The Influence of Service Planning
Decisions on Rail Transit Success
or Failure
MTI Report 08- 04
MTI The Influence of Service Planning Decisions on Rail Transit Success or Failure MTI Report 08- 04 June 2009
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Mineta Transportation Institute
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Created by Congress in 1991
MTI REPORT 08- 04
THE INFLUENCE OF SERVICE PLANNING
DECISION ON RAIL TRANSIT SUCCESS OR
FAILURE
June 2009
Jeffrey R. Brown, Ph. D.
Gregory L. Thompson, Ph. D.
TECHNICAL REPORT DOCUMENTATION PAGE
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Mineta Transportation Institute
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June 2009
Jeffrey Brown Ph. D. and Gregory L. Thompson, Ph. D. MTI Report 08- 04
Some United States metropolitan areas with rail transit systems enjoy ridership and productivity success while
others do not. This study examines the experiences of 11 U. S. metropolitan areas with between onr million and
five million persons to better understand why some areas are successful and others are not. A particular focus is the
role of service planning decisions in facilitating transit success. We find that successful transit systems are those
that: 1) articulate a clear, multidestination vision for regional transit; 2) rely on rail transit as the system’s
backbone; 3) recognize the importance of the non- CBD travel market; 4) encourage the use of transfers to reach a
wider array of destinations; 5) recognize that rail transit alone is not enough to guarantee success; and 6) recognize
the importance of serving regional destinations.
Dual mode transportation
systems; Transportation
operations; Urban
transportation
514
FHWA- CA- MTI- 09- 2608
The Influence of Service Planning Decisions on Rail Transit Success
or Failure
by Mineta Transportation Institute
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http:// transweb. sjsu. edu
Copyright © 2009
Library of Congress Catalog Card Number: 2008931324
ACKNOWLEDGMENTS
We would like to thank the staff and leadership of the Mineta Transportation Institute for
their help during this project, especially Executive Director Rod Diridon and Director of
Research Trixie Johnson. We thank Professor J. M. Pogodzinski for supervising the San José
State University graduate students who contributed to this project. We would like to thank
the following graduate students at Florida State University and San José State University for
their assistance during the various phases of the project: Alex Bell, Jamie Brooks, Caitlin
Cihak, Amy Fauria, Gabrielle Matthews, Kelly McClendon, Anthony Robalik, Aanal Shah,
and Jon Skinner.
We also would like to extend our thanks to our interviewees and our contacts at the transit
agencies and metropolitan planning organizations in each of the study areas for their generous
contribution of data, knowledge, and above all, time, to this research. Any errors or omissions
are the responsibility of the authors.
Additional thanks are offered to MTI staff including Communications Director Donna
Maurillo, Research Support Manager Meg Fitts, Student Webmaster and Technical Assistant
Ruchi Arya, and Student Publications Assistant Sahil Rahimi. Editorial and publication
assistance was provided by Catherine Frazier.
Mineta Transportation Institute
i
TABLE OF CONTENTS
EXECUTIVE SUMMARY 1
INTRODUCTION 5
WHAT WE DO KNOW ABOUT THE FACTORS ASSOCIATED WITH ( RAIL)
TRANSIT SUCCESS OR FAILURE 15
LITERATURE ON TRANSIT RIDERSHIP IN GENERAL 15
LITERATURE ON RAIL TRANSIT RIDERSHIP IN PARTICULAR 25
LESSONS FROM THE LITERATURE 29
RESEARCH METHODOLOGY 31
DEVELOPMENT OF TRANSIT PLANNING AND SYSTEM DEVELOPMENT TIMELINES 32
GUIDEBOOK TO SUCCESSFUL RAIL TRANSIT PERFORMANCE 35
INTRODUCTION 35
TWO VISIONS OF TRANSIT SYSTEM DEVELOPMENT 36
ASSESSMENT OF TRANSIT PERFORMANCE IN 11 METROPOLITAN AREAS 37
TRANSIT SERVICE ORIENTATION IN 11 METROPOLITAN AREAS 41
THE ROLE OF RAIL TRANSIT AS A SYSTEM’S BACKBONE 49
THE IMPORTANCE OF THE NON- CBD TRAVEL MARKET 53
THE IMPORTANCE OF TRANSFERS 63
THE IMPORTANCE OF ACCURATE TRANSFER MEASUREMENT 68
TWO CAUTIONARY TALES: RAIL ALONE IS NOT ENOUGH TO GUARANTEE
SUCCESS 72
THE IMPORTANCE OF SERVING REGIONAL DESTINATIONS 76
APPENDIX A: ATLANTA, GEORGIA 91
APPENDIX B: DALLAS- FT. WORTH, TEXAS 125
APPENDIX C: DENVER, COLORADO 153
APPENDIX D: MIAMI, FLORIDA 185
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APPENDIX E: MINNEAPOLIS– ST. PAUL, MINNESOTA 219
APPENDIX F: PITTSBURGH, PENNSYLVANIA 245
APPENDIX G: PORTLAND, OREGON 271
APPENDIX H: SACRAMENTO, CALIFORNIA 299
APPENDIX I: SALT LAKE CITY, UTAH 325
APPENDIX J: SAN DIEGO, CALIFORNIA 347
APPENDIX K: SAN JOSÉ, CALIFORNIA 375
APPENDIX L: INTERVIEW QUESTIONS 399
ENDNOTES 419
ABBREVIATIONS AND ACRONYMS 435
BIBLIOGRAPHY 437
ANNOTATED BIBLIOGRAPHY 453
ABOUT THE AUTHORS 505
PEER REVIEW 507
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vii
LIST OF FIGURES
1. Riding habit for 11 metropolitan areas 12
2. Service productivity for 11 metropolitan areas ( 1984– 2004) 13
3. Riding Habit for Case Study Areas ( 1984– 2004) 37
4. Service Productivity for Case Study Areas ( 1984– 2004) 38
5. The regional service concept for San Diego 45
6. System maps for successful metropolitan areas 46
7. System maps for metropolitan areas that lack regional transit systems 46
8. Multidestination transit system in Broward County, Florida 47
9. Multidestination transit system in Miami- Dade County, Florida 48
10. System maps for metropolitan areas that have not fully leveraged rail transit 51
11. Passenger activity at San Diego rail stations and bus stops in 2005 54
12. The busway/ BRT backbone alternative: Pittsburgh, Pennsylvania 55
13. Sacramento LRT System: CBD versus non- CBD stations 58
14. Dallas LRT system: CBD versus non- CBD stations 60
15. Denver LRT system versus non- CBD stations 62
16. Evidence of transfer activity at Portland LRT stations in spring 2007 65
17. Evidence of transfer activity at Minneapolis LRT stations in 2006 68
18. Average weekday metro rail boardings in Miami in 2007 73
