a center for Overtown and the redevelopment of commercial and entertainment districts among other developments. However, no funding was identified to make the citizens’ vision a reality (Treasure Coast 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, which includes 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-term prospects for economic growth while at the same time protecting natural resources and environment (Colgan 1997). This approach attempts to integrate environmental and economic decisions and planning across sector functions in a long-term framework in which recognizes that borders are meaningless 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 as rehabilitation, reclamation, and enhancement of natural ecosystems for higher productivity are addressed (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. Public space, open space, a jobs-housing balance in number and variety, connectivity, and efficient and practical use of geography and passive solar energy are highly regarded concepts (Center for Livable Communities 1999). These are in fact guiding some of the economic and community development initiatives in the Overtown area (see for example, SFRPC 1999). Public participation is one of the cornerstones of sustainability theory. Every individual shares the responsibility and is accountable for the stewardship of the economy and the environment for the benefit of present and future generations. This requires that government agencies provide the public adequate, accessible, and timely information and requires understanding and respect for differing social and economic views, values, traditions, and aspirations. All individuals and government elected and appointed officials share this responsibility in a spirit of partnership and open cooperation (Colgan 1997, Center for Livable Communities, 1999). Public participation in the decision-making process provides the foundation for implementing policies and developing strategies that promote sustainable communities. Meaningful participation ensures that individual community members will take responsibility for the outcomes of the projects-they will have had the opportunity to decide and design them and will have a stake in the ultimate success or failure. Although it is a critical component to any economic or community development 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, address specific cultural issues to facilitate communication.

•

Use liaison organization to link neighborhoods with respect to regional issues and area-wide planning.

•

Recognize the limitations of traditional public hearings and comment periods; use innovative approaches to elicit involvement and recognize that what is practical for the planner may serve to 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 and announcements at prime time.

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

Meaningful partnership roles for neighborhoods on basis of self-help development: enabling the neighborhood residents, property owners, and businesses to become capable and responsible to develop 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 make specific projects work out.

•

Decisive/reliable governmental priorities and partnership commitments based on sound paybacks to tax payers: in the era of "do more with less," public-private partnerships can be used to forward 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 market place due to the encouragement of initiatives by property owners, local businesses, developers and lenders.

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

10

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 only recently been seriously undertaken and many would argue that it is still a long way from the ideal described here. However, several initiatives are on the table that utilize the community design and the 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 and travel behavior. How to define and measure accessibility has attracted the attention of many researchers and many forms of accessibility measures have been developed, which Richardson and Young (1982) classified into a spectrum of accessibility measures as shown in Table 4.1. Table 4.1 Summary of Accessibility Measures Accessibility Measures Topological

Features indicates if two points are connected by a transportation link

modal accessibility

the degree of connectivity of two places depending on the modes available. This is consistent with the PPMS concept.

temporal accessibility

accessibility varying during different time periods (e.g. transit service 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-way traffic rules, and denial of access to the transportation system to certain population groups).

Relative accessibility

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

Integral accessibility

ease of travel between one point and multiple different points based on travel time or cost

place-accessibility

only spatial separation between one place and other places accounted for

activity-accessibility

activities at destinations accounted for explicitly

cumulative opportunity index

number of opportunities (e.g. jobs) reachable from the origin within a 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 maximum utility to be gained in destination choice situation

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The logit model logsum term is given by: m

Ai = ln å e c ( Bk - Cik ) k =1

where Ai is the accessibility index, Bk is the benefits gained by participating in activity at site k, Cik is the cost of travel between sites i and k, and c is a sensitivity coefficient. Richardson and Young considered one major deficiency of the above measures of accessibility being that in the calculation of accessibility of a point within a region, it was assumed that all trips that contribute to the accessibility of that point start from that single point. Instead they proposed that for linked trips, the choice 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 preceding destination, and so on. For a linked trip with two destinations, the linked accessibility of a site o is given by LAo = ln 2 + Bx + By + Bo - Cox - Coy + Cxy

where LAo is the linked accessibility of site o, Bi (i = x, y, o) is the benefit to be gained by participating in activity at site i, and Cij (i = o, x and j = x, y) is the travel cost between sites i and j. It was demonstrated that in the case of two-destination linked trips, accessibility calculated as the logit model logsum term will be significantly underestimated when the origin is far from the center point between the two destinations. In other words, as the distance between the destinations and the origin increases, the linked accessibility will better reflect the benefit of making a linked trip, which reduces the travel time as compared to two unlinked trips. One important implication is that the accessibility of a suburban resident may be improved by linking trips and thus long distance from the urban core may not be as large a deterrent to urban sprawl as expected if unlinked accessibility is used. Allen et al. (1993) considered that the relative or integral accessibility in its original form or modified forms was not able to reflect the overall accessibility in an area. Consequently, they developed an area accessibility measure that was based on the average of the integral accessibility of a set of random points to other points in the area, and showed that if a rectangular area of dimensions of X and Y was divided such that there were I and J equally spaced internal points in the rectangle, respectively, then the average accessibility, E, will be E=

X X Y Y + + + 3 3I 3 3 J

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

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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 activity centers. Zonal attractiveness may be measured by total employment or commercial and service employment. The friction term f(tij) often assumes an exponential form with coefficients estimated by 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 to recent policy initiatives at the federal, state, and local levels to look for ways to improve mobility and reduce congestion without building new highways. These policy initiatives are motivated by the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA), which provided new funding opportunities for transportation improvement projects not targeting single-occupancy-vehicle (SOV) mobility, the Transportation Equity Act for the 21st Century (TEA-21), which initiated a new sustainable development pilot program to help state and local governments plan environmentally-friendly development, the Clean Air Act Amendments of 1990 (CAAA), which sets vehicle miles traveled (VMT) as a form of mitigation to meet air quality attainment, rising public concerns about petroleum consumption in the U.S. and global warming, and political pressure to reduce fuel consumption. One of the approaches to reduce VMT is to change travel behavior via policies such as taxation, pricing, and land use planning. The question is therefore whether land use policies that encourage “transit/pedestrian friendly” neighborhoods will be effective. Researchers have been attempting to answer this questions by looking into land use factors and their links to travel behaviors. One of the most influential works may be that by Pushkarev and Zupan (1977). They investigated the impact of land use, spatial separation, and transit service quality on transit ridership. The land use 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 between the CBD and the residential areas. By comparing different bus routes, the authors found that there was a significant correlation (0.75) between transit use and density. There is a four percent increase of workers using transit for every doubling of density. Their analysis results led to several interesting findings: 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.” They pointed out, however, that the higher transit ridership was not induced by density per se, but due to increased availability of employment and other opportunities, as well higher parking cost and more congested roads that have limited capacity to accommodate automobiles. In another study of the 1979 New York Urban Region survey data, Pushkarev and Zupan concluded that “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).

13

By simple regression, Newman and Kenworth (1989) also found high correlation between automobile use (measured by petroleum consumption) and density by studying major cities around the 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, government policy, 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 travel behavior. An empirical study was performed by Frank and Pivo (1994) to determine if density was a proxy of other factors or itself caused a difference in mode choice, with the purpose of discovering ways to implement urban forms that promote accessibility in urban areas. By analyzing mode choice for work 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 surveyed household and mean age of survey participants per census tract, and other non-urban-form variables including proportions of survey participants with a driver’s license, mean number of vehicles for survey participants ending trips in a census tract, and proportions of transit trip ends made by survey participants employed outside home, by those participants who had a bus pass, and by those who had access to less than one vehicle, respectively. The land use mix was measured by an entropy index defined as follows:

entropy = - singlefamily ´ log10 ( singlefamily) + multifamily ´ log10 (multifamily ) + retail and services ´ log10 (retail and services) + office ´ log10 (office) + entertainment ´ log10 ( entertainment ) + institutional ´ log10 (institutional ) + industrial/manufacturing ´ log10 (industrial/manufacturing ) Multivariate regression analyses showed that urban-form variables entered after including significant non-urban-form variables in the models did contribute to mode choice, with positive impact on transit use and walk and negative impact on SOV use, respectively. The analyses also suggested that employment density at both trips ends should be used to explain the variation in mode choice instead of using the density at one trip end. Additionally, land use mix seemed to better explain the choice of walk mode. The property of the functions that relate the urban-form variables to mode choice was also investigated. The authors suggested that such functions are non-linear in nature. Plot of mode choice versus gross employment per acre was created and from the plots the authors determined that

14

significant shifts from SOV to transit use and walking occur between an employment density of 20 and 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 of population density and income on modal split (Kockelman, 1995) . She showed that density (or other factors proxied by density such as land prices, parking fees, transit service frequency, and congested roadways), not income, was the influential factor on modal split. The study analyzed data of 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. By analyzing census tract data using single variable regression, the percent of workers not driving alone was found to be significantly related to density (correlation 0.891 and R2 0.794), but not so to income level (correlation -0.289 and R2 0.084). Density and income are not significantly correlated. In multiple regression analyses, a destination index is used to serve as a coarse proxy for transit levelof-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 different cities. The weight for San Francisco is 10 while 3 is used for other cities (Berkeley, Palo Alto, and San Jose). The multiple regression results again showed that density and destination index are more important than income levels. The elasticity of percent of workers not driving alone is +0.35 with respect to residential population density, -0.10 with respect to income, and 0.2 with respect to the destination index. Not included in the model are working place parking policies, congestion along traveled routes, access to alternative modes, land use mix, trip length and cost, and transit service supply (destination index is a crude estimate of transit service availability), and non-work trips, all affecting 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 dummy variable for access to the BART system, the rail rapid transit. The regression models suggest an elasticity of +0.35 for density, -0.25 for income, and +0.17 for BART access, respectively. Although BART access appears to have a significant impact on single vehicle occupancy, Kockelman conceded 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, and vehicle ownership are important factors in determining the mode choice for residents near the BART stations. Kockelman (1997) investigated the link between urban form and travel behaviors and concluded in that accessibility, land use mixing, and land use balance were all statistically significant and influential to travel behaviors, including mode choice. In addition to the accessibility index described in the accessibility section previously, other measurements used are briefly introduced below. Pj ´ ln( Pj ) Entropy = - å j Entropy (Land Use Balance) ln( J ) where Pj is the proportion of land development of the jth type and J is the number of different types of 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 15

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

Mean entropy = - å k

å

Pjk ´ ln( Pjk ) j

ln( J ) K

where K is the number of actively developed hectares in a census tract, and Pjk the proportion of land use type j within a 0.8-km radius of developed area surrounding the kth hectare. 1 8 X ik k Kåi 8 where K is the number of actively developed hectares in the census tract, and Xik is a dummy variable that assumes 1 if the central active hectares’ use type is different from that of a neighboring hectare, and 0 otherwise.

Dissimilarity Index (Land Use Mix)

mix index =

å

Linear regression models relating vehicle kilometers traveled (VKT) per household and different sets predictors showed that the inclusion of the accessibility, entropy, and land use mix indicators significantly increased the R2 when compared with models that only included household size, income per household member, and auto ownership. In the logit mode choice model, the inclusion of accessibility, population density, and employment density (all measured at both the origin and destination 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, auto ownership, household size, and member income as explanatory variables. Analysis of the elasticities of independent variables with respect to household VKT (total and non-work home-based) and mode choice shows that these variables are highly sensible to accessibility (e.g. with an elasticity of -0.35 for non-work home-based VKT and 0.22 for walk/bike choice). Land use mix and mean entropy are also 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 this study, the limitations of simple regression or logit models in determining the direction of causation have been recognized by the author, who contended that a structural model may be able to better explain the causation. A similar study by Sun et al. (1998) also used a similar approach. Using the 1994 Portland Travel Survey data, density (population, employment, dwelling units), land use mix, accessibility, annual household income, household size, dwelling type, number of phone lines in a household, presence of 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:

