Creating a Pedestrian Level-Of-Service Index for Transit Stops
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Creating a Pedestrian Level-of-Service Index for Transit Stops: Evidence from Denver’s Light Rail System Patrick James Gallagher B.A., State University of New York, College at Geneseo, 2010 A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of the Arts At the University of Connecticut 2012 i APPROVAL PAGE Master of Arts Thesis Creating a Pedestrian Level-of-Service Index for Transit Stops: Evidence from Denver’s Light Rail System Present by Patrick J. Gallagher, B.A. Major Advisor _________________________________________________________________ Carol Atkinson-Palombo, Ph.D. Associate Advisor ______________________________________________________________ Robert Cromley, Ph.D. Associate Advisor ______________________________________________________________ Norman Garrick, Ph.D. University of Connecticut 2012 ii ACKNOWLEDGEMENTS Foremost I would like to thank my advisor Carol Atkinson-Palombo who spent countless hours giving me feedback and guidance over the last year. I would also like to thank my associate advisors Robert Cromley and Norman Garrick for their insightful comments and suggestions. Finally, I would like to thank my family and friends for their support over the past year. Fieldwork for this research was funded by the US Department of Transportation through the University of Connecticut Center for Transportation and Livable Systems. iii TABLE OF CONTENTS 1 INTRODUCTION………………………………………………………………………….1 1.1 Background and Research Questions………………………………………………...1 1.2 Structure of the Thesis………………………………………………………………..3 2 LITERATURE REVIEW…………………………………………………………………4 2.1 Introduction…………………………………………………………………………...4 2.2 Accessibility and Mobility……………………………………………………………5 2.3 Walking Distance …………………………………………………………………….7 2.4 Measuring Pedestrian Accessibility…………………………………………………..8 2.5 Social Paths and the Informal Pedestrian Environment…………….……………….15 2.6 Conclusion…………………………………………………………………………...17 3 CONCEPTUAL FRAMEWORK………………………………………………………..18 3.1 Introduction………………………………………………………………………….18 3.2 Sustainable Transportation…………………………………………………………..19 3.3 Transportation – Land Use Relationship…………………………………………….20 3.4 Conclusion…………………………………………………………………………...27 4 STUDY AREA……………………………………………………………………………29 4.1 Introduction………………………………………………………………………….29 4.2 Streetcars, Busses and Auto-Dependence…………………………………………...29 4.3 RTD Light Rail………………………………………………………………………31 iv 5 DATA AND METHODOLOGY………………………………………………………..34 5.1 Introduction…………………………………………………………………………34 5.2 Data…………………………………………………………………………………34 5.3 Methodology Part I (Social Paths and Transit service-areas)………………………39 5.4 Methodology Part II (The Pedestrian Level of Service Index)……………………..46 5.5 Conclusion…………………………………………………………………………..49 6 DISCUSSION……………………………………………………………………………..51 6.1 Introduction………………………………………………………………………….51 6.2 Social Paths and Transit service-area Analysis……………………………………..51 6.3 Pedestrian Level of Service Index…………………………………………………..56 6.4 Applications in Pedestrian Planning………………………………………………...70 6.5 Conclusion…………………………………………………………………………...73 7 CONCLUSION…………………………………………………………………………..75 7.1 Findings……………………………………………………………………………..75 7.2 Critique and Future Research……………………………………………………….77 WORKS CITED……………………………………………………………………….80 APPENDIX A: FIGURES NOT INCLUDED IN TEXT……………………………90 APPENDIX B: TABLES NOT INCLUDED IN TEXT……………………………..99 APPENDIX C: PYTHON SCRIPT………………………………………………….108 v LIST OF TABLES Table 5:1 Pedestrian Level-of-Service Index Variables……………………………………47 Table 5:2 Final Scores for the Pedestrian Level-of-Service Index………………………...48 Table 6:1 Social Paths and Pedestrian Catchment (PC) Ratio……………………………..53 Table 6:2 Social Paths and Route Directness Index (RDI)………………………………...53 Table 6:3 Clusters for Parking Spaces……………………………………………………..56 Table 6:4 Clusters for Transit Connectivity………………………………………………..57 Table 6:5 Clusters for RDI………………………………………………………………....58 Table 6:6 Clusters for PC Ratio…………………………………………………………….58 Table 6:7 Clusters for Retail Density……………………………………………………….59 Table 6:8 Clusters for Employment Density………………………………………………..60 Table 6:9 Clusters for Population Density………………………………………………….60 Table 6:10 Clusters for Transit-Conducive Land Uses……………………………………...61 Table 6:11 Clusters for Land Use Diversity…………………………………………………62 Table 6:12 Hierarchical Cluster Analysis Results…………………………………………...63 Table 6:13 Averages for Each Accessibility Grade Class…………………………………...63 Table 6:14 Final Pedestrian Level-of-Service Scores………………………………………..68 Table 6:15 Stapleton Station Scoring………………………………………………………...72 Table B:1 Station Parking Spaces and Scoring……………………………………………..99 Table B:2 Station Transit Connectivity and Scoring………………………………………100 Table B:3 Station