19. Average weekday light rail boardings in San Jose in 2007 75
20. Regional destinations and transit system in San Diego, California 77
21. Regional destinations and transit system in Portland 78
22. Regional destinations and transit system in Denver 79
23. Regional destinations and and present MARTA transit system in Atlanta 80
24. Regional destinations and present MARTA transit system in Atlanta 81
25. Regional destinations and transit system in Dallas- Fort Worth 82
26. Regional destinations and transit system in Twin Cities, Minnesota 83
27. Regional destinations and transit system in Miami 84
28. Regional destinations and transit system in Salt Lake City, Utah 85
29. Regional destinations and transit system in Sacramento, California 86
30. Regional destinations and hypothetical transit system in San José, California 87
31. Regional destinations and transit systems in Pittsburgh 88
32. Atlanta metropolitan statistical area 91
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33. Atlanta MSA: Population by county ( 1970– 2000) 92
34. Atlanta MSA core counties: population density by census tract ( 2005) 94
35. Atlanta MSA: employment by county ( 1970– 2000) 95
36. Atlanta MSA core counties: employment density by census tract ( 2005) 97
37. Transit systems in the Atlanta metropolitan area ( 2007) 99
38. MARTA transit system ( 2007) 103
39. Atlanta MSA riding habit ( passenger miles per capita) ( 1984– 2004) 112
40. Atlanta MSA load factor ( passenger miles per vehicle mile) ( 1984– 2004) 113
41. MARTA average daily rail station entries ( 2006– 2007) 120
42. Hypothetical regional transit system for Atlanta and its relation to
employment ( 2005) 124
43. Dallas- Fort Worth metropolitan statistical area 125
44. Dallas- Fort Worth MSA: population by county ( 1970– 2000) 126
45. Dallas- Fort Worth MSA: population density by census tract ( 2005) 128
46. Dallas- Fort Worth MSA: employment by county ( 1970– 2000) 129
47. Dallas- Forth Worth MSA: employment density by census tract ( 2005) 131
48. Transit systems in the Dallas- Fort Worth metropolitan area ( 2007) 133
49. DART transit system ( 2007) 136
50. Dallas- Fort Worth MSA riding habit ( passenger miles per capita) ( 1984- 2004) 142
51. Dallas- Forth Worth MSA load factor ( passenger miles per vehicle mile) ( 1984- 2004) 143
52. DART’s Red and Blue Lines, showing stations serving CBD 149
53. Dallas- Fort Worth MSA transit system and its relation to employment ( 2005) 151
54. Denver metropolitan statistical area 153
55. Denver MSA: population by county ( 1970– 2000) 154
56. Denver MSA: population density by transportation analysis zone ( 2005) 157
57. Denver MSA: employment by county ( 1970– 2000) 158
58. Denver MSA: employment density by transportation analysis zone ( 2005) 161
59. Transit system in the Denver metropolitan area ( 2007) 161
60. Denver RTD light rail transit lines 162
61. Denver MSA riding habit ( passenger miles per capita) ( 1984– 2004) 172
62. Denver MSA load factor ( passenger miles per vehicle mile) ( 1984– 2004) 173
63. Denver transit system and its erlation to employmente ( 2005) 183
64. Miami metropolitan statistical area 185
65. Miami MSA: population by county ( 1970– 2000) 186
66. Miami- Dade County: population density by transportation analysis zone ( 2005) 189
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List of Figures ix
67. Miami MSA: employment by county ( 1970– 2000) 190
68. Miami- Dade County: employment density by transportation analysis zone ( 2005) 192
69. Transit system in the Miami metropolitan area ( 2007) 195
70. Transit system in Broward County ( 2007) 196
71. Transit system in Palm Beach County ( 2007) 197
72. Transit system in Miami- Dade County ( 2007) 200
73. Rail transit in the Miami central business district ( 2007) 201
74. Miami MSA riding habit ( passenger miles per capita) ( 1984- 2004) 207
75. Miami MSA load factor ( passenger miles per vehicle mile) ( 1984– 2004) 208
76. Metro rail average weekday boardings by station ( 2007) 213
77. Metro Mover average weekday boardings by station ( 2007) 214
78. Miami MTD transit system and its relation to employment ( 2005) 217
79. Minneapolis- St. Paul metropolitan statistical area 219
80. Minneapolis- St. Paul MSA: population by county ( 1970– 2000) 220
81. Minneapolis- St. Paul core area: population density by transportation analysis
zone ( 2005) 222
82. Minneapolis- St. Paul MSA: employment by county ( 1970- 2000) 223
83. Minneapolis- St. Paul core area: employment density by transportation analysis
zone ( 2005) 225
84. Transit system in the Minneapolis- St. Paul metropolitan area ( 2007) 227
85. Minneapolis- St. Paul MSA riding habits 234
86. Minneapolis- St. Paul MSA load factor ( passenger miles per vehicle mile)
( 1984- 2004) 235
87. Hiawatha LRT average weekday boardings by station ( 2006) 240
88. Twin Cities Transit System and its relation to employment ( 2005) 243
89. Pittsburch metropolitan statistical area 245
90. Pittsburgh MSA: population by county ( 1970- 2000) 246
91. Pittsburgh MSA: population density by transportation analysis zone ( 2005) 248
92. Pittsburgh MSA: employment by county ( 1970– 2000) 249
93. Pittsburgh MSA: employment density by transportation analysis zone ( 2005) 251
94. Transit systems in the Pittsburgh metropolitan area ( 2007) 253
95. PAT Transit System 256
96. PAT Transit Services in central Pittsburgh ( 2007) 257
97. Pittsburgh MSA riding habit ( passenger miles per capita) ( 1984- 2004) 261
98. Pittsburgh MSA load factor ( passenger miles per vehicle mile) ( 1984– 2004) 262
99. PAT Transit System and its relation to employment ( 2005) 268
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100. Portland metropolitan statistical area 271
101. Portland MSA: population by county ( 1970– 2000) 272
102. Portland MSA: population density by transportation analysys zone ( 2005) 274
103. Portland MSA: employment by county ( 1970– 2000) 275
104. Portland MSA: employment density by transportation analysis zone ( 2005) 277
105. Tri- Met Transit System ( 2007) 280