Accessibility = e ln(Y ) - 0.175T + 0.009 T

16

2

+ 0.000009 T 3

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 was independent of household income. To determine if density and land use mix is due to the choice by people with certain income levels, histograms were plotted. The authors claimed from the histograms that low income households have a slightly higher concentration in high density areas and areas with better land use mix, there was no fundamental difference in household income distribution in different types of neighborhoods. Regression analysis showed that density and land use balance make little difference in the number of daily trips but has a significant impact on house VMT. High density and high entropy both contribute to a reduction of VMT (by 19 percent and 45 percent, respectively). In a study of Miami-Dade County in Florida, Messenger and Ewing (1996) established two sets of simultaneous equations by place of residence and by place of work. The first set relates transit share by place of residence to zero or one automobile households, land use mix/balance, and bus peak frequency; 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 residential density to zero or one automobile households, logarithm of overall density, a variable rating street network resemblance to a grid system, and a dummy variable indicating proximity to the rail rapid transit, respectively. The second set relates transit share to morning peak bus run time to downtown and zonal average seven-hour parking cost; and the parking cost to logarithm of overall density, a dummy variable indicating a zone is part of the downtown, and proportion of jobs in commercial and service sectors, respectively. The equations are simultaneously estimated by a full-information maximum likelihood method. The first set of equations (based on place of residence) has a better explanatory power (R2's ranging from 0.34 to 0.49) than the second set (based on place of work) (R2's ranging from 0.11 to 0.38). From the estimated equations, it was decided that the density needed to support a 25-minute bus headway was 8.4 dwelling units per acre (1.4 higher than that proposed by Pushkarev and Zupan) at the transit operator’s minimum productivity and 19.4 dwelling units per acre at the system wide average productivity. Additionally, different factors affect transit use at different trip ends. Bus mode share at trip origins is primarily a function of low automobile ownership, and secondarily of job-housing balance and transit service level, although job-housing balance 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 a function of parking cost, overall density, and access to downtown. The models of trip end transit mode share only explain a small portion of the variation in the data, indicating that other factors need to be identified. In an attempt to determine if land use truly has a causal relationship with travel behavior or whether it is other socioeconomic, demographic, and transportation supply characteristics, which are also associated 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 San Francisco Bay Area (SFBA) and investigated the travel behavior variables and a wide array of variables that are objectively or subjectively measured. The five neighborhoods are approximately 17

one square mile in size. The medium zonal income was held relatively uniform to control the effect of income on travel while other characteristics such as land use density and mix are chosen as to represent extreme conditions. The travel behavior is measured by number of trips, number of transit trips, and number of non-motorized trips per person per day, and the mode shares. Data about the sites were collected, which included street characteristics (width, sidewalk, bike lanes, speed limits and other traffic control devices), public transit service (bus stops, service frequencies, etc.), location and type of commercial developments, parks and other public facilities, and general neighborhood characteristics (for detail see (Kitamura et al., 1994)). Dummy variables were used to represent access to rail transit, mixed land use, high density, presence of sidewalk, presence of bike lanes, backyard, available parking space, house ownership, sex, homemaker, student, professional, low education level, college education, graduate degree, high and medium personal income, respectively, apartment, single-family home, and responses to an array of questions related to reasons for staying in the area (no reason to move, streets pleasant to walk, cycling pleasant, good local transit, enough parking, and congestion problem). Measured variables include distances to nearest 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 the variables 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 as parking availability negatively impact total number of person trips, and high density, proximity to parks and bus stops, access to rail transit stations, and presence of sidewalks encourage nonmotorized travel. Furthermore, attitudes (pro-environment, pro-transit, suburbanite, automotive mobility, time pressure, urban villager, TCM, and workaholic) were determined to have more significant impact on travel behavior than socioeconomic and land use characteristics, with land use characteristics being the weakest predictors. This is of particular interest because current modal split models do not include them as determinants of mode choice. These variables may also account for the some of the unexplained variability in transit mode choice since we know people are not always as rational as assumed in logit models in which a trip maker is supposed to make a mode choice by maximizing the utility of the trip, which involves comparing the generalized costs for a trip via different means. On the other hand, it is impractical to include such attitudinal information in the models 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 public transportation investment (Seskin 1996). The TCRP project attempt to answer the questions of how urban form influences the demand for light rail and commuter rail transit and how transit influences land uses. Urban structure, employment and residential densities, land use mix and urban design were found to influence transit use. However, although land use mix and urban design was significant in explaining transit use, individual land use and design was not. Also, density is more powerful than land use mix and urban design in explaining transit use. On the other hand, the influences of transit on urban form were described by using the following four factors: property value, intensity of development, urban structure, and timing of development. First, accessibility to rail transit typically results in higher residential and commercial property values and rents. Second, 18

although rail transit has impact on transit station areas where transit confers a distinct accessibility advantage on a location, the effects are varied among different networks. Third, both CBDs and subregional centers have benefitted from station-area development. Finally, major rail investments can accelerate development in station areas. 4.4 Neighborhood and Urban Design Neotraditional neighborhoods are characterized by a closely spaced street grid, high density, and location often near street car tracks. Such neighborhoods are often older and built before the end of the World War II. There has been much debate as whether urban design has any impact on transit use. Some argue that neotraditional neighborhood design encourages walking and transit use, while others 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 local accessibility indices. The indices are based on the gravity model and are proportional to local (or regional 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 at superdistrict (34 in total) level and used for analyses. Handy found that two to four more bicycle and walk trips were made by residents in two areas that closely resemble neotraditional neighborhood than by those living in areas that are automobile oriented . She did not conclude if these trips by non-motorized modes actually replace some of the automobile trips or the neotraditional neighborhood simply encouraged more walk and bicycle trips. The approach of analysis based on accessibility indices have several weaknesses. Firstly, the use of superdistricts may mask the variability of accessibility in different parts of a zone. Secondly, the local accessibility is easily affected by the choice of zonal boundaries, which are somewhat arbitrary. Another limitation of the study is that the trip data did not distinguish convenience shopping (happening mostly locally) and comparison shopping (often at regional centers). Therefore it is impossible to evaluate 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 may affect travel patterns are not controlled in the study. Cervero considered a fault of many comprehensive studies on the relationship between built environment 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 Los Angeles, he carefully paired “transit neighborhoods” and “auto neighborhoods” by a set of selection criteria (Cervero, 1994). The “transit neighborhoods” are defined as initially built along street car lines 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 (over 50% of intersections being “T” intersections or cul-de-sac), and built after 1945. To match the auto neighborhoods with the transit neighborhoods, criteria controlling income, transit services, topography, and size are used. For an auto neighborhood to match a transit neighborhood, there can be no more than ten percent variation of medium household income from that of the transit neighborhood; there should be transit services (type and density) comparable to the transit 19

neighborhood; it should have similar topographic and natural conditions; and it should be located no more than four miles from the transit neighborhood. Additionally, an auto neighborhood also has to have a significantly lower percentage of four-way intersection cross roads and the net residential density lower than or equal to that of the transit neighborhood. By applying these criteria, seven neighborhood pairs in SFBA and six in Los Ageles were identified. A comparison of the SFBA paired neighborhoods revealed that while other demographic characteristics (such as mean vehicles per household, percent of white households, and mean age of residents) of the neighborhood pairs do not differ significantly, most auto neighborhoods have a higher auto ownership, produce much more drive-alone trips, have a lower transit use, and have much lower walk trip rates than transit neighborhoods, the latter being especially obvious. On average, transit neighborhoods generate around 70 percent more transit trips and 120 pedestrian/bicycle trips. This may be partly contributed to the fact that transit neighborhoods tend to have better transit service supplies (measured by daily VMT per acre). By comparison, the transit neighborhoods in Los Angeles do not demonstrate the same significant amount of transit use or reduction of single occupancy driving. Cervero contributed this phenomenon to the overall strong auto orientation in Los Angeles and believed that the positive effects of transit neighborhoods in such an environment are limited. To take his conclusion one step further, however, one may argue that the inconclusive relationship between transit neighborhoods and transit use in Los Angeles may be a result of inadequacy of the transit services, which is affected by the built environment. In SFBA, transit services are much more concentrated and at a much higher level in transit neighborhoods than in Los Angeles, perhaps due to the higher percentage of neighborhoods that qualify as transit neighborhoods. Not only does this attract people who desire to use transit to these neighborhoods, but this also allows the transit providers to provide a good level of service in a large area and increase the overall accessibility via transit. On the contrary, because of the dominant autooriented neighborhoods in Los Angeles, it is difficult to provide good transit services even to transit neighborhoods with the same efficiency and accessibility to opportunities. Using data of the entire Los Angeles area, Cervero also regressed the percent of transit trips against variables including gross residential density (households per acre), natural logarithm of household income, neighborhood type (auto or transit), and density interaction (product of residential density and neighborhood type), and achieved a R2 of 0.55. According to the model, all variables are significant at a significance level of < 0.001. In Los Angeles, everything else held constant, transit neighborhoods will generate 1.4 percent transit trips per every 1,000 households while those in SFBA will generate 5.1 percent transit trips. Another conclusion was 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 Los Angeles. Work trips by transit averaged 8 percent more 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 on reducing automobile dependency, were supported by Clifton and Handy (1998) in a study of six 20

Austin, Texas neighborhoods. The study explores the motivations for travel as well as the patterns of travel. Travel surveys and focus groups were used to study the travel choices of residents of the six case study neighborhoods. The results suggest that the role of urban form plays in travel behavior is not entirely straightforward, sometimes influencing travel choices directly, sometimes indirectly, sometimes influencing choices in the short term, sometimes in the long term, and sometimes not having any measurable influence on choices at all. In the end, it appears that certain land use policies can help to provide alternatives to driving, but that the reduction in driving is likely to be small. 4.5 Florida Sustainable Communities Network (FSCN) INDEX Software The Florida Sustainable Communities Network (FSCN) INDEX software is the result of the collaboration between the Florida Department of Community Affairs (DCA) and Criterion Planners/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 to establish base-line information and to measure the impact that development projects will have on a community. City and county governments participating in the DCA’s Sustainable Communities Network are encouraged to use this software as a tool to help them measure progress in their efforts to attain sustainability goals. Indicator scores are calculated for 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 add indicators as they see necessary and as data collection allows) as shown in Table 4.2. Table 4.2 Initial Indicators of FSCN INDEX Software Community Element

Land-Use

Indicator

Definition

Urban area footprint

Total community land area in acres per resident

Infill

Percent of building permits issued annually on property platted more than five years prior to building permitting

Use mix

Dissimilarity among one-acre grid cells containing predominant land use

Use balance

Proportional balance of land area among uses

Land redeveloped

Percent of designated land area redeveloped per year

Jobs/housing balance

Ration of jobs to dwelling units

21

Community Element

Conservation

Indicator Natural areas protection

Percent of total land area protected as natural area or equivalent

Agricultural land conversion

Acres of agricultural land urbanized per year

Imperviousness

Percent of total land area covered by impervious surfaces

Open space protection

Percent of total land area dedicated to open space

Density

Dwelling units per net acre of land designated for residential uses

Affordability

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

Transit proximity

Average travel distance from dwellings to closest transit stop in feet

Density

Number of employees per net acre of land designated for employment uses

Land supply

Percent of employment-designated lands that are vacant or redevelopable

Transit proximity

Average travel distance from businesses to closest transit stop in feet

Transit mode share

Percent of all person trips made by transit modes*

Bicycle network coverage

Percent of total street centerline miles with designated bike routes

Pedestrian network coverage

Percent of total street centerline miles with improved sidewalks

Street connectivity

Ratio of street intersections versus intersections and cul-de-sacs

Transit service density

Index of miles of transit routes multiplied by number of transit vehicles traveling those routes each 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/bike modes*

Water consumption

Residential water use in gallons per capita per day

Park space availability

Acres of park and school yards per 1,000 residents

Housing

Employment

Transportation

Facilities & Services

Definition

* From FSUTMS 22

The model built on these data is meant to provide government agencies with periodic community-wide indicator reports as well as to allow them to evaluate the incremental development that may have occurred within each period. The model can be used for broad-scale, comprehensive planning on areas that are 500 acres or smaller, for an intermediate-scale neighborhood master planning, and for site-specific, permit-level analysis in site planning, from one to 200 acres. Baseline information provides benchmarks by which future trends and progress can be measured, and potential 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 has been 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 the 26 "starter" indicators to 95. They are mainly interested in using the software at the citywide and neighborhood levels, and expect to be able to track individual changes in land-use and transportation policies in terms of the sum of their effects on the city. Overall, Tampa also gave the software high ratings. However, they found that the investment in data collection, the limitations with respect to the 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 of meaningful results for eight of the 26 indicators were substantial difficulties in the program. Despite these problems, they found that "the performance of the INDEX Template provided significant benefits that should reduce costs and time requirements over the long-run. It is a worthwhile planning 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 ideas concisely and identified several areas that warranted further examination. Criterion is a private company that modified this tool specifically for Florida; however, the tool has been developed for use across the nation. While there are many similarities between INDEX and VOLUTI in terms of land use indicators used and being GIS based, there are several main differences between 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 planning with area size ranging from specific sites to 500 acres while VOLUTI is design for both small and large areas; (3) While INDEX may be linked to a travel demand model, e.g. it uses model output to display mode shares and per capita VMT, its focus is on land use planning. VOLUTI emphasizes linkage between land use and transportation and therefore travel demand models have a much stronger 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 23

models. The Lowry and Lowry Derivative Models are based on the theory proposed by Lowry in his work “Model of Metropolis” (1964). The basis of the models is the assumption that, everything else being equal, place of employment determines place of residence. Constrained by regional employment and population totals, the model will allocate residence population close to non-service type of work places then allocates service employment to serve the population, which in turn requires the allocation of more residence for the service employees. TOMM model (Time Oriented Metropolitan 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 mathematical programming 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 by Hill et al. (see Putman 1979) combined regression analysis with simultaneous equations to estimate model coefficients using existing land uses. The last group of models are based on economics and markets. These models emphasize the location of housing and trade-offs between travel distance, density, and amenities. The National Bureau of Economic Research (NBER) model belongs to this group of models. A land use model that has been adopted by a number of metropolitan planning organizations in Florida is ULAM (Urban Land use Allocation Model) developed by Transportation Planning Services, Inc., 1998, at the direction of an advisory group of representatives from five counties in FDOT District IV service area. ULAM uses county-wide population and employment control totals and concurrency requirements to constrain growth, and automatically allocate growth to traffic analysis zones (TAZs) based on availability of vacant land, developable land, maximum allowable growth in each zone, allowable density, historical growth trend, etc. The link to transportation in ULAM 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 that help 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 an educational program to illustrate what sustainability is and how to achieve it. Six aspects or relevant perspectives of sustainability are examined: 1. World view: it may be set to one of the four: pessimistic, technology fix, optimistic, and social change. 24