Average RDI and Scoring……………………………………………...101 Table B:4 Station PC Ratio and Scoring…………………………………………………..102 Table B:5 Station Retail Density and Scoring……………………………………………..103 Table B:6 Station Employment Density and Scoring……………………………………...104 vi Table B:7 Station Population Density and Scoring………………………………………..105 Table B:8 Station Walking-Conducive Land Use and Scoring……………………………106 Table B:9 Station Land Use Diversity and Scoring………………………………………..107 vii LIST OF EQUATIONS Equation 5:1 Land Use Entropy Equation………………………………………………….39 Equation 5:2 Pedestrian Catchment Ratio………………………………………………….43 Equation 5: 3 Route Directness Index………………………………………………………44 viii LIST OF FIGURES Figure 2:1 A Social Path Viewed from the Air……………………………………………...15 Figure 3:1 The Transportation-Land Use Relationship……………………………………...21 Figure 3:2 The Transportation-Land Use Relationship for Pedestrian Accessibility………..22 Figure 4:1 Denver Metropolitan Population, 1970 – 2010…………………………………..30 Figure 4:2 Current RTD Light Rail System………………………………………………....32 Figure 4:3 Denver FasTracks Plan…………………………………………………………..33 Figure 5:1 Street Network and Social Paths, Arapahoe at Village Center Station………….36 Figure 5:2 Evolution of Transit Service-Area Methodologies………………………………41 Figure 5:3 Transit Service-Area Methodology in ArcGIS…………………………………..42 Figure 5:4 Route Directness Index for Downtown Stations………………………..............44 Figure 5:5 RDI Methodology in GIS………………………………………………………...45 Figure 6:1 Littleton-Mineral Using Street Network and Pedestrian Network Methods…….54 Figure 6:2 Final Pedestrian Level-of-Service Index Map…………………………………...69 Figure 6:3 Stapleton Transit Service-Area and RDI………………………………………...72 Figure A:1 Station Parking Scoring………………………………………………………….90 Figure A:2 Transit Connectivity Scoring……………………………………………………91 Figure A:3 Average RDI Scoring……………………………………………………………92 Figure A:4 PC Ratio Scoring………………………………………………………………..93 Figure A:5 Retail Density Scoring…………………………………………………………..94 Figure A:6 Employment Density Scoring…………………………………………………..95 Figure A:7 Population Density Scoring……………………………………………………..96 Figure A:8 Walking-Conducive Land Uses Scoring………………………………………..97 Figure A:9 Land Use Diversity Scoring…………………………………………………….98 ix CHAPTER 1: INTRODUCTION 1.1 Background and Research Questions Since the 1990s there have been increased efforts to promote public transportation in American cities. Growing awareness of the environmental and economic risks associated with the structural dependence on fossil fuels has generated discussion about the ways to reduce fossil fuel consumption. Fossil fuel consumption can be reduced in many ways by implementing either technological solutions (such as improving the fuel efficiency of vehicles) or behavior-changing solutions (such as incentivizing people to reduce vehicle miles traveled or VMT). Policy alternatives that fall into this latter category include providing public transportation, and co- locating housing, employment, and amenities in mixed-use developments to reduce the need to drive between highly-segregated land uses (TCRP, 1997; Ewing et al. 2008). Currently, 40 percent of urban trips are less than 2 miles. Of these trips, 90 percent are taken by car (USDOT, 2011). In the last two decades, over a dozen American cities including Denver, Phoenix, Dallas, Salt Lake City and Charlotte have installed commuter light rail systems in an attempt to reduce auto-dependence. In that same time period the number of annual light rail trips has more than doubled from 175 million to 457 million (APTA, 2011). Consensus is emerging that simply overlaying public transit onto the existing urban fabric does little to encourage transit ridership, and much depends on the quality of the pedestrian environment. Transportation and land use policy have served as catalysts for improving our pedestrian environments. Several planning paradigms such as smart growth, new urbanism and transit-oriented development have promoted land use policies that are conducive to walking and transit use. Similarly, since the passage of ISTEA in 1991, the federal government has increased the amount of funding for transit and 1 pedestrian projects. The resurgence of public transit infrastructure projects requires new methods of measuring pedestrian accessibility to transit. This thesis will create a comprehensive pedestrian level-of-service index for Denver’s RTD Light Rail system that seeks to bridge the gap between spatial and amenity driven approaches for measuring accessibility. Scholars have offered several definitions for accessibility. However, two definitions that inform this work are the ease of getting from one location to another using a transportation network (Dalvi and Martin, 1976) and the potential for interaction (Hanson, 1959; Handy, 2002). Traditionally,