106. Portlant MSA riding habit ( passenger miles per capita) ( 1984- 2004) 286
107. Portland MSA load factor ( passenger miles per vehicle) ( 1984– 2004) 287
108. Tri- Met average weekday light rail boardings by station ( Spring 2007) 292
109. Tri- Met system in Portland and its relation to employment ( 2006) 297
110. Sacramento metropolitan statistical area 299
111. Sacramento MSA: population by county ( 1970– 2000) 300
112. Sacramento MSA: population density by regional analysis district ( 2001) 302
113. Sacramento MSA: employment by county ( 1970– 2000) 303
114. Sacramento MSA: employmente density by regional analysis district ( 1999) 305
115. Transit systems in the Sacramento metropolitan area ( 2007) 307
116. RT Transit System ( 2007) 309
117. Sacramento MSA riding habit ( passenger miles per capita) ( 1984– 2000) 314
118. Sacramento MSA: load factor ( passenger miles per vehicle) ( 1984- 2004) 316
119. Sacramento light rail transit system ( 2007) 320
120. Salt Lake City metropolitan statistical area 325
121. Salt Lake City MSA: population by county ( 1970- 2000) 326
122. Salt Lake MSA: Population Density by transportation analysis zone ( 2006) 328
123. Salt Lake City MSA: employment by county ( 1970– 2000) 329
124. Salt Lake City MSA: employment density by transportation analysis zone ( 2005) 331
125. Transit system in the Salt Lake City metropolitan area ( 2007) 333
126. Salt Lake City MSA riding habit ( passenger miles per capita) ( 1984- 2004) 337
127. Salt Lake City MSA load factor ( passenger miles per vehicle mile) ( 1984– 2004) 338
128. Salt Lake City transit system and its relation to employment ( 2005) 344
129. San Diego metropolitan statistical area 347
130. San Diego MSA: population density by census tract ( 2006) 349
131. San Diego MSA: employment density by census tract ( 2000) 350
132. Transit system in the San Diego metropolitan area ( 2007) 353
133. San Diego MTDB service concept element ( 1979) 358
134. On- freeway bus station from 1979 service concept elementy 359
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List of Figures xi
135. LRT station in Mission Valley ( 2002) 359
136. San Diego MSA riding habit ( passenger miles per capita) ( 1984– 2004) 362
137. San Diego MSA load factor ( passenger miles per vehicle mile) ( 1984– 2004) 363
138. San Diego Green Line LRT 12- month moving average daily boardings 368
139. Boardings at transit stops within the San Diego region, 2005
( before opening of Green Line) 372
140. San Diego LRT 12- month moving average daily boardings 373
141. San Francisco Bay Area counties 375
142. Santa Clara County: population density by transportation analysis zone ( 2005) 378
143. Santa Clara County: employment density by transportation analysis zone ( 2005) 379
144. Santa Clara Valley Transportation Authority ( VTA) transit system ( 2007) 380
145. Santa Clara Valley Transportation Authority ( VTA) light rail system 381
146. San José MSA riding habit ( passenger miles per capita) ( 1984– 2004) 389
147. San José MSA load factor ( passenger miles per vehicle mile) ( 1984– 2004) 390
148. VTA average weekday LRT boardings ( by station) ( 2007) 395
149. Concept map of new bus connections to LRT and Caltrain in San José and
their relation to employment ( 2005) 397
xii List of Figures
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xiii
LIST OF TABLES
1. Classification of 45 study MSAs 7
2. Riding habit ( passenger miles per capita) in 45 MSAs 9
3. Service productivity ( passenger miles per vehicle mile) in 45 MSAs 10
4. Regional riding habit and service productivity by MSA 38
5. Average weekday bus route of primary transit agency by MSA 39
6. Rail service productivity for primary transit agency by MSA 40
7. Service orientation of primary transit agency by MSA 41
8. Bus and rail service shares for primary transit agency by MSA 52
9. Bus and rail rider share for primary transit agency by MSA 52
10. Morning peak period passenger alightings for San Diego CBD- serving routes 55
11. RT Gold Line weekday a. m. peak alightings 57
12. RT Gold Line weekday a. m. peak alightings 59
13. Dallas ( DART) LRT afternoon peak period boardings 59
14. RTD Southeast Corridor light rail transit boardings 63
15. RTD Southwest Corridor light rail transit boardings 63
16. Summary of transfer rates by mode for all MSAs 64
17. Access and egress methods used by San Diego transit riders 66
18. San Diego top 20 transit stops in fiscal year 2005 and fiscal year 2005 67
19. Author- calculated MARTA transfer rate ( 1972– 2003) 70
20. Breakdown of MARTA linked trips 70
21. Population in the metropolitan Atlanta area ( 1970– 2005) 93
22. Employment in the Atlanta metropolitan area ( 1970– 2005) 96
23. Transit ridership ( passenger miles) on non- MARTA systems ( 1990– 2004) 100
24. Demographics of CCT and Gwinnett/ Clayton county transit riders 102
25. Atlanta MARTA rail segment openings since 1980 103
26. Demographics of MARTA transit riders 105
27. Author- calculated MARTA transfer rate ( 1972– 2003) 110
28. Breakdown of MARTA linked trips 110
29. Ridership on MARTA fixed- route transit services ( 1972– 2004) 114
30. Average trip lengths ( MARTA) ( 1984– 2004) 115
31. MARTA fixed- route transit service ( 1972– 2004) 116
32. MARTA service productivity ( 1984– 2004) 118
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33. MARTA bus route performance 119
34. Population in the Dallas– Fort Worth metropolitan area ( 1970– 2005) 127
35. Employment in the Dallas- Ft. Worth metropolitan area ( 1970– 2005) 130
36. Transit ridership ( passenger miles) on non- DART systems ( 1984– 2004) 135
37. Dallas DART rail segment openings since 1996 136
38. Demographics of DART transit riders 138
39. Mode use and transfer activity by DART riders 141
40. Ridership on DART fixed route transit services 143
41. Average trip lengths ( DART) ( 1984– 2004) 144
42. DART fixed route transit service ( 1984– 2004) 145
43. DART service productivity ( 1984– 2004) 145
44. DART bus route performance 147
45. Dallas ( DART) LRT afternoon peak period boardings 148
46. Population in the Denver metropolitan area ( 1970– 2005) 156