2. Politics: different political setting including undecided, command and control, market based, and voluntary 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, and consequences 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 are five 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, urban densification, mixed growth, and no change. Different scenarios will be evaluated and results of qualitative and quantitative analyses are said to be displayed. Harrelson et al. (1998) developed a visualization tool for the purpose of evaluating redevelopment strategies for the Myrtle Beach Air Force Base. The GIS data were created from one-meter resolution satellite imagery, with ground truthing using GPS and in situ data. The visualization tool is built with World Construction Set (WCS) Version 4, a proprietary software package by Questar Production (http://www.3dnature.com/index.html) in Brighton, Colorado. The software is capable of creating photorealistic terrain modeling, rendering, and animation. The application can render GIS features such as roads, wetland boundaries, forested wetlands, and vegetation, and can populate terrains 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 if it 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 and landscape applications. An alternative approach to virtual reality modeling is to combine geometric models with photographs, which eliminates the need to produce realistic surfaces and material rendering. The Urban Simulation team at the University of Los Angeles is in the process of creating a virtual model of 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 including 2D overlay of orthophotos on maps, 3D visualization with geometric models, 4D visualization with animated geometric models, surface and terrain models, drape of orthophoto onto terrain, photo-simulation that uses photographs instead of 3D geometric models and rendering, animation of a series of image frames, and real-time virtual reality and simulation. The authors pointed out that 3D geometric modeling of a simple street scene could take 2-3 months of work and would involve intensive computation, while painting photographs over simple 3D models will reduce the work to 25

2 weeks. 3D modeling effort may be reduced by using predefined 2D and 3D geometric objects created in CAD software. Figure 4.1 illustrates a scene created using photo-rendering. This kind of visualization will be tremendously helpful with public involvement. Another study of using 3D modeling techniques to produce images of roads and bridges for public involvement is reported in Wallsgrove and Barlow (2001). They created street scenes using CAD such as MicroStation and 3D Studio.

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

26

Table 4.3 Software Packages for 3D Modeling and Visualization (from McGaughey 1997) Software Package

Visualization Technique

Scale

OS

Cost

Standard Visaulization System (SVS)

geometric modeling

plot

DOS

Free

UTOOLS and UVIEW

geometric modeling

stand or landscape

DOS

Free

SmartForest

geometric modeling

stand or landscape

UNIX

Free

Landscape Management Systems (LMS)

geometric modeling

all scales

Windows

Free

all scales

UNIX

Free

Gnu Image Manipulation Program (GIMP) video imaging USFS, Southern Research Station visualization system

geometric modeling

stand or landscape

UNIX

Free

Persistence of vision rayytacer (POVRAY)

geometric modeling

all scales

many platforms

Free

VisualFX

geometric modeling

stand or landscape

DOS

$$

CLRView

geometric modeling

stand or landscape

IRIX

Free

TrueFlile

image draping

landscape

Windows

Free, $$

Visual Explorer

geometric modeling and image draping

landscape

Windows

Free, $$

VsitaPro3

geometric modeling and image draping

landscape

Windows, Mac

$$

World Construction Set

geometric modeling and image draping

all scales

Windows, Mac

$$

27

Additional Information Reference

5.

USER FEEDBACK OF THE PROTOTYPE VOLUTI

To collect feedback on the first VOLUTI implementation attempt, a number of urban or transportation planners were interviewed. Presentations have also been made at a number of conferences, including the 1999 September Annual Meeting of the South Florida Section of American Planning Association held at Miami Beach, Florida, the GIS showcase sponsored by the FIU library GIS lab in February 2001, the Florida Model Application Conference held in May 2000 in 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 in April 2001. The responses have been all positive and enthusiastic. Many planners requested for the presentation materials and papers. Many commented that the tool filled a gap and would be useful for them. A number of people also requested for the software. Locally, private demonstrations have been made to Mr. Fabian Cevallos of Broward Transit, Frank Baron 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 are briefly summarized below. Aggregate vacant lands by ownership. It has been suggested to examine the ownership of vacant lands so that land that is divided into small parcels but owned by a single entity may be identified for large development projects. This suggestion has been implemented. Simultaneous consideration of existing land use, zoning, and accessibility to transportation facilities to determine the potential of development projects. An example of this is that if the zoning of a parcel is estate residential and the parcel is near a principal arterial, it may be considered as inappropriately 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 the information necessary for this purpose is available in VOLUTI but the user needs to obtain the facts through several queries. Future effort may include providing more integrated and comprehensive analysis tools. Evaluation of transportation project impact on land use by conducting a before-and-after study of a major transportation improvement project. Such studies not only will be beneficial from the point view of understanding land use and transportation interaction, but also are necessary to improve the accuracy of site impact analysis procedures. In this implementation of VOLUTI, traffic volumes and ratios of volume to capacity before and after a land development project can be generated using the FSUTMS model and displayed. However, the real impact of development projects have not been studied extensively or in detail. Providing additional information on tax base along with land use composition. Since increasing tax base is one of the objectives of many public officials involved in making land development decisions, this information may be helpful in identifying and evaluating alternatives for increasing

28

tax revenue. This information may also be used to promote mixed land use where land use is mostly residential. This information has been added in VOLUTI. Providing information on availability of office floor space, retail floor space, and industrial floor space. Such information is a more accurate measure of market supply. Current VOLUTI implementation 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 sewer infrastructure affects decisions on land development since they represent a significant cost component. The water and sewer line information has been obtained from the Miami-Dade County Water 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. This has been implemented in VOLUTI. The information includes travel times by different modes between TAZs as well as a measure of accessibility of different locations based on a combination of ease of travel and opportunities represented as employment size.

29

6.

DATA COLLECTION AND PROCESSING

The data collection effort has been much more extensive than that in the previous VOLUTI implementation. 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 the collection and processing of these data. Due to the amount of information, a complete description of the data used in VOLUTI 2.0 is given in file named metadata.doc on the VOLUTI distribution CD. 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 other changes in properties such as new sales are added to the database, which results in its containing assessed 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 for approval. Upon approval, the information on assessed values for the last year is removed from the database. Therefore, the final version of the updated database contains only two years of assessment information. Therefore, there is a time window in which the database contains three years of assessment data. Although at the time when the database was acquired, the current year assessment data are pending approval and are subject to modifications, the potential changes will be insignificant according to the Public Access Section of ITD. The property tax appraiser’s database used in VOLUTI 2.0 has been obtained form ITD and contains only two years of assessment data. The information utilized includes the following: Folio number Current land value Current building value Current total value Previous land value Previous building value Previous total value Previous sales price Previous sales year County land use code Lot size

a unique property record identifier of 13 digits. 2000-2001 assessed land value. Note that this value is not the market value, but reflects the market value. 2000-2001 assessed building 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 not have land values. 1999-2000 assessed land value. 1999-2000 assessed building value. 1999-2000 assessed total value. the selling price of the most recent sale of the property. the year in which the property was last sold. a numerical code indicating the land uses. Some examples of possible land uses are single family, duplex, apartments, light industrial, office, commercial, etc. the lot size in square feet. The numbers are expressed as integers with the last two digits being the fractions. A new column is 30

Building square footage

therefore created that expresses the values as the actual square footage using floating point numbers. the building area in square feet.

Data definition for the property tax appraiser’s database does not currently exist. This lack of metadata results in a significant amount of effort in determining the database structure and the meanings of the fields, especially since the original data were in ASCII file format and must be converted 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 not include all the parcels, but most parcels in the Overtown area are in the data set. The GIS parcel data created and owned by Florida Power and Light (FP&L) are both spatially actual and complete in geographic coverage. However, since it is proprietary and unavailable to the city or FDOT, the data must 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 land use code, and a condition code of A (excellent), B (good), C (fair), V (vacant), VSB (vacant with structure in good condition), VSC (vacant with structure in fair condition), VSD (vacant with structure in poor condition), or PL. The rating was assigned by the City of Miami staff based on field 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 zones represented as polygons and land use code, atlas sheet number, zoning district number, and special district number. The zoning codes used by the city are different from those used by the county. There are 17 zoning codes: Code 0 1 2 3 4 11 12 13 25 35 45 55

Zoning Type SP R-1 (single family) R-2 (duplex) R-3 (multifamily low density) R-4 (multifamily high density) C-1 (restricted commercial) C-2 (liberal commercial) CBD (central business district) O (office) G/I (government/institutional) I (industrial) RT (Rapid Transit) 31

81 82 97 98 99 6.4

PR (parks/recreation) CS (conservation) EXP (expressway) RR (rail road) Not defined

Land Use Data

Land use data are obtained from ITD. The data are created from the FP&L’s parcel maps based on the land use code. Contiguous parcels of the same land use codes are aggregated to arrive at polygons that have a single land use. The land use data are dated 1994 and 1998, respectively. The land uses represented include the following: Code 10, 11, 13 12 20 30, 35, 50 61 101, 110, 112, 113, 115

Land Use Type single family townhouses two-family (duplexes) migrant camps mobile home parks shopping centers, commercial, office, stadiums, tracks 200 hotels and motels 310, 320, 339, 340 359, 370 industrial 411, 412, 420, 430, 440, 450, 451, 460, 470 institutional 510, 516 ~ 519, 527, 530, 550, 560 ~ 562 580 parks 570 water conservation area 540 cemeteries 610 ~ 612 airports/ports 640 ~ 642 streets/roads, expressways, ramps 613 ~ 637, 650 ~ 670 communications, utilities, terminals, plants 710 ~ 790 agriculture 801 ~ 805 vacant 911 ~ 936 water Multiple codes for the same type of general land use type indicate subcategories that provide more details on the actual land uses. For instance, single family has two subcategories reflecting different densities, and institutional has ten subcategories such as public and private schools, colleges and universities, cultural centers, hospitals, nursing homes, government and administration, military, prison, etc.

32

6.5

Employment data

In VOLUTI, employment data that include employment type and employment size by business location were utilized. The data were purchased from InfoUSA in 1999. The data are used in creating local accessibility to essential service indices. The original data were in ASCII format and were 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, and employment 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 data on 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 has been 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 useful when determining if water and sewer lines are adequate for a proposed land use. Metadata are not available. 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 the location 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 useful is the functional classifications of roadways. The functional classifications reflect the hierarchical nature of the roadway system, with some collecting traffic from the local area and some serving as major through routes. The functional classes of the roadways in this map include limited access highways, major arterials, minor arterials, and collectors. The other GIS data set is a detailed street map that includes all roadways including local streets. The major road GIS layer serves as a background in VOLUTI to orient the user, and are also used in buffer analysis (see Section 11.4). The detailed street map is not shown but may be displayed. It is also used for geocoding purpose such as locating a place by its address (see Section 11.2).

33

State Road Inventory Data FDOT District 6 completed a GIS pilot project in 1999. The project involved the implementation of 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 FDOT District 6 Planning and Statistic Administration Office: •

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

•

1999 Average Annual Daily Traffic (AADT). AADT is defined as the total number of vehicles traveled on a road during one year divided by the number of days in that year. It is either obtained from permanent traffic counters or estimated based on short period counts such as 24-hour or 48-hour counts. When AADT is estimated, variations in traffic volumes during a day, during a week, and in different seasons are accounted for. AADT is computed for 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 to designate the six levels of service. A generally represents the most favorable conditions, and F the least favorable conditions. Levels of service are determined according to the FDOT Level of Service Handbook, last updated in 1998 and in effective since March 1999. The methods used are based on the 1997 update Highway Capacity Manual and a computer program is used to determine LOS for all state roads. Prior to March 1999, 1995 Level of Service Handbook was used as the basis for determining LOS, which also employs the methodology from the Highway Capacity Manual. The LOS data used for this project is for the year of 1999.