47. Employment in the Denver metropolitan area ( 1970– 2000) 158
48. Denver TRD rail segment openings since 1994 160
49. Demographics of RTD bus riders 164
50. RTD bus use by trip purpose 165
51. Riding habit of RTD bus riders 165
52. Demographics of RTD light rail riders 166
53. RTD light rail transit use by trip purpose 166
54. Riding habit of RTD light rail transit riders 166
55. Transfer rates on RTD bus system 171
56. Ridership on RTD fixed route transit services ( 1984– 2004) 173
57. Average trip lengths ( RTD) ( 1984– 2004) 174
58. RTD fixed route transit service ( 1984– 2004) 175
59. RTD service productivity ( 1984– 2004) 176
60. RTD bus route performance 177
61. RTD light rail transit performance 178
62. RTD Southeast Corridor light rail transit boardings 179
63. RTD Southwest Corridor light rail transit boardings 179
64. Population in the Miami metro area ( 1970– 2005) 188
65. Employment in the Miami metropolitan area ( 1970– 2005) 190
66. Broward County Transit ( BCT) ridership, service and performance ( 1984– 2004) 196
67. Palm Tran ridership, service and performance ( 1984– 2004) 198
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68. Tri- Rail ridership, service and performance ( 1989– 2004) 198
69. Miami MDT rail segment openings since 1984 199
70. Demographics of MDT bus riders 201
71. Demographics of MDT metro rail riders 202
72. Access/ egress methods for MDT bus riders 205
73. Access/ egress methods for MDT Metro Rail riders 206
74. MDT bus rider attitudes toward transferring 206
75. Ridership on MDT fixed route transit services ( 1984– 2004) 208
76. Average trip lengths ( MDT) ( 1984– 2004) 209
77. MDT fixed route transit service ( 1984– 2004) 210
78. MDT service productivity ( 1984– 2004) 211
79. MDT bus route average weekday performance 212
80. Population in the Minneapolis- St. Paul metropolitan area 221
81. Employment in the Minneapolis- St. Paul metropolitan area ( 1970– 2005) 224
82. Minneapolis- St. Paul light rail transit segment openings 228
83. Demographics of Metro Transit bus riders 229
84. Demographics of Metro Transit light rail transit riders 229
85. Access methods for Metro Transit LRT riders 230
86. Ridership on Metro Transit fixed- route transit services ( 1984– 2004) 236
87. Metro Transit fixed route transit service ( 1984– 2004) 237
88. Metro Transit fixed- route transit service ( 1984- 2004) 238
89. Metro Transit service productivity ( 1984– 2004) 238
90. Metro Transit bus route performance 239
91. Population in the Pittsburgh metropolitan area ( 1970– 2005) 247
92. Employment in the Pittsburgh metropolitan area ( 1970– 2005) 249
93. Transit ridership ( UPT) on smaller Pittsburgh systems ( 1984– 2004) 254
94. Transit ridership ( passenger miles) on smaller Pittsburgh systems ( 1984– 2004) 254
95. Pittsburgh light rail transit segment openings since 1984 258
96. Ridership on PAT fixed route transit services ( 1984– 2004) 263
97. Average trip lengths ( PAT) ( 1984– 2004) 263
98. PAT fixed route transit service ( 1984– 2004) 264
99. PAT service productivity ( 1984– 2004) 265
100. PAT bus route performance 266
101. Population in the Portland metropolitan area ( 1970– 2005) 273
102. Employment in the Portland metropolitan area ( 1970– 2005) 276
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103. Clark County Transit ( C- Tran) ridership and service ( 1984– 2004) 279
104. Portland light rail transit segment openings since 1986 281
105. Demographics of Tri- Met riders 282
106. Reasons riders use Tri- Met transit services 282
107. Modes used byTri- Met riders 282
108. Transfers made by Tri- Met riders to complete a one- way trip 285
109. Ridership on Tri- Met fixed route transit services ( 1984– 2004) 287
110. Average trip lengths ( Tri- Met) ( 1984– 2004) 288
111. Tri- Met fixed route transit service ( 1984– 2004) 289
112. Tri- Met service productivity ( 1984– 2004) 290
113. Tri- Met bus route performance 291
114. Population in the Sacramento metropolitan area ( 1970– 2005) 301
115. Employment in the Sacramento metropolitan area ( 1970– 2005) 304
116. Ridership on smaller Sacramento systems ( 1984– 2004) 308
117. Sacramento light rail transit segment openings since 1987 309
118. Demographics of RT riders 310
119. Ridership on RT fixed route transit services ( 1984– 2004) 314
120. Average trip lengths ( RT) ( 1984– 2004) 315
121. RT fixed route transit service ( 1984– 2004) 317
122. RT service productivity ( 1984– 2004) 317
123. RT bus route performance 318
124. RT Blue Line weekday a. m. peak alightings 321
125. RT Gold Line weekday a. m. peak alightings 322
126. Population in the Salt Lake City metropolitan area ( 1970– 2005) 327
127. Employment in the Salt Lake City metropolitan area ( 1970– 2005) 330
128. Salt Lake City light rail transit segment openings 334
129. Ridership on UTA fixed- route transit services ( 1984– 2004) 338
130. Average trip lengths ( UTA) ( 1984– 2004) 339
131. UTA fixed- route transit service ( 1984– 2004) 340
132. UTA service productivity ( 1984– 2004) 340
133. UTA bus route average weekday performance 341
134. Population and employment in the San Diego metropolitan area ( 1970– 2005) 348
135. San Diego light rail transit segment openings 352
136. Demographics of San Diego transit riders 354
137. Access and egress methods used by San Diego transit riders 354
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List of Tables xvii
138. Ridership on San Diego MSA fixed- route transit systems ( 1984– 2004) 364
139. Average trip lengths ( San Diego) ( 1984– 2004) 364
140. San Diego fixed- route transit service ( 1984– 2004) 365
141. San Diego service productivity ( 1984– 2004) 366
142. San Diego average weekday bus route performance ( FY 2006) 367
143. San Diego rail line average weekday performance ( FY 2006) 369
144. Morning peak period passenger alightings for San Diego CBD- serving routes 369
145. San Diego top 20 transit stops in fiscal year 2005 and fiscal year 2006 370
146. Population and employment in the San José metropolitan area ( 1970– 2005) 377
147. San José light rail transit segment openings 382
148. Demographics of VTA Riders 382
149. Access and egress modes for VTA riders 383
150. Ridership on VTA fixed- route transit services ( 1984– 2004) 390
151. Average trip lengths ( VTA) ( 1984– 2004) 391
152. VTA fixed- route transit service ( 1984– 2004) 392
153. VTA service productivity ( 1984– 2004) 392
154. VTA bus route performance 393
155. VTA light rail transit line performance 394
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Mineta Transportation Institute
1
EXECUTIVE SUMMARY
This investigation of the role of service planning decisions in promoting rail transit success or
failure focused on the experiences of eleven metropolitan areas with between 1 million and 5
million persons that have rail transit. These metropolitan areas include: Atlanta, Georgia;
Dallas- Fort Worth, Texas; Denver, Colorado; Miami, Florida; Minneapolis- St. Paul,
Minnesota; Pittsburgh, Pennsylvania; Portland, Oregon; Sacramento, California; Salt Lake
City, Utah; San Diego, California; and San José, California. The authors collected and
examined a combination of documentary evidence and statistical data, and conducted
interviews with key informants in each study area. The resulting case study narratives are
included as appendices in this project’s report.
The authors define a rail transit system as having been successful if it has contributed in a
favorable way to metropolitan transit riding habit and service productivity. Riding habit refers
to the number of passenger miles per capita for the combined set of transit agencies in a
metropolitan area. Service productivity refers to load factor, the ratio of passenger miles to
vehicle miles, for the combined set of transit agencies in a metropolitan area. For this study’s
purposes, riding habit success means that transit patronage ( measured as passenger miles) is
keeping pace with or exceeding population growth. Service productivity success means that a
metropolitan area’s transit agencies are experiencing either productivity increases or
productivity declines less severe than the national average ( nationally, service productivity fell
14% from 1984 to 2004).
Based on these definitions, two metropolitan areas emerge from the analysis of transit
performance as unqualified successes: Portland and San Diego. Portland is clearly a success. It
ended the period with the largest riding habit while also experiencing the largest percentage
growth in riding habit. It also experienced a very large increase in productivity, ending up
with the second most productive transit among the cases.
San Diego also is a success. Its riding habit increased by almost 30 percent, ending the period
almost tied with Denver and Atlanta, but lower than Portland and Miami. Its productivity,
relatively high to begin with, also improved, but only slightly. All of this is despite San Diego
slipping significantly from 2002 through 2004 in both riding habit and productivity. ( San
Diego today likely is higher on both these counts. The authors obtained special passenger
tallies from 2003 through 2007, showing very strong ridership growth between 2004 and
2007 inclusive of all its modes, as discussed in the case study.)
The other metropolitan areas offer a more mixed record. In general, those metropolitan areas
that have a more multidestination vision and have leveraged their rail investments to bring
about that vision ( San Diego, Portland, Miami, and Atlanta) have been the most productive.
They also have enjoyed the best record in riding habit. Those metropolitan areas with
relatively minor rail services set in a system with a central business district ( CBD)- express bus
2 Executive Summary
Mineta Transportation Institute
focus ( Pittsburgh and Minneapolis- St. Paul) have lower overall regional transit productivity
and less encouraging riding habits.
Those metropolitan areas that have introduced very good rail services but have continued to
operate bus services in competition with them ( Salt Lake City and Sacramento in terms of its
more recent rail extensions, and Pittsburgh) generally have obtained good results for their rail
lines but poor results with their bus systems, with an overall depressing effect on regional
transit performance. These systems generally have viewed bus and rail systems as competitive,
and they let the passenger decide what mode of transit is best for their particular trip. The
result has been duplicative service between many suburban points and the CBD and the
absence of service, or very inconvenient service to other destinations. This condition has
produced low productivity primarily for the bus services.
Overall, this study’s analysis indicates that the most successful metropolitan areas have
deployed rail transit as the backbone of an integrated, multidestination bus- rail transit system
that provides the passengers with the ability to access an array of regional destinations. The
analysis revealed a number of principles that underlie rail transit success. The key principles
are as follows:
1. Successful transit systems articulate a clear, multidestination vision for regional transit.
A multidestination vision is premised on the notion that the transit market consists of a mix of
passengers traveling for varying purposes at different times of the days to many different parts
of the metropolitan area. Metropolitan areas that embrace this vision disperse their service
throughout their networks. In these networks, rail lines replaced many of the bus routes that
formerly traveled to the CBD. Bus routes tend to be more focused on rail stations in the
suburbs, both feeding passengers to CBD- bound trains, but also distributing train passengers
to suburban destinations. Transfers are important, designed to expand the number of
destinations that passengers may reach. In such systems, rail lines sometimes function as
regional distributor lines, absorbing passengers from connecting bus services in the suburbs
and distributing these passengers to important destinations or to important bus transfers in
many parts of the regions.