•

Number of lanes. The number of lanes of a roadway is for both directions. It is an important factor 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 the beginning mile post, the ending mile post, and the attribute data. An event theme can then be generated in ArcView, which has the same appearance of the base map but the attribute values can be properly displayed. Transit Services The transit routes, bus stops, and rail stations were obtained from ITD as part of the public access GIS CD. However, the bus route and bus stop data have been outdated. According to the MiamiDade Transit Agency (MDTA), many errors exist in the bus stop database. These databases are still 34

used in the current VOLUTI version, but should be replaced when the updated databases become available. 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 Environmental Evaluation. 6.9

Traffic Analysis Zones

VOLUTI uses two traffic analysis zone (TAZ) structures, one of 1990 and the other of 2000. TAZs are used as the basic geographic units for analyses related to traffic and transportation, as well as in the FSUTMS models. The TAZ structures are used in VOLUTI to display FSUTMS model related demographic, socioeconomic, and land use information. The 1990 and 2000 TAZ structures are different. Therefore to display changes in, e.g., population, will require the establishment of the correspondence between the zones in the two structures. For this project, the correspondence is established 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 may be collected, some of which have been used to construct 360° panoramas using a shareware, which is 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 infrared digital orthophotos from the United State Geological Survey (USGS), and the one-foot resolution black-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 photograph in which displacements caused by camera orientation and terrain have been removed through a rectification process. It is a uniform scale photographic image and can be considered a photographic map. The uniform scale of a digital orthophoto makes it possible to determine map measurements or to overlay information, using the digital orthophoto as a base map. Features are represented in their true ground position, making direct measurement of distance, areas, angles, and positions possible. This makes the digital orthophoto valuable as a layer in a GIS or as a tool for revision of other map materials such as base maps and topographic maps.

35

The one-meter resolution digital orthophotos are the product of the National Digital Orthophoto Program (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 Service are partners in this program. The primary goal of the program is to ensure the public domain availability of digital orthophoto quadrangle (DOQ) data for the Nation. The primary imagery source for digital orthophotos is the NAPP photographs and NAPP-like photography. The NAPP photography is quarter-quadrangle centered (3.75 minutes of latitude by 3.75 minutes of longitude in geographic extent) and taken at an aircraft altitude of approximately 20,000 feet above mean terrain, using a 152-millimeter focal-length camera. The NAPP photography is approximately 1:40,000 scale. The NAPP photographs are used in the production of one-meter DOQs. 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 minutes of 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 continually updated. The NAPP program has had three cycles during which various states were flown over to be photographed. Cycle 1 covered 1987 to 1991. Florida was not included in the NAPP during this cycle, therefore no DOQs were available for Florida for this period of time. Cycle 2 of the program covered the period of 1992 to 1996. Florida was flown over in 1994. The third cycle covers the period of 1997 to 2003. Florida is scheduled for 2000 for a new update. According to USGS, after 2003, the aerial photos will be updated on a 10-year cycle for most areas, and on a 5-year cycle in areas where land use change is more rapid. For more information on the NAPP, readers are referred to the USGS web site at http://edcwww.cr.usgs.gov/dsprod/prod.ht ml. The one-meter DOQs have a ground resolution of one meter or 3.28 feet. It can show great detail of features including buildings, sidewalks, roads, cars, etc. Figure 6.1 illustrates the details contained in the photograph. A DOQ covers one-fourth of a USGS 7.5 minute quadrangle area. Since the size of USGS quadrangles vary with latitude, the size of DOQs also change accordingly. The DOQs obtained for this project cover approximately an area of 6 mile by 6 mile. The DOQs overlap each other near the edge. Figure 6.1 Detail of a Digital Orthophoto Quarter Quadrangle 36

The DOQs are large data sets. The infrared DOQ files used for this project have a size of about 150 megabytes each (quarter quadrangle). To speed up the display, each digital orthophotos quarter quadrangle is divided into 16 smaller patches and converted to the JPEG format, resulting in a reduction of the size of the data sets by 57 times. An image catalog is then constructed to specify the coordinates of each of the small patches. ArcView si able to use the information from the image catalog 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 the collection and distribution of digital cartographic data. The costs include a $45 base charge, with a $15.00 charge for each DOQ. Black-and white DOQs cost $7.50 but they are not available for Florida. 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 were also digital orthophotos. The photographs are taken in 1998 and 1999. Although they are black-andwhite, their high resolution makes them highly desirable. Figures 6.2 and 6.3 illustrate the difference in the resolution in the two types of aerial photographs, respectively.

Figure 6.2 One-Meter Color Infrared Digital Orthophoto from the USGS 6.12

Figure 6.3 One-Foot Digital Orthophoto from Miami-Dade County

Demographic and Socioeconomic Data

The demographic and socioeconomic data come from two sources: the 1990 census data and the 1999 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 block group polygons are used to displayed the census data. The block group polygons are available from the Census Bureau. The data from the FSUTMS ZDATA1 and ZDATA2 files are estimated by the Miami-Dade County Planning Department for the year of 1999 and obtained through the Miami-Dade County MPO, who 37

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 age of 65, single-family population, multi-family population, service employment, commercial employment, industrial employment, and school enrollment. Population and employment densities are 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 changes in these variables between the year of 1990 and 1999. Such longitudinal data are useful for examining the trends in demographic and socioeconomic changes. 6.13

Environmental Data

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

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

J

Cell Entropy = - å

j =1

p j ln( p j ) Ln( J )

Cell mean entropy = - å k

å

Pjk ´ ln( Pjk ) j

ln( J ) K

N

Zonal mean entropy = where Pj J

= =

K Pjk

= =

N

=

å (cell entropy of

zone i )

i =1

N

the proportion of land development of the jth type the number of different types of land development, which include residential, commercial, public, offices and research sites, industrial, and parks. J = 6 in VOLUTI. the number of actively developed cells in a TAZ. the proportion of land use type j within a 0.8-km radius of developed area surrounding the kth hectare. the number of cells in a TAZ.

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

39

7.2

Job/Housing Balance

This is the ratio of total employment by total households in each TAZ. A low ratio indicates a predominantly residential area. A large ratio greater than 1.5 may indicate a predominately nonresidential area. The data used to calculate this index are the 1999 TAZ-based employment and housing 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 further development. In urban areas where high density development has occurred, the parcel sizes tend to be small. Large parcel sizes are an indicator that land use may be intensified by further subdividing the parcels therefore increasing the density. The average parcel size is calculated using the parcel GIS 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 the acceptable level of service standard with regards to recreation and open space as a minimum of 1.3 acres of public park space per 1,000 residents (City of Miami Planning Department 1993). This measure is computed by dividing the acreage of park areas by the population (in thousands) in a TAZ, and reflects the adequacy of recreational space. The population data are the 1999 population estimate for 2000 TAZ structure. The park information is from the 1998 land use data by MiamiDade 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 use categories: Agriculture Airports/Ports Cemeteries Communications, Utilities, Terminals, Plants Industrial Institutional Multi-Family Office Parks (Including Preserves & Conservation) Shopping Centers, Commercial, Stadiums, Tracks Single-Family Streets/Roads, Expressways, Ramps Transient-Residential (Hotels/Motels) Vacant Water 40

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

Figure 7.1 Dialog Box for Displaying Land Use Change

Figure 7.2 Change in Single Family Land Use (1994 1998) 7.6

Changes in Population, Employment, and Dwelling Units by TAZs

Changes in total population, total employment, single family dwelling units, and multi-family dwelling units for each TAZ between 1990 and 1999 may be queried. The data sources are the 1990 and 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 unit change in the Overtown and vicinity area. Although it appears that there has been an increase in single family land use according to the map in Figure 7.3, Figure 7.3 actually indicates a decline in single family dwelling units in the area. Because the display of changes in dwelling units involves the use of both 1990 and 2000 TAZ structures, and the two TAZ structure are different, a Figure 7.3 Change in Single Family Dwelling Units relationship must be established (1990 - 1999) 41

between the two. The 2000 TAZ structure has added about 200 zones bringing the total to 1423 zones. While the majority of the zone boundaries remained the same, the boundaries of many have been modified. Additionally, because of a further refinement of the coordinates to reflect more accurate boundaries, some of the boundary lines are also changed. As a result, establishing the correspondence between the two TAZ structures became more complicated due to the “slivers” from simply overlaying the two TAZ structures, rendering an automated process for determining if a zone has 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 are matched. Matching the TAZ structures over the entire county will require considerable effort, and is 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 a rectangle on the map, result in a pie chart showing the percentages of different land uses in the specified area. Along with the land use composition, another pie chart displays the share of tax contributions from different land uses. Such information is often used by public elected officials in consideration of approval of new development projects with the goal of increasing the tax base.

42

8.

ACCESSIBILITY AND MOBILITY EVALUATION

Several indices have been developed to measure accessibility and mobility. The difference between the concepts of accessibility and mobility is that the former considers travel as a derived need for the purpose of carrying out economic and social activities and the location of activity centers will affect travel 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 within easy reach by, for example, non-motorized modes, then good mobility on highways becomes irrelevant. This concept therefore supports the theory of mixed land uses as a means to reduce travel demand. Mobility, on the other hand, disregards the land use effects on travel demand and simply considers the ability to move vehicles as a measure of accessibility. In the current version of VOLUTI, two sets of measurements have been developed. The accessibility measures include regional accessibility by highway and transit travel modes, respectively, and local accessibility to essential services. The mobility measures include highway travel time contours, transit travel time contours by transit modes, transit-highway travel time difference, transit travel time contours based on all modes, and transit transfers. These measures are described in detail in the subsequent subsections. 8.1

Regional Accessibility by Highway

Regional accessibility by highway mode measures the accessibility to opportunities in a region assuming driving as the travel mode. The opportunities may be employment or population (labor force). Accessibility has been recognized as one of the most important factors that affect both land use and travel behavior. In VOLUTI, the regional accessibility to employment and population by automobile is similarly defined as relative indices expressed as a number between 0 and 100: æ 100 ö N RAEH i = ç ÷ å Emp j exp( -0.0954 ´ tij ) è RAEH max ø j = 1 æ 100 ö N RAPH i = ç ÷ å Pop j exp( -0.0954 ´ tij ) è RAPH max ø j = 1

where RAEHi = RAPHi = Empj = Popj = tij = N =

index of regional accessibility to employment by highway travel for zone i index of regional accessibility to population by highway travel for zone i total employment in zone j total population in zone j shortest congested highway travel time between zone i and zone j the number of zones

The travel times are obtained from the 1990 Miami-Dade County FSUTMS model, which includes both highway and transit modes. The travel times are not actual travel times that occurred on the 43

roadways. However, the indices are a relative measurements and the use of accurate actual travel times is not critical. The coefficients in the formulae are obtained by fitting the highway travel times from the 1999 South East Florida Travel Characteristics Study data set based on exponential functions. The employment data are from the FSUTMS ZDATA2. 8.2

Regional Accessibility by Transit

The indices of regional accessibility to employment and population, respectively, by transit are similarly defined as for the highway travel mode. The formulae are: æ 100 ö N RAETi = ç ÷ å Emp j exp( -0.0166 ´ tij ) è RAEH max ø j = 1

æ 100 ö N RAPTi = ç ÷ å Pop j exp( -0.0166 ´ tij ) è RAPH max ø j = 1

where RAETi RAPTi Empj Popj tij N

= = = = = =

index of regional accessibility to employment by transit mode for zone i index of regional accessibility to population by transit mode for zone i total employment in zone j total population in zone j shortest congested transit travel time between zone i and zone j the number of zones

The coefficient in the exponential function is determined by fitting an exponential curve to transit trip length frequency distribution obtained from the household survey of the 1999 Southeast Florida Travel 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., SevenEleven), bakeries, and drug stores. Availability of such essential services is both an indication of local land use mix and of potential demand on transportation facilities as none or little service availability means that people will have to travel far to meet their needs instead of possibly walking or bicycling to these destinations.

44

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

[local accessibility]

zone i

é total " essential service" employment ù =ê ú population ë û zone i

This measure does not consider the size of a zone (which affects the travel need) or the ease of travel within 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 that opportunities 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 the 1990 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 land development). However, because the current VOLUTI implementation for site impact analysis does not consider transit modes in the FSUTMS model, the model output of the highway travel times will be rather different than those from a model that considers transit modes. Therefore, comparison of travel time change between the base year conditions and after a DRI analysis is done will not be accurate. 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 limitation also 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 transit model. The travel time displayed is for individual modes in the 1990 Miami-Dade County FSUTMS model, which include metrorail and metromover, express buses, local buses, jitneys, HOV, and demand responsive services. The user may choose to use the transit travel time with or without penalties. Penalties are applied to reflect the cost of slower travel speed of transit, walk time, access time, wait time, and transfers. Because site impact analysis in VOLUTI currently does not support the 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 zones considering all transit modes. The shortest path is found for an origin zone and a destination zone by comparing link travel times of all transit modes. The user may choose to include penalties in the calculation, 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. 45

8.7

Transit Transfers Required

The number of transfers required for traveling by transit is an important measure of transit service quality. Transfers have negative impact on service quality as well as on ridership because of the inconvenience and delay involved. According to the transit onboard survey, which was a part of the 1999 Southeast Florida Travel Characteristics Study, more than half of the surveyed users had one or more transfers. Information on transfers is useful 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 transit services may be identified and possible improvements can be investigated. The number of transfers is obtained by finding the shortest path between a zone pair considering the penalty 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 travel time may be compared in a number of ways. Figure 8.1 shows the dialog box that allows a user to choose how a comparison can be performed. Figure 8.2 illustrates a map showing the difference between transit and highway travel times by buses during peak hours with penalties applied to out-ofvehicle time and transfers. Apparently, transit travel time is generally much longer than highway travel time. The differences in transit and highway travel times range between 0 to 327 minutes. Large differences are usually the result of longer distance between two zones, long headway of buses, and transfers required.