The authors’ analysis indicates that the most successful metropolitan areas embraced the
multidestination service philosophy and applied it on a regional scale. In the most successful
metropolitan areas, transit patrons can use a combined bus- rail transit system to easily reach a
wide array of destinations both inside and outside the CBD. Less successful metropolitan areas
do not present the same array of travel options to their patrons. Some focus most of their
service on the CBD, which is a declining activity center. Others do not integrate their bus and
rail services to feed one another. Still others embrace an integrated, multidestination vision,
but apply it on a less- than- regional scale. In each of these cases, the net result is lower riding
habit and service productivity, in short lower transit performance, than the region might
otherwise have enjoyed.
2. Successful transit systems rely on rail transit as the system’s backbone.
Mineta Transportation Institute
Executive Summary 3
The most successful metropolitan areas rely heavily on rail transit as the backbone to the
metropolitan transit system. In these areas, rail carries a disproportionate share of riders
compared to the proportion of service that it represents. It does so not only because of its
higher carrying capacity than bus, but also because it plays an important role moving
passengers throughout the larger transit network. In metropolitan areas like Atlanta, for
example, the rail system serves as a trunk line, and the extensive bus network serves as a feeder
and distribution system for the region.
The authors’ analysis indicates that the most successful metropolitan areas use rail transit as a
backbone for their regional transit systems, around which they restructure their bus network.
The rail then serves as a trunk line and the bus network as feeders and distributors for a system
that provides riders with service to an array of travel destinations throughout the metropolitan
area. Much less successful is an approach where rail is a minor part of a larger CBD express bus
based vision. Metropolitan areas that have adopted this approach have experienced
lower- than- expected and/ or declining patronage— even in corridors similar to those where rail
has seen high or increasing patronage.
3. Successful transit systems recognize the importance of the non- CBD travel market.
Most transit agencies have long regarded the CBD as an important focal point for their transit
service, and the widespread incidence of CBD- radial transit networks attests to the continuing
popularity of this philosophy. However, the most successful metropolitan areas make a
conscious effort to serve non- CBD destinations, because those are the parts of the metropolitan
area that are growing and contain most of the destinations transit patrons wish to reach.
The authors’ analysis indicates that non- CBD bound riders make up a sizable share of
patronage on even CBD- focused transit services. Thus, serving non- CBD markets is even more
critical than one might have initially expected. These non- CBD destinations represent the
major destinations patrons wish to reach, and they are also the areas of growth in each
metropolitan area. The CBDs, by contrast, are in most cases stagnant or in decline.
4. Successful transit systems encourage the use of transfers to reach a wider array of
destinations.
The use of transfers makes it possible for transit systems to serve a wider array of origins and
destinations in dispersed metropolitan areas than can be served by one- seat- ride,
point- to- point service. Transfers help extend the geographic reach of the transit system.
The authors’ analysis shows that successful transit systems take advantage of the potential for
smooth transfers to broaden the array of potential destinations that their passengers can reach.
These systems make it easy for their passengers to transfer by timing the connections to
minimize wait time, and thus reducing the time penalty associated with transfers. They
provide free transfer rights for their riders to reduce the financial penalty associated with
transfers. Less successful transit systems do not do these things. They either attempt to avoid
transfers by providing one- seat- ride service to a much smaller set of destinations, and/ or they
make it difficult and inconvenient for their riders who must transfer.
4 Executive Summary
Mineta Transportation Institute
5. Successful transit systems recognize that rail transit alone is not enough to guarantee
success.
The most successful transit systems take a comprehensive approach to rail transit planning
that focuses on providing passengers with easy access to the rail service, often through an array
of modes. The service is located in a corridor that allows rail transit, and its bus connections, to
link the major activity centers to which patrons wish to travel. These principles are followed
by successful rail transit systems in San Diego, Portland, and Atlanta.
The authors’ analysis shows that simply placing rail transit in corridors that are collocated
with major activity centers is not sufficient to guarantee ridership success. It is necessary to
carefully plan how riders will access and egress the rail transit system and then reach their final
destination. It is also important to provide high- speed, high- frequency service. The analysis
also shows that using rail transit as an economic redevelopment tool may result in
lower- than- anticipated ridership when the development fails to materialize. This happens
when the line is built in a corridor where development makes no economic sense, regardless of
planning measures to stimulate it, as was the case in Miami. The development that Miami
Metrorail was supposed to stimulate in the depressed sector of northwest Miami never
materialized, and patronage from that corridor never materialized either.
On the other hand, extending rail transit into a corridor that is “ hot” for development from
the perspective of both the market and regional planning priorities can result in
complementary development occurring around rail transit stations. This has been the case in
Portland’s Washington County and to a lesser extent in San Diego’s Mission Valley.
6. Successful transit systems recognize the importance of serving regional destinations.
One of the most important lessons from the case studies is that successful transit systems seek
to serve all of the region’s major activity centers. These activity centers represent the
destinations to which people wish to travel, and failure to serve these centers with
high- quality service places transit at a competitive disadvantage versus the automobile. In
metropolitan areas where significant activity centers are not served, the result has been
diminished riding habit and productivity.
The authors’ analysis clearly indicates that the most successful transit systems provide
high- quality service to the array of major activity centers throughout the region. The rail
system serves as a backbone for the regional transit service strategy. Less successful systems
either serve only a limited portion of the region or prioritize serving one major activity center,
the CBD, despite the fact that this center is in relative decline in nearly all the study areas. As
the discussion of Atlanta indicates, extending the reach of a successful sub- regional system to
an entire region is not an overwhelming task from a logistical and planning perspective,
although in certain settings it may require a vote of the electorate or legislative action.
Mineta Transportation Institute
5
INTRODUCTION
Between 1980 and 2005, sixteen U. S. metropolitan areas opened rail transit systems. These
metropolitan areas joined ten others whose rail transit systems predate the recent rail transit
renaissance. 1 Some of these rail transit metropolises have enjoyed increased riding habit and/ or
service productivity in recent years, while others have experienced stagnant or declining riding
habit and/ or service productivity. The purpose of this research is to understand why some
metropolitan areas with rail transit have experienced transit performance success and others
have not done so. The specific focus of this research is to better understand the role that service
planning decisions have played in rail transit success or failure.
The eleven metropolitan areas that we examine in this report have both bus and rail transit
services. But the various metropolitan areas’ transit agencies have approached the planning of
these two parts of the transit system very differently. In some metropolitan areas, transit
agencies use both modes, and the ability for passengers to transfer between them, to expand
the geographic reach of the transit system. In other metropolitan areas, transit agencies have
focused, as much as possible, on providing one- seat rides between suburban residential
districts and a primary activity center, generally the central business district. In some
metropolitan areas, transit agencies restructured their bus systems once they opened their rail
transit investment. In other metropolitan areas, transit agencies did not significantly change
their bus systems when the rail transit opened.
Through this research, the authors have assessed the effects of the various service strategies the
transit agencies have pursued, while also taking into account the roles played by metropolitan
population and employment trends, urban structure, and transportation- land use policies
( including transit- oriented development) as influences on rail transit success or failure. Our
hypothesis is that service planning decisions are important determinants of ridership and
productivity success that most scholarly and practitioner literature has tended to overlook.
The authors defined a rail transit system as having been successful if it has contributed in a
favorable way to overall transit riding habit and service productivity. Riding habit refers to the
number of passenger miles per capita for the combined set of transit agencies in a metropolitan
area. Service productivity refers to load factor, the ratio of passenger miles to vehicle miles, for
the combined set of transit agencies in a metropolitan area. For the purposes of this study,
riding habit success means that transit patronage ( measured as passenger miles) is keeping
pace with or exceeding population growth. Service productivity success means that a
metropolitan area’s transit agencies are experiencing either productivity increases or
productivity declines less severe than the national average ( nationally, service productivity fell
14% from 1984 to 2004). 2
6 Introduction
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Transit Performance in MSAs with 1 Million to 5 Million Persons
Prior to undertaking this research, the authors examined transit performance trends between
1984 and 2004 in all 45 U. S. metropolitan statistical areas ( MSA) with year 2000 populations
between 1 million and 5 million. 3 They selected this population range because it includes
most of the recent additions to the ranks of the rail transit metropolises. This population class
excludes larger metropolitan areas such as New York, Chicago, and Philadelphia, whose urban
development and transit development histories are quite different from those of most other
metropolitan areas. The 1 million to 5 million population class is also the locale for much of
the recent urban population growth in the United States. This class includes many non- rail
cities that may be considering investing in rail transit.
The authors stratified the 45 metropolitan areas based on their classification on two variables.
First, they distinguished between metropolitan areas that had bus- only transit systems and
those with combined bus- rail systems. Second, they distinguished between metropolitan areas
on the basis of their service orientation. Service orientation refers to the way a transit agency
structures its service. A transit agency manager can concentrate service on the central business
district ( CBD) or disperse service to connect multiple destinations. The first approach
represents a radial service orientation, whereas the second represents a multidestination service
orientation. Here, they examined the percent of transit routes that served the CBD and
classified the metropolitan areas as either radial ( those with more than 55% of bus routes
serving the CBD) or multidestination ( those with fewer than 55% of bus routes serving the
CBD). The authors chose 55% because it is a number slightly above 50%, or half of the area’s
transit routes. Combining these two classification schemes results in four groups of
metropolitan areas: multidestination bus- and- rail, multidestination bus- only, radial
bus- and- rail, and radial bus- only. The metropolitan areas contained in each of the four groups
are shown in Table 1.
To undertake the transit performance analysis, the authors aggregated the transit data from all
transit agencies in each metropolitan area to produce metropolitan- level performance
measures. They examined the performance of each metropolitan area between 1984 and 2004
on two measures: riding habit ( passenger miles per capita) and service productivity ( passenger
miles per vehicle mile). They also examined the performance of the four MSA groups
( stratified as discussed above) on each of these performance measures. The authors used the
median value as the measure of overall group performance.
Mineta Transportation Institute
Introduction 7
Table 1 Classification of 45 study MSAs
Table 2 presents the results of our investigation of riding habit ( passenger miles per capita).
The table reports riding habit by MSA for 1984, 2004, and the percent change in riding habit
between 1984 and 2004. The left panel of the table presents performance statistics for
multidestination MSAs and the right panel reports does so for radial MSAs. The top half of the
table reports performance statistics for bus- and- rail MSAs and the bottom half of the table
does so for bus- only MSAs. Underneath each MSA group panel, the table reports the median
value for the group in 1984 and 2004 and the median percent change ( 1984– 2004).
Multidestination MSAs
Bus Only ( in 2004) % Non- CBD Routes Bus and Rail ( in 2004) % Non- CBD Routes
Las Vegas 73.58 Atlanta 75.00
Milwaukee 48.53 Dallas 61.08
Norfolk 49.18 Denver 58.70
Phoenix 61.36 Miami 67.61
Rochester 45.00 New Orleans 50.50
San Antonio 45.00 Portland 56.82
Sacramento 69.05
St. Louis 54.55
San Diego 81.87
Seattle 53.88
Radial MSAs
Bus Only % Non- CBD Routes Bus and Rail ( in 2004) % Non- CBD Routes
Albany 9.52 Buffalo 34.92
Austin 22.86 Cleveland 39.68
Birmingham 5.41 Hartford 6.90
Charlotte 27.16 Houston 38.32
Cincinnati 7.14 Jacksonville 23.81
Columbus 24.14 Memphis 20.90
Dayton 23.53 Minneapolis- St. Paul 34.80
Grand Rapids 21.74 Pittsburgh 21.33
Greensboro 16.39 Salt Lake City 42.42
Greenville 0.00
Indianapolis 7.14
Kansas City 40.82
Louisville 29.63
Nashville 0.00
Oklahoma City 16.13
Orlando 43.75
Providence 29.31
Raleigh 22.89
Richmond 9.62
Tampa 33.33
Source: Brown and Thompson, 2008
8 Introduction
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The table shows that the multidestination MSA groups enjoyed higher riding habit than the
radial MSA groups in 1984 and 2004, and over time between 1984 and 2004. The
bus- and- rail MSA groups enjoyed higher riding habit than the bus- only MSA groups in 1984,
in 2004, and over time between 1984 and 2004. The median MSA in three of the four groups
( multidestination bus- only, radial bus- and- rail, and radial bus- only) experienced decreased
riding habit between 1984 and 2004. The exception to this pattern is the multidestination
bus- and- rail group ( shown in the top left table panel). The median MSA in this group enjoyed
the best riding habit performance of all the groups in 1984 ( 128.4), in 2004 ( 148.9), and over
time between 1984 and 2004 (+ 8.8%). In the median MSA in this group, riding habit
increased faster than population between 1984 and 2004.