Figure 8.1 Dialog Box for Comparison of Transit and Highway Travel Time 46

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

47

9.

DEVELOPMENT OF LAND USE SCENARIOS

In this section, the creation of land development scenarios is described. The purpose of developing these scenarios is to test VOLUTI’s capability of evaluating the impact of land use changes on the transportation system. 9.1

Development Models

Over the years, planners have used four general models for development in Overtown. Model 1 focused on the creation of public housing and the implementation of urban renewal initiatives as directed by federal agencies. Model 2 encouraged locals to develop their own plans with federal financial support in the form of Community Development Block Grants. Model 3 strived to preserve the cultural heritage of the community by restoring key older historical structures in order to attract tourism and reinvestment. Model 4 featured the construction of megastructure complex that included the Miami Arena, the Metrorail station and middle-income apartment towers to stimulate economic spillover (Gale 1999). Model 1 is obsolete. Model 4 failed to create jobs or encourage development (the Miami Heat games were relocated from the Miami Arena to the new American Airlines Arena on Biscayne Bay in Miami). Models 2 and 3 are still in effect but their efficacy depend largely on the availability of federal funding (Miami-Dade County Empowerment Trust 2000). During the fiscal year 1999-2000 ended June 20th, 2000, the Empowerment Zone (EZ) Trust completed its first phase planning efforts on the Town Park Housing New Markets project in Overtown. The EZ Trust has been awarded $10 million from the Miami-Dade County Housing Agency to support this project. The Trust’s objective is to develop a mixed income, single detached and semi-gated community in Overtown (Miami-Dade County Empowerment Trust 2000). The Overtown Neighborhood Assembly has made a clear commitment to economic development by being the first Assembly to pledge 100 percent of its funds ($200,000) to the Empowerment Trust Micro-Loan Fund (ETML Fund). Applications for funding were released on July 26, 2000. Private investment pledges to the ETML Fund and the number of new jobs to be created by this program has not been determined (Miami-Dade County Empowerment Trust 2000). In July 1999, the Overtown Advisory Board, Eastward Ho! (which encourages redevelopment in interior neighborhoods rather than westward) and other agencies sponsored a design charrette in Overtown. The charrette was a formal week-long brainstorming session where residents worked with designers, town planners, and government officials to design a new Overtown. The Overtown Redevelopment Area Design Charrette Report published in March 2000 proposed the creation of a center for Overtown and the redevelopment of commercial and entertainment districts among other developments. However, no funding was identified to make the citizen’s vision a reality (Treasure Coast Regional Planning 2000). The scenario developments presented here are therefore not based on any specific anticipation of federal funding, but are based on methodologies generally accepted by the planning community. 48

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 series for 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-Dade County Department of Planning and Zoning. The estimates for the year 1999 were derived primarily from 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 projections were 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 residential capacity of each area were used. Capacity estimates included some capacity outside the urban development boundary and occasional adjustments were made for redevelopment activities. Preliminary projections for each projection year were controlled to county totals that were established using a component method (births, deaths, and migration flows). Census tract projections were made by allocating the Minor Statistical Area projections. In the central areas this was done by means of a shift-share technique. In the suburbs logistic curves were used for each tract. The tract level population projections were converted to housing projections and these were compared 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 Overtown area. A careful review of those areas where the initial projections showed significant declines in housing was done and redevelopment plans and prospects for these areas were reviewed. No area showed large losses of housing. The stability of the urban development boundary line coupled with the continuing influx of immigrants from the Caribbean and Central and South America suggests that there will be increased demand for housing in all areas (Kerr 2000a). The Miami-Dade County Department of Planning and Zoning estimates low and high population projections to be within five percent from the medium population projection for the years 2005 and 2010 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 living in 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 49

ratio of total persons living in households to the total number of occupied housing units (Myers 1992). Based on the 1990 US Census (Census Bureau 1990) the average household size by census tract is given 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 relationship is commonly used by real estate analysts to predict the demand for housing in local areas (Myers 1992). The projected demand for housing based on a low, medium, and high population series is shown 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

Year

30.01 30.01 30.01 30.01 Net Change

2005 2010 2015 2020

Low (44) 21 36 133 146

Demand for Future Dwelling Units Medium (46) 22 95 148 219

High (48) 23 154 163 292

Table 9.3 Change in Dwelling Unit Demand in Tract 31.00 Census Tract 31.00 31.00 31.00 31.00 Net Change

Year 2005 2010 2015 2020

Low 416 69 60 231 776

Demand for Future Dwelling Units Medium 438 73 176 257 944 50

High 460 76 292 283 1,111

Table 9.4 Change in Dwelling Unit Demand in Tract 34.00 Census Tract

Year

34.00 2005 34.00 2010 34.00 2015 34.00 2020 Net Change

Low (100) 12 24 109 45

Demand for Future Dwelling Units Medium (105) 13 81 121 110

High (110) 13 138 133 174

Table 9.5 Change in Dwelling Unit Demand in Tract 36.01 Demand for Future Dwelling Units Census Tract 36.01 36.01 36.01 36.01 Net Change 9.3

Year 2005 2010 2015 2020

Low 275 67 52 171 565

Medium 289 71 133 190 683

High 304 75 215 209 803

Vacant Land

This section documents vacant land available for new construction and determines the development capacity of this land supply based on current zoning. It identifies where future growth can be accommodated based on census tracts. Overall, current vacant land supply is sufficient to accommodate future demand for housing units based on low, medium and high population series. Current zoning allows up to a maximum of 4,348 additional dwelling units to be built on current vacant land within the study area. The maximum capacity within each census tract is determined by multiplying the acres of vacant land available by the 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 high population 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 land for future dwelling units as a net change from 1999 to 2020 is as follows:

51

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

Zoning

30.01 30.01 30.01 30.01 31.00 34.00 34.00 36.01 36.01

R-11 R-22 R-33 R-44 R-3 R-3 R-4 R-3 R-4 Total

Maximum Units Allowed 11 26 336 855 794 675 1,081 45 525 4,348

Future Demand for Dwelling Units Low Medium High 11 11 11 26 26 26 109 182 255 5 — 150 3176 776 794 794 45 110 174 — 1137 2338 45 45 45 520 525 525 1,532 1,956 2,380

1

R-1 is Single Family Residential designation. Up to 9 dwelling units per acre are allowed according to the Miami Comprehensive Neighborhood Plan 1989-2000 and Zoning Ordinance Number 11000. 2

R-2 is Duplex Residential designation. Up to 18 dwelling units per acre are allowed according to the Miami Comprehensive Neighborhood Plan 1989-2000 and Zoning Ordinance Number 11000. 3

R-3 is Medium Density Multifamily Residential designation. Up to 65 dwelling units per acre are allowed according to the Miami Comprehensive Neighborhood Plan 1989-2000 and Zoning Ordinance Number 11000. 4

R-4 is High Density Multifamily Residential designation. Up to 150 dwelling units per acre are allowed according to the Miami Comprehensive Neighborhood Plan 1989-2000 and Zoning Ordinance Number 11000. 5

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

6

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

7

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

8

This represents the additional future dwelling units required in Census Tract 36.01. 52

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 at least 2,100 dwelling units. A jobs-to-housing ratio for each census tract is determined in proportion to 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 1,532 dwelling units to meet future demand. At the 1,532 dwelling units level the jobs-to-housing ratio is calculated to be 0.73 jobs per dwelling unit. The total number of jobs to be created is calculated by multiplying 0.73 jobs by 1,532 dwelling units. This calculation yields a total of 1,118 jobs to be created according to the low population growth scenario. A projection of future jobs for each census tract based on low, medium and high population series is as follows (Nelessen 1994): Table 9.7 Projection of Jobs by Census Tract Census Tract 30.01 31.00 34.00 36.01 Total

Future Jobs Medium 204 879 102 637 1,822

Low 107 566 33 412 1,118

High 331 1,259 197 910 2,697

Each census tract must have a minimum amount of local commercial facilities. For planning purposes, a ratio of 52 square feet of commercial development per dwelling unit is established for areas with at least 2,100 dwelling units adjusted for the jobs-to-housing ratio (Miami-Dade County Empowerment Trust 2000). A commercial development-to-housing ratio for each census tract is determined in proportion to 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 1,532 dwelling units to meet future demand. At the 1,532 dwelling units level the commercial development-to-housing ratio is calculated to be 28 square feet of commercial development per dwelling unit. The total square feet of commercial development to be created is calculated by multiplying 28 square feet of commercial development by 1,532 dwelling units by the jobs-tohousing ratio of 0.73. This calculation yields a total of 30,930 square feet of commercial development to be created according to the low population growth scenario. A projection of future commercial development is as follows (Miami-Dade County Empowerment Trust 2000):

53

Table 9.8 Projected Commercial Development in Sq-Feet Census Tract

Zoning

Demand for Future Commercial Development (sq. ft.) Low

Medium

High

30.01 30.01 31.00 31.00 34.00 34.00 36.01

C-19 C-210 C-1 C-2 C-1 C-2 C-1

191,299 81,156 35,558 29,532 202,443 148,322 89,474

2,948 — 15,667 --909 — 11,406

9,202 — 35,558 4,109 4,622 — 28,699

22,103 19,00911 35,558 29,532 13,171 — 60,785

36.01

C-2

151,135 928,919

— 30,930

— 82,190

— 180,158

Total 9.5

Vacant Land (sq. ft.)

Recreation and Open Space

The acceptable level of service standard for the City of Miami with regards to recreation and open space 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 is as follows: Table 9.9 Recreation and Open Space by Census Tract Census Tract 30.01 31.00 34.00 36.01 Total

Zoning PR12 PR PR PR

Open Space Available (acres) 0.6 4.0 8.4 0.0 13

Future Demand for Open Space Low

Medium

High

0.57 3.17 0.15 1.7 5.59

0.85 3.84 0.37 2.06 7.12

1.15 4.53 0.59 2.41 8.68

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. 54

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

55

10.

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 land development projects on the transportation system and vice versa. This improvement is made in two ways. First the user can select and define property parcels for development and specify land use intensities, then evaluate the impact of the development in terms of increased traffic volumes, the volume over capacity ratio (V/C) in the transportation network FSUTMS, and the accessibility measures. The second way is to allow the user to modify the transportation system and evaluate the system performance. Since VOLUTI is not an integrated model for transportation and land use planning, the interactions between land use and transportation cannot be fully captured. The interaction is only modeled through accessibility. The figure below illustrates the interaction between VOLUTI and FSUTMS in the current implementation.

Figure 10.1 Interactions between VOLUTI and FSUTMS The impact of land use on transportation is direct as new land developments will result in changes in the transportation network in terms of traffic volume. The converse, however, is not true. In other words, changes in accessibility will not immediately produce results in land use patterns. This has been recognized in literature and is still a subject of research. In this section, the methodology employed 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 is typically referred to DRI analysis, or analysis of Development of Regional Impact. The methodology used 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 are preliminary in nature, and can not be taken as a DRI analysis normally conducted by engineering 56

firms. An actual DRI analysis will require much more detailed information. Information about transportation improvement projects, either having occurred since the last FSUTMS model update, having been committed, or being anticipated, must be collected and the transportation network edited accordingly to reflect the conditions of the transportation system at the expected time of the land use project. Similarly, land use changes must also be accounted for to reflect the land use conditions at the expected time of the land use project. To perform DRI analysis in VOLUTI, land development projects must first be defined. The procedure as well as the graphic user interface will be explained in Chapter 11. Here only the basic concepts of a development scenario will be briefly introduced to facilitate the understanding of the methodology. A development scenario is defined as projects located in a number of new TAZs, each of a single land use such as single-family residential, multi-family residential, shopping center, etc. Creating new TAZs for development projects instead of adding the new land use (in the forms of population and employment) is based on convenience consideration. Since currently only vacant land is used for developments, adding new developments in new TAZs will not affect the existing TAZs in terms of the zonal socioeconomic and demographic characteristics. VOLUTI currently does not have the capability to modify the land use in a zone, which will be discussed later. The reason for limiting the land use within a new TAZ is to avoid complication from the need to deal with “internal capture.” Internal capture means that if a TAZ has mixed use, some of the trips may occur internally thus will not affect the network traffic volume. Internal capture is a complicated issue and requires much detailed information about the specific uses within a zone, therefore is not considered at present. Each new TAZ may contain a number of parcels of the same land use type. The hierarchical structure of a scenario is illustrated in Figure 10.2, in which N is the number of existing TAZs in the FSUTMS model. Any newly added TAZs are sequentially numbered beginning at N+1.

Figure 10.2 Structure of a Land Use Scenario with Three TAZs

57

Two basic methodologies are described in the FDOT 1997 Site Impact Handbook for site impact analysis: manual method and model method. The model method refers to the use of FSUTMS for forecasting future traffic volumes, which has gained wider acceptance in recent years. FSUTMS is a district-approved analysis process, which can assist in determining trip distribution, internal capture, 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 the process in order to allow quick visualization of overall traffic impacts from new developments. The following 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 numbers for development intensity in VOLUTI. ITE’s Trip Generation is the most intensive collection of available trip generation data for different land uses throughout the United States and Canada since the 1960s. The document is recommended in the FDOT Site Impact Handbook for estimating travel demand from new development. Table 10.2 illustrates the lookup table for independent land use variables. In VOLUTI, the most appropriate variable for trip generation is left to the user to choose since no specific guidelines for variable selection are provided in Site Impact Handbook. A database in MS-Access format (ITE.MDB) is created to save the related trip generation information for each land use and variable listed in Tables 10.1 and 10.2. The ITE.MDB file stores the data collected from the PM peak hour of adjacent street traffic (usually between 4:00 and 6:00) for most of the land uses. 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. Peak Hour”, and “Weekday, P.M. Peak Hour of Adjacent Street Traffic” are applied for the land uses of city park, general office building, and walk-in bank, respectively.

58

Table 10.1 Codes, Types and Independent Variable Numbers for New Land Uses Land Use Code 110 210 220 310 320 411 710 820 565 834 852 870 911

Land Use Type General Light Industrial Single-Family Detached Housing Apartment Hotel Motel City Park General Office Building Shipping Center Day Care Center Fast-Food Restaurant with Drive-Through Window Convenience Market (Open 15-16 Hours) Apparel Store Walk-in Bank

Variable Number 4, 5, 6 1, 2, 3, 6 1, 2, 3 4, 7, 8 4, 7, 8 6, 9 4, 5 13 4, 5, 10 5, 11, 12 5 5 4, 5

Table 10.2 Variable Lookup Table for New Lane Uses Variable Number 1 2 3 4 5 6 7 8 9 10 11 12 13

Variable Name Dwelling Units Persons Vehicles Employees 1000 Sq. Feet Gross Floor Area Acres Occupied Rooms Rooms Picnic Sites Students Seats PM Peak Hour Traffic on Adjacent Street 1000 Sq. Feet Gross Leasable Area

The SITEIMPACT.EXE file, a VOLUTI component written in the Visual Basic (VB) computer language, is developed to simultaneously enumerate the traffic impacts from as many as 208 new development sites, each with one or more vacant parcels specified. The program is designed to transport data between the modeling modules of the urban transportation planning process in 59

FSUTMS and the GIS environment in VOLUTI. To simplify the problem, the transit service in the transportation network is ignored, which leaves the highway-only analysis the only travel demand modeling option in VOLUTI. Figure 10.3 illustrates the site impact procedures applied in SITEIMPACT.EXE. The following sections depict the tasks performed in each procedure in more detail. Initialization Background Traffic Trip Generation Create ZDATA3 Selected Zone Analysis

Figure 10.3 Site Impact Analysis Process 10.3

Initialization

Figure 10.4 illustrates the basic tasks performed in the initialization procedure. It starts with retrieving the name of the computer system directory where VOLUTI.INI is saved when it is first installed. Next, the VOLUTI project directory can be identified by retrieving the program parameters from the VOLUTI2.INI file that is stored in the system directory. The SITEIMPACT.exe program then verifies the existence of the FSUTMS software for version 5.3 or higher. This is achieved by searching the system registry for the program location. After the required program directories are obtained, the next task is to read the new land uses that the user has specified in the GIS environment, followed by creating a new PROFILE.MAS file to update the directories for storing the FSUTMS programs and the input data for the study area. If the program detects any abnormal termination of the program execution during initialization, an error message will be generated and the application will be terminated.

60

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 the transportation 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 taken as the background traffic. In this procedure, the input files that are needed for executing the highway-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 already been created and stored by VOLUTI in the “Modified” subdirectory. Thus, they are not duplicated in this procedure. In the original Miami-Dade network data, traffic analysis zones (TAZs) 1 to 1179 and 1180 to 1200 are 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 new TAZ 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 in the modified network are now ranging from 1 to 1379 and 1380 to 1400, respectively. The zone numbers between 1180 and 1379 are thus available for new development sites. In order to implement the new TAZ structure in the site impact analysis process, the following FSUTMS input files are manually modified and stored in the “F_dade90\Original” directory: PROFILE.MAS, LINKS.90A, XY.90A, EETRIPS.90A, ZDATA4.90A, and IEEIEE.A90. 61

In FSUTMS, the PROFILE.MAS parameter file is referenced during program execution to identify parameter 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 network on a link-by-link basis. It is one of the network data files required for the Highway Network Building (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 Y coordinates for each node in the highway network. The following changes are made to the original XY.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. The EXT model estimates trips traveling through the study area, between entry and exit points represented by external TAZs. The changes are made to update the external TAZ numbers in the EETRIPS.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 characteristics such as land use, population, employment, and other economic activity measures. The ZDATA4 file contains internal-external trip productions for each external traffic analysis zone. Internal-external trips 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 table 1), E-I trips (trip table 2), and E-E trips (trip table 3). The trip tables 1 and 2 comprise what the FSUTMS model considers as I-E trips. The following procedures modify the external TAZ numbers in the IEEIEE.A90 file without altering the data: •

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

62

• •

Follow the simple format of the Build Trip Table program in TRANPLAN to create a new trip 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. The IEEIEE.OUT file in Figure 10.5 refers to the updated trip table created according to the simple format of the Build Trip Table program. $BUILD TRIP TABLE $FILES INPUT 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

10.5

Trip Generation

The trip generation step is the most critical procedure in the site impact analysis since it estimates the amount of travel associated with each proposed land use. According to FDOT Site Impact Handbook, 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. Figure 10.6 gives the steps that is applied to replicate trip generation, in which n refers to the development site that is currently in the process of travel demand estimation and m is the total number of new development 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 travel demand in terms of vehicle trips for each new development using ITE’s Trip Generation. The second task is to examine the study area in order to locate the TAZs that may have similar land uses with the new developments. The information, e.g., the percentage of trips by purpose, for TAZs found in the second task is applied in the third task to convert the total number of vehicle trips to production and attraction in person trips by different trip purposes. The process will continue until the travel demand for all of the new land uses is estimated. The following sections describe the procedures performed in each step of trip generation procedure in detail.

63

Start Set n = 1 Set m = Number of New Sites Estimate ITE Vehicle Trips For Site n Examine Land Use Convert Vehicle Trips to Person Trips by Purpose n=n+1

n>m?

No

Yes End

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 regression equation coded in the program. Since the resulted total number of trips from different approaches can vary substantially, the method for selecting average trip generation rates or regression equation described in Site Impact Handbook is applied to find a better estimate of trip generation. Figure 10.7 shows the tasks performed in this procedure. 64

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 method may provide better estimation than the other under certain conditions. If the trip generation equations are not given in ITE’s Trip Generation, the average trip rate is assumed in the SITEIMPACT program to estimate the total site traffic for each land use. Otherwise, the following method 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 the average 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, consider the next criterion.

•

If the standard deviation is less than 110 percent of the average rate or the correlation coefficient (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 estimates new travel demand in vehicle trips, which need to be converted and apportioned to production and attraction person trips among the FSUTMS trip purposes (home-based work, home-based shopping, 65

home-based social/recreation, home-based other, and non home-based). In the procedure of examining land uses, the TAZs with the same land use are identified. As shown in Figure 10.8, the zone-specific productions and attractions for background traffic that are stored in the PRODS.A90 and ATTRS.A90 files in the “F_dade90\Original” directory are first retrieved. The socioeconomic data 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, land use 110 is for general light industrial development. Thus, the average trip purpose percentages for zones with non-zero industrial employment in the ZDATA2 file are applied to apportion trips to different purposes. Table 10.3 gives the lookup table for the socioeconomic variables used to identify zones with the same land use. Start Read PRODS & ATTRS Get Area-wide Trip Purpose Percentages End

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

110 210 220 310 320 565 710 820 834 852 870 911

ZDATA2 ZDATA1 ZDATA1 ZDATA1 ZDATA1 ZDATA2 ZDATA2 ZDATA2 ZDATA2 ZDATA2 ZDATA2 ZDATA2

Industrial Employment Single Family Population Multiple Family Population Hotel/Motel Units Hotel/Motel Units Service Employment Commercial Employment Commercial Employment Service Employment Commercial Employment Commercial Employment Service Employment

66

10.8

Trips Conversion

Figure 10.9 lists the tasks performed in the trip conversion procedure. The SITEIMPACT program first converts the peak hour adjacent street traffic (one hour between 4 and 6 PM) to daily vehicle trips by a factor of 0.15. The daily vehicle trips for new development sites are then converted and apportion 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 first category includes land uses for residential uses such as land use 210 (single-family detached housing) and 220 (apartment). The second category is for hotel and model land uses. The third category includes the city park land use, which is assumed to generate HBSR trips only. The last category includes the other land uses provided in VOLUTI that generate attraction trips only. Start Convert to Daily Trips Convert to P&A by Purpose End

Figure 10.9 Trip Conversion Procedure Residential category. For residential land uses, the production and attraction vehicle trips are estimated based on its development intensity. The number of attraction vehicle trips is first assumed to equal to the intensity multiplied by NHB auto occupancy factor, i.e., AOFAC(5). The number of productions in vehicle trips is then obtained by subtracting the daily vehicle trips with the estimated attraction vehicle trips. The following equations are applied to estimate attractions in person trips by different purposes. A(i, 3) = A(i, 4) = A(i, 5) =

Int(0.5 * intensity(i) + 0.5) Int(0.2 * intensity(i) + 0.5) Int(0.3 * intensity(i) / 2 + 0.5)

where: i A(i, n)

= =

intensity(i) Int

= =

the new development site i that is currently under processing; attractions in person trips for site i for purpose n: 3 for HBSR, 4 for HBO, and 5 for NHB; the intensity associated with development site i; and the function to obtain the integer portion of a number.

67

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 in the examine land use procedure.

ö æ P _ trips(i )* P _ vehicle(n) ÷ ç Tot _ P _ veh P _ vehicle (5) P(i , n) = Int ç + 0.5÷ AOFAC (n) ÷ ç ÷ ç ø è where: P(i, n)

=

P_trips(i) P_vehicle(n) Tot_P_veh AOFAC(n)

= = = =

productions in person trips for site i for purpose n: 1 for HBW, 2 for HBS, 3 for HBSR, and 4 for HBO; estimated productions in vehicle trips for site i; total productions in vehicle trips for purpose n; total productions in vehicle trips from all purposes; and auto occupancy factor for purpose n.

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

æ ö ITE (i )* Tot _ P _ veh P _ trips(i ) = Int ç + 0.5÷ è Tot _ P _ veh + Tot _ A_ veh ø A_ trips( i ) = ITE ( i ) - P _ trips( i ) The A_trips(i) and Tot_A_veh variables are the estimated attractions for site i and the total attractions of the background traffic, both in vehicle trips. The ITE(i) variable is the number of estimated daily ITE vehicle trips. The number of NHB attractions in person trips is then estimated as follows: A_ trips(i )* A_ vehicle(5) ö æ ÷ ç Tot _ A_ veh ç A(i ,5) = Int 0.5* + 0.5÷ ÷ ç AOFAC(5) ÷ ç ø è

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) HBA_Trips(i)

= =

Int(P_trips(i) - P(i, 5) * AOFAC(5) + 0.5) Int(A_trips(i) - A(i, 5) * AOFAC(5) + 0.5)

68

(Eq. 10.1)

The following equations are then applied to estimate the number of productions and attractions in person trips for each home-based purpose where A_vehicle(n) is equal to the total attractions in vehicle trips for purpose n and all the other variables are previously defined. æ HBP _ Trips( i )* P _ vehicle( n ) ö ç ÷ Tot _ P _ veh - P _ vehicle(5) ç P( i, n ) = Int + 0.5÷ AOFAC( n ) ç ÷ ç ÷ è ø æ HBA_ Trips( i )* A_ vehicle( n ) ö ç ÷ Tot _ A_ veh - A_ vehicle(5) A( i, n ) = Int ç + 0.5÷ AOFAC( n ) ç ÷ ç ÷ è ø

(Eq. 10.2)

Social/recreation category. All of the trips are assumed to be HBSR trips for city park development. 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, the number of NHB person trips is first estimated by the following equation:

ITE (i )* A_ vehicle(5) æ ö ç ÷ Tot _ A_ veh A(i ,5) = Int ç 0.5* + 0.5÷ AOFAC(5) ç ÷ ç ÷ è ø

Equation 10.1 is applied again to calculate the value of HBA_Trips by substituting ITE(i) for A_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 the procedure via the dialog box illustrated in Figure 10.10. The user selects the development zone number in the box at the upper left corner of the dialog box to browse the associated trip information for each new land development. The user can also alter the number of trips by purpose to their desired values. Clicking the Default Trips button will display the original trip values calculated by the program. Note that adjustments to the trip generation rates provided by ITE may be necessary to 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 file to replace the trip generation data in ITE's Trip Generation for a specific land use or entering new number of trips by purpose in the dialog box for each new development site.

69

Figure 10.10 Dialog Box for Editing Number of Trips

10.9

Creation of ZDATA3

The tasks in the creation of ZDATA3 file is shown in Figure 10.11. In this procedure, the new development trips are added into the ZDATA3 file. Since the total number of attractions are adjusted to the total number of productions at the end of trip generation in FSUTMS, the attraction trips from the SITEIMPACT program are balanced in advance to take the adjustment effect into account before the ZDATA3 file is updated. Start Get Original Attractions Balance Attractions Print Trips End

Figure 10.11 Procedure for Creating ZDATA3 File 70

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

newA( i )* Total _ A( i ) Total _ P( i ) Total _ P( i )

Total _ A( i ) +

(Eq. 10.3)

where: i newA(i)

= =

Total_A(i) Total_P(i)

= =

trip purpose i, excluding NHB; total attractions for trip purpose i from all of the new development; total unadjusted attraction for trip purpose i; and total productions for trip purpose i.

After the trips are balanced by the factor calculated using Equation 10.3, they are added into the ZDATA3 file. Figure 10.12 illustrates a partial ZDATA3 file created by the SITEIMPACT program for a new single-family detached housing development. Note that the number of NHB productions is zero since FSUTMS will automatically generate NHB productions equal to the ZDATA3 estimate. 3 3 3 3 3 3 3 3 3 3

1173P+ 1173P+ 1173P+ 1173P+ 1173P+ 1173A+ 1173A+ 1173A+ 1173A+ 1173A+

356100 170 100 175 100 313 100 0 100 0100 0 100 104 100 29 100 15 100

New New New New New New New New New New

210 210 210 210 210 210 210 210 210 210

development development development development development development development development development development

Figure 10.12 Sample ZDATA3 File for New Development 10.10 Selected Zone Analysis In this procedure, the highway only analysis in FSUTMS is performed. A single assignment is made that tracks the total trips as one purpose and development trips as a separate purpose. This is achieved 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 the mode split and highway assignment modules in FSUMTS. After trips are assigned to the network with the EQUILIBRIUM HIGHWAY LOAD routine, the development trips in purpose 2 are retrieved and then displayed in the VOLUTI GIS environment to allow the visualization of the traffic impacts of new developments.

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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 low projection scenario are shown in Figures 10.13, and 10.14, respectively. Because the model is outdated, 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 change in 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 changes in volume over capacity (v/c) ratios. The legend indicates the absolute changes in v/c ratios. The numbers on the links are the v/c ratios after the development, and those in parentheses are the v/c ratios before the development.

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

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

72

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

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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 with limited knowledge of ArcView or GIS to use VOLUTI, it is designed as a menu driven program, in which all queries may be made by selecting from the menus. Some customized tools are added to allow the user to interact with a map, such as selecting a TAZ or a network link. In this chapter, the design of the user interface is described.

Figure 11.1 Top-Level Menu in VOLUTI

11.1

Top Level Graphic User Interface

The top level menu in VOLUTI is illustrated in Figure 11.1. The menu names in uppercase letters are customized menus that were not part of the standard ArcView menu. The View menu is original to ArcView, but has been substantially expanded (see Figure 11.2). The functions added to this menu are as follows: (1)

Theme (Layer) Manager. Because all themes in the project view will have their legends displayed in the table of content area (space to the left of the map area), when more than a few themes are displayed, the table of contents becomes too long and examining a legend may require scrolling the table of content. To avoid a crowded table of contents, all themes that are not relevant to the current query are moved to a hidden view, which may be revealed by the user using the Theme Manager (see Figure 11.3). Selecting a theme from the hidden layer and turning it on will result in removing it from the hidden layer and placing it in the project view so it can be viewed.

(2)

Image background control. Image backgrounds (one-foot resolution digital orthophotos or one-meter resolution color infrared digital orthophotos) may be toggled on and off.

(3)

Overtown boundary display. This menu selection will display the theme that shows the boundary of Overtown. Figure 11.2 The View Menu 74

(4)

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

Figure 11.3 Theme Manager Dialog Box (5)

Clearing matched address. This clears the graphics representing the geocoded address from the map display.

(6)

Default display area. The user may define a default display area by drawing a rectangle on the map display. When query results are displayed, VOLUTI will automatically zoom to the default 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, this one will clear all the queries results.

Beside the menus, some tool buttons are created. Tool buttons are necessary when input needs to be given interactively by the user on the map by pointing and clicking or by drawing geometric shapes. 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 when VOLUTI is expecting an input from the map and the user decides to perform another task before returning to the current task. In other words, if a query requires interactive input from the user by pointing and clicking in the map display area, then a corresponding tool button associated with this query must be depressed. These tool buttons will be described in the following sections when their corresponding menu choices are discussed.

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11.2

Land Use Menu

The land use menu provides a group of query choices related to land uses. Figure 11.4 shows the Land 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. When a user chooses this menu entry, a map will be displayed indicating all sites that have photos attached. At this point the tool button (shown at right) will be depressed and the user may begin to click on the points on the map. Either static photos or a panorama will be displayed. If a series of static photographs are available, a dialog box will appear and the user can browse through the photographs by clicking the “Previous” and “Next” buttons. (2)

Zoning. This selection will display the zoning map of selected zoning types. The types of zoning are given in Table 11.1. The source of the information is the City of Miami Planning Department. Table 11.1 Zoning Codes Database Code Zoning Code

Figure 11.4 Land Use Menu

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

25

O

35

G/I

45

I

55

RT

Rapid transit

81

PR

Parks/recreation

82

CS

Conservation

97

EXP

Expressway

98

RR

Rail road

99

Central business district Office Government/Institutional Industrial

Not defined 76

(3)

Vacant Land. For this query, the parcel map is used and all parcels designated as vacant by its 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 the vacant land in acres. VOLUTI will first find all vacant parcels and then aggregate those vacant parcels that are adjacent and owned by the same entity. The result is a new map displaying 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 the lot size is less than 0.1 (or 10%), it is considered to be underdeveloped and has the potential for expansions.

(6)

Underdeveloped Land of Given Size. This is similar to the query about vacant land of given size. However, no new theme will be created. All parcels that are deemed underdeveloped and 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 Dwelling Units. These queries display maps showing the number of vacant single-family and multifamily dwelling units by TAZ.

(9)

Dwelling units per acre. This is a measure of density or land use intensity. It is arrived by dividing 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 land use types, see Section 6.4.

(11)

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

(12)

Building Stock. Commercial, industrial, and office building stocks in square feet are indicative of the adequacy of the infrastructure necessary to support land developments for the 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 records of commercial, industrial, and office uses in the area are retrieved and the building square footage of the individual properties is summed up, respectively. Figure 11.6 shows an example of this query. 77

Figure 11.5 Land Use Composition and Tax Base Make–Up

Figure 11.6 Commercial, Office, and Industrial Building Stocks

78

(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 may be noted that the land use mix indices in the study area are in the middle of the spectrum while the job/housing are not well balanced in the Overtown area with either low number of job per capita in some areas or no residential population in other areas.

Figure 11.7 Land Use Mix in Study Area (14)

Figure 11.8 Job/Housing Balance

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

Figure 11.10 Multifamily Land Use Change between 1994 and 1998

Figure 11.9 Land Use Change Dialog Box (15)

ZDATA Change (1990/1999). ZDATA files are the input files of demographic and socioeconomic data for the FSUTMS model. Four types of data may be displayed: total population, single-family dwelling units, multifamily dwelling units, and total employment by TAZ. The user needs to specify a particular type of information using the dialog box shown in Figure 11.11. Figure 11.11 ZDATA Change Dialog Box 79

(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 information by TAZ.

(17)

Sales Price History (One Property). The tool menu shown to the right consists of four tool buttons. They are used for queries on sales price(s) of a property, the assessed values 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 two paragraphs). The appropriate button must be depressed for a particular query in this group. Property sales prices reflect the real estate market condition in an area and are considered in VOLUTI as an indicator of the health of a community. The data used in this query 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 of tool buttons is referred to as a tool menu) is depressed and the parcel map is displayed. The user then selects a parcel and the sales prices for the last three sales, if available, will be displayed as a bar chart as shown in Figure 11.12.

Figure 11.12 Display of Sales Price History of One Property (18)

Assessed Value (One Property). Another measure of real estate market conditions is the assessed values of a property. This query works in a similar manner as the sales price query. The active tool button for this query is the second one in the tool menu. Figure 11.13 illustrates the result of one such query.

(19)

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

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Figure 11.13 Assessed Values of One Property

Figure 11.14 Dialog Box for Choosing the Type of Properties

Figure 11.15 Average Assessed Values in a Region

81

(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 important considerations, especially if smart growth policies are established. An example is the recent debate in the Florida legislature to require that a resident development be approved only if the public schools in the area have additional capacity to accommodate the anticipated increase in student enrollment due to the development. This query will search for all the public facilities in an area of a given radius surrounding a point specified by the user by pointing and clicking on the map. The tool button used by this query is the top one in the tool menu shown to the right. The tool button at the bottom is used by the next query, Public Facilities in a Region. The result is a summary of the number of facilities of each type found within the given radius and new themes, each showing one type of facility. Figure 11.16 illustrates the results of this query.

Figure 11.16 Selected Public Facilities with a 2-Mile Radius (21)

Public Facilities in a Region. This is similar to the previous query but instead of searching in an area around a user specified location, the search is done in a area defined by a polygon drawn 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 this option.

(23)

Water Lines and Sewer Lines. These two menu entries display the water and sewer lines as shown in Figure 11.17. The thickness of the lines indicate the diameter of the pipelines. For different land uses, the required pipeline diameters will vary.

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Figure 11.17 Water and Sewer Lines 11.3

Environment Menu

This menu provides information on environmental concerns such as water bodies, flood zones, pollution, etc. Figure 11.18 shows this menu. The functions provided by this menu are described below. (1)

Shorelines, Lakes and Canals. Three themes are displayed showing the shorelines, lakes, and canals, respectively.

(2)

Flood Zones. The flood zone map is compile based on the Federal Emergency Management Agency flood model. The flood zone map is illustrated fro the Overtown area in Figure 11.19. The high risk areas are near the Atlantic coast and in the Miami River basin.

Figure 11.19 Flood Zone Map

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Figure 11.18 Environment Menu

(3)

Public Well Field Protection Area. When a development is situated in a well field protection area, care must be taken to assure that there will be no adverse impact on the public drinking water from the developments. The legend in Figure 11.19 shows the time it takes for pollutants to travel to the pumping stations in different surrounding areas.

Figure 11.20 Public Well Field Protection Areas (4)

Trash Centers and Land Fills. A map will display the trash centers and land fills. Currently, VOLUTI does not provide the user any information about if a particular development is located too close to a land fill.

(5)

Hazardous Waste Sites. The hazardous waste sites are displayed as points. They are also a consideration in determining suitability of development projects. They may potential impose additional cleanup costs or pose a threat to public health.

11.4

Socioeconomic Menu

The socioeconomic and demographic data that may be displayed in VOLUTI include the TAZ structure and data associated with TAZ, which are typically estimated (or obtained from census) for demand model purposes, and the census block group boundaries with the associated census data. The census block group boundaries and the census data used in the current VOLUTI version are from 1990 since the new census data are not yet available at tract or block group level. The TAZ based data are the 1999 estimates, which are used in the 1999 FSUTMS model

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Figure 11.21 Socioeconomic and Demographic Data Menu

calibration. Figure 11.21 shows the queries that may be performed on socioeconomic and demographic data. Display of these data are briefly described here. 1. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

TAZ based demographic and socioeconomic data. The data that can be displayed include: population density, which is the TAZ population divided by the TAZ area population of age 16 and younger population between the ages of 16 - 65, which represents the labor force population aged over 65, which represents the retired population single-family population multi-family population commercial employment service employment industrial employment total employment employment density school enrollment

2. (1) (2) (3) (4) (5)

The census block group based data include: population population density number of housing units vacant housing units median rent

There are many different types of census data that might be of interest to planners, such as racial makeup of the population, education attainment of the population, etc. If more census data are to be made available, it will necessary to change the menu structure and treat the choices of census data in 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 applications such as transportation project impact analysis, transit service area analysis, and land use study. In VOLUTI, a buffer analysis begins with the user selecting one or more lines representing roadways (see Figure 11.22). VOLUTI will then ask the user to specify a buffer size and a particular type of information such as population or dwelling units by different size to be analyzed (see Figures 11.23 and 11.24). The result of the analysis will be summarized as statistics in a message box (see Figure 11.24) and a map displaying the distribution of the specified variable by TAZ within the buffer as shown in Figure 11.26.

85

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 Analysis Result Summary 86

Figure 11.26 Buffer Analysis Result as a Distribution Map 11.5

Transportation Facilities

This menu (see Figure 11.27) supports queries related to types of transportation facilities available and selected attributes of roadways. The types of transportation facilities include public transit facilities including bus routes and bus stops13, limited access highways, principal arterials, collectors, and railroad tracks. These facilities may be individually displayed or be displayed as a subset defined by the user (with the Show Selected Facilities option). The data are from the ITD major roads data. The roadway attributes include number of lanes, 1996 average annual daily traffic (or AADT) on state roads, traffic volume from the 1990 FSUTMS model, and 1996 level of service (LOS) on state roads.

Figure 11.27 Transportation Facility Menu

In addition to a map that shows the LOS of state roads, a select panel will appear to allow the user to choose a particular level of service (see Figure 11.28) to see a video clip of an expressway operating at that LOS. Figure 11.28 Selecting a Video Clip Demonstrating LOS

13

The data are from 1992. Metro-Dade Transit Agency has a plan to update the information in the future. 87

11.6

Accessibility

Accessibility measures have been discussed in Chapter 6. The menu is shown in Figure 11.29. The original regional highway accessibility refers to the accessibility computed using the 1990 FSUTMS model. The regional accessibility with new development will update the accessibility indices by including the traffic impact from the new development. The update will be for the scenario for which the FSUTMS model is run most recently. The regional accessibility by transit cannot be updated for scenarios currently because the problems associated with performing select zone analysis with transit mode. A more detailed discussion may be found in Chapter 8.

Figure 11.29 Accessibility Menu

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

Figure 11.30 Congested Highway Travel Time Contours in Minutes

88

Figure 11.31 Transit Travel Time Contours (All Modes with Penalties)

Figure 11.32 Difference between Transit and Highway Travel Time

89

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

Figure 11.34 Regional Accessibility to Employment Opportunities by Car

90

Figure 11.35 Regional Accessibility to Employment Opportunities by Transit

Figure 11.36 Local Accessibility Index for Miami-Dade County

91

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 time differences range from less than 5 minutes to two hours. There appear that in some corridors, the travel time difference is smaller than in other corridors, meaning that transit service is more competitive 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 information will be useful to study job access issues for the Overtown residents, many of whom have no other transportation means other than transit. Regional accessibility to employment opportunities by car and by transit is shown in Figures 11.34 and 11.35, respectively. While the regional accessibility by car resembles concentric circles, a result of 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’s residents advantages, since they do not account for the match or mismatch of the residents’ skills and the types of jobs. Nonetheless, these measures will be useful in evaluating job opportunities for different 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-Dade County and in Overtown is shown in Figures 11.36 and 11.37, respectively. It appears that the Overtown 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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11.7

Site Development

For this implementation of VOLUTI, the user is able to select vacant parcels for new development projects. The Site Development menu offers the user three choices: creating a new development scenario, editing an existing scenario, or deleting an existing scenario (see Figure 11.38). These three choices are described below. 1.

Figure 11.38 Site Creating New Scenarios. When the user selects from the menu Impact Menu “New Scenario,” the parcel map will be displayed and vacant parcels in the default display area are highlighted. A new theme saving the land development project information is created and named as Scenario . This is the scenario sequence number, and is the last scenario number increased by one. The scenario name and sequence number is shown in the upper left 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 the parcels, define the zonal centroid and the necessary network connectors for the TAZ using the same dialog box. As has been discussed in Chapter 10, a land development scenario may consist of one or more TAZs. Each TAZ, in turn, may include one or more parcels. Land use type and intensity are specified for the entire TAZ, which means that all parcels added to the TAZ will have the same land use type, or TAZs are of single land use. The basic steps for creating a new land development scenario is as follows: (a) (b)

(c)

(d)

(e)

Select a new TAZ number from “Zone Number” list box to create a new TAZ. 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 into the newly created TAZ. The total square footage from all the parcels in the TAZ is displayed in the Total Parcel Lot Size box. As an example, Figure 11.39 shows parcel 1639 has been added to TAZ 1172. Select an anticipated zoning type for the selected parcel. Because zoning code may be changed, VOLUTI does not restrict the user from speculating what zoning might be in the future for a particular parcel. The selection should not, however, be arbitrary, and should follow the guidelines of sustainable development or smart growth. In the example shown in Figure 11.39, Ru_th or townhouse district is chosen as the anticipated zoning type. Once the zoning type is chosen, a list of permissible land uses will appear in the Choose Land Use Type list box, choose one from the list. In Figure 11.39, the two possible land use types, single-family detached and apartment are the possible choices and apartment is chosen. Once a land use type is chosen for the parcel, a list of land use intensity variables is displayed in the Select Land Use Independent Variable list box. Choose one from the list. Again, as shown in Figure 11.39, the intensity variables for apartment use include number of dwelling units, number of residents, and number of vehicles. The number of dwelling units is selected as the variable to use.

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Figure 11.39 Land Use Scenario Input Dialog Box (f) (g)

(h)

(i)

Enter an integer in the Intensity box. In the example, 100 is entered. 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 choose the User option in the Define Centroid by area at the upper right corner of the dialog box, then simply points and clicks on the scenario map within the TAZ to indicate the location of the centroid. Define network connectors. The connectors are simplified representation of access roads to the transportation network in FSUTMS. All trips originating from or entering the TAZ will reach the network via these connectors. To define a connector, the user first clicks the Define Connectors button located at the right side of the dialog box. Then the user simply clicks on a nearby network link as shown in Figure 11.40. Note that a connector editing tool box will be displayed while connectors are being edited. The first button allows a connector to be added. The second allows a connector to be deleted. 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 of FSUTMS 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 centroid connectors. Information on a particular parcel that have been defined as part of a TAZ may be displayed by selecting the parcel number from the Parcel ID list box. The selected parcel will flash on the map and a dialog box will display the information related to the parcel.

94

Every time a TAZ number is clicked, a parcel number is clicked, or a parcel is selected from the 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 TAZ information.

Figure 11.40 Adding Zonal Centroid and Connectors 2.

Editing an Existing Scenario. The user may choose to open an existing scenario to edit. With this choice, a dialog box will appear showing all scenarios that exist. Once a scenario is selected, the dialog box shown in Figure 11.39 will appear with the information of the chosen scenario displayed. The user may then proceed to make the changes to the scenario. Figure 11.41 Selecting a Scenario to Edit

3.

Deleting an existing scenario involves the user specifying one scenario to delete (see Figure 11.42) and will cause VOLUTI to remove not only the themes related to the selected scenario (the scenario theme and the connector theme), but also all the related FSUMTS input files. Figure 11.42 Deleting a Scenario 95

11.8

Travel Demand

From the Traffic Impact menu (see Figure 11.43), the user may choose to view the impact of a land development scenario on the transportation network. The impact may be shown as the traffic volume on network links and percent of increase from when there is no new development, or the V/C ratio and changes in V/C ratio caused by the development. The volumes and V/C ratios may be displayed for each link for one direction or both directions. Examples of the output for Figure 11.43 Traffic Impact Menu traffic volume and V/C ratios are given in Figures 11.44 and 11.45, respectively. The scenarios used are the residential projects defined as part of the low growth scenario described in Chapter 9. Figure 11.44 shows the additional traffic volume due to the developments on network in both directions (total volume 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 with changes smaller than one percent are not labeled. Figure 11.45 shows the V/C ratio increase on the network. The legend indicates the change in V/C ratio, while the labels on the network links are the V/C ratios after development projects are implemented.

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

96

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

97

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 sense of the number of opportunities and transportation system conditions. A DRI analysis tool has been implemented to perform quick and preliminary assessment of impacts of land development projects on 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 some requesting for the completed software. To further enhance the tool and make it easily adapted for other localities, the following issues need attention and in some cases improvements are recommended. 1.

GIS Data Maintenance and Availability. GIS applications are data intensive. Not only a significant amount of data must be available initially, they need to be updated continually if VOLUTI is to be useful a few years after its initial installation. There are several problems that 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, and City of Miami. FDOT may be considered as a single source of data. The data from MiamiDade County, on the other hand, came from multiple agencies, including ITD, Department of Water and Sewer, Miami-Dade Transit Agency, Metropolitan Planning Organization, and Department 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, or documentation on the data in many cases. This happens more often when the data are maintained by county individual departments other than ITD. The solution to this problem is to establish an enterprise GIS database within the county and municipalities, respectively, and close coordination between the county and the local governments to make arrangements on data collection, maintenance, and sharing. This will be a long process, and will require some changes in the business processes. The advancement in information technology in recent years is moving the businesses in that direction with more data sharing. For instance, more data are becoming available on the Internet. However, a true enterprise GIS database will take a long term effort and a great deal 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 once it is calibrated. The 2025 model should also be added. The transit mode needs to be included to evaluate at system level the development impact on transit ridership and to investigate land use alternatives and transportation programs that promotes public transit and reduce single-occupancy car use. The current VOLUTI implementation does not include all the possible land uses, which should be added.

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Another issue to be investigated is the interpolation between the base year and future year models. Land use projects are typically planned with a time frame of several years to over ten years, which are unlikely to occur in the model base year or the future year. It is necessary, therefore, to reflect the conditions at the project implementation time. Such conditions include demographic (e.g. population, household size, dwelling units, etc.), socioeconomic (mainly employment information), and transportation system (roadway changes, additional transit services, tolls, parking fees, etc.). Some of the information is not readily available in digital format at present, and some does not exist. For instance, an accurate estimation of population for any year between the model base year and future year is not likely available. To perform such an estimate may involve much work. Employment estimation by zone is another challenge, regardless of the year for which it is needed. The 1999 Miami-Dade County FSUTMS model has also adopted a lifestyle trip generation model, which consider such variables as presence of children in households and number of workers in households as the basis of determining the number of trips produce by households for different trip purposes. Methods for estimating these variables are being developed by the county Planning Department. The transportation network update involves reflecting all the changes in the roadways, transit services, toll, parking costs, fuel costs, etc., in the model. Some data may not be easily forecast, such as fuel costs. Information on transportation improvement projects that have been carried out or expected to be completed around the time of the development projects to be modeled may be continually collected and a database constructed, which may be used in model network update. The database should be spatiotemporal in nature, i.e., both project location information and specifics about the projects need to be coded. Programs may be developed to automatically take information from the database and the model network may be updated for any given time. 3.

Evaluation of Scenarios. Procedures and tools should be developed to allow different scenarios 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 as ULAM. This link will allow a better understanding of the impact of transportation on growth, that is how transportation improvements will affect growth in population and jobs in different areas.

5.

Accessibility Measures. Current accessibility measures may be improved based on more empirical research on their link to the travel behaviors. Additionally, population and employment resulted from new developments should be added to existing TAZs before accessibility measures are update to reflect the improved accessibility due to new developments.

6.

Decision Support. Current VOLUTI implementation has limited capability of decision support. A better capability may be arrived at by supporting more sophisticated queries and providing more analysis functions. Examples of queries and analyses that support decision making may be to evaluate potentials of land for development, identify land development opportunities for a given goal or objective, determining adverse factors that may make a 99

development project questionable or increase the costs significantly, and perform transportation equity analysis. 7.

Visualization. While virtual reality remains to be an expensive technology and is unlikely to be practical on a large regional scale, the visualization may be further enhanced. One potential type of data that can be used for visualization is the video logs that FDOT routinely collects on all the state roads. Presently, the LOS measures and display of operating conditions are only available for state roads. The possibility of adding the capability of showing 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 require more detailed information that is not currently available in VOLUTI. A simplified algorithm that gives a preliminary evaluation of LOS may be developed. Additionally, it is possible to develop a methodology to categorize the local highway operating conditions based on typical roadway configurations, intersection configurations, signalizations, and traffic volumes to display video clips for different operating conditions. This will make it much easier for elected officials and the public to understand how the transportation system is functioning or what impact development projects will have on the roadways. For developments at a scale smaller than regional ones, three-dimensional models of buildings and roadways may be useful for visualizing the aesthetic effects of highway or development projects. This may also be achieved with two-dimensional graphics. For instance, 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-oriented ArcView program, released in May 2001 by the Environment System Research Institute (ESRI). Although for the foreseeable future, Arcview 3.X versions will continue to be supported by its vendor due to the large number of existing ArcView applications, ArcView 8 will certainly gradually replace ArcView 3.X versions in the future. In ArcView 8.0, the programming environment is Visual Basic, which provides more functionality and permits better flexibility in integrating ArcView application with other window based software components and programs. Other features that may be added to VOLUTI include map production functions and on-line help documents. To make VOLUTI portable to different localities that use different databases, a mechanism to 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 different databases and their structures, and determine what functions should be available or how the functions 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 free distribution of the software and workshops held in various parts of the state.

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Although the interactions between land use and transportation are complicated and certainly not a simple cause-effect relationship, planners and policy makers have realized that transportation projects do have impacts on land uses and vice versa. Figure 12.1 illustrates all the forces, some political, 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 conditions to achieve sustainable or smart growth and invest wisely in transportation to facilitate this goal.

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

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