Within the multidestination bus- and- rail MSA group, there is considerable variation in riding
habit change between 1984 and 2004. One potential explanation for this variation in riding
habit change relates to the use transit agencies in each MSA make of their rail transit
investments in the context of the overall regional transit network. This is, in fact, a focus of
this project’s research. Most MSAs within the group introduced rail transit some time during
the study period ( including Dallas, Denver, Miami, Portland, Sacramento, Saint Louis, and
Seattle).
Table 3 presents the results of the authors’ investigation of service productivity ( passenger
miles per vehicle mile). The table is organized in the same manner as Table 2. As was true in
the case of riding habit, the best performing MSAs were the multidestination bus- and- rail
MSAs. Using the median MSA as the means of comparison, the table shows that these MSAs
enjoyed the highest productivity among the four groups in 1984 and in 2004 ( 11.3 and 9.3,
respectively) and also experienced the smallest productivity decline between 1984 and 2004
(- 12.7%). The productivity decline among the multidestination bus- and- rail MSAs was about
one- half the productivity decline in the radial bus- and- rail MSAs (- 25.4%), suggesting that
multidestination service orientation is a significant explanation for better transit productivity.
This suggestion is strengthened by the performance of the multidestination bus- only MSAs.
These were the second best performers in 2004 ( after ranking third among the groups in 1984)
and saw productivity decline only slightly more than for their bus- and- rail counterparts
(- 15.3%). By contrast, the radial MSAs ( both bus- only and bus- and- rail) saw productivity
declines in excess or 25% between 1984 and 2004.
Interestingly, nine MSAs experienced service productivity increases between 1984 and 2004,
and three of these MSAs possessed high productivity transit systems ( load factors greater than
10) in 2004. Two of the three MSAs ( Portland and San Diego) are in the multidestination
bus- and- rail group, while the third ( Las Vegas) is in the multidestination bus- only group.
Mineta Transportation Institute
Introduction 9
Table 2 Riding habit ( passenger miles per capita) in 45 MSAs
Multidestination
Bus and Rail
MSAs
1984 2004
Percent
Change
( 1984– 2004)
Radial Bus and
Rail MSAs 1984 2004
Percent
Change
( 1984– 2004)
Atlanta 173.06 149.07 - 13.86 Buffalo 82.21 56.53 - 31.23
Dallas 63.33 66.12 4.40 Cleveland 144.50 94.57 - 34.56
Denver 131.74 149.01 13.11 Hartford 68.50 59.43 - 13.24
Miami 125.14 163.80 30.90 Houston 97.41 99.28 1.92
New Orleans 161.51 94.92 - 41.23 Jacksonville 55.01 48.28 - 12.23
Portland 161.89 223.71 38.19 Memphis 40.87 55.85 36.66
Sacramento 74.41 67.66 - 9.07 Minneapolis- St
Paul 105.62 86.36 - 18.24
St. Louis 71.66 91.72 28.00 Pittsburgh 130.46 116.43 - 10.75
San Diego 117.19 148.87 27.03 Salt Lake City 70.66 80.66 14.15
Seattle 203.13 198.06 - 2.49
Median 128.44 148.94 8.76 Median 82.21 80.66 - 12.23
Multidestination
Bus Only MSAs 1984 2004
Percent
Change
( 1984– 2004)
Radial Bus
Only MSAs 1984 2004
Percent
Change
( 1984– 2004)
Las Vegas 22.67 107.05 372.17 Albany 53.40 45.90 - 14.05
Milwaukee 143.49 101.99 - 28.93 Austin 60.16 80.02 33.02
Norfolk 53.06 51.32 - 3.26 Birmingham 21.32 17.13 - 19.64
Phoenix 40.16 53.49 33.20 Charlotte 22.23 36.68 65.02
Rochester 53.67 39.13 - 27.10 Cincinnati 81.42 72.40 - 11.08
San Antonio 101.14 82.84 - 18.09 Columbus 82.39 25.15 - 69.48
Dayton 85.99 41.61 - 51.61
Grand Rapids 16.37 17.03 4.04
Greensboro 11.67 9.50 - 18.65
Greenville 4.46 4.94 10.69
Indianapolis 44.54 22.82 - 48.76
Kansas City 33.83 25.74 - 23.92
Loiusville 86.59 40.42 - 53.32
Nashville 34.97 20.15 - 42.39
Oklahoma City 12.52 16.75 33.80
Orlando 27.24 68.62 151.96
Providence 55.51 55.07 - 0.78
Raleigh 15.96 27.64 73.13
Richmond 69.86 29.39 - 57.93
Tampa 38.64 38.82 0.48
Median 53.36 68.16 - 10.68 Median 36.80 28.51 - 12.56
Source: Brown and Thompson, 2008.
10 Introduction
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Table 3 Service productivity ( passenger miles per vehicle mile) in 45 MSAs
Multidestination
Bus and Rail
MSAs
1984 2004
Percent
Change
( 1984– 2004)
Radial Bus and
Rail MSAs 1984 2004 Percent Change
( 1984– 2004)
Atlanta 13.88 13.79 - 0.72 Buffalo 10.23 6.49 - 36.61
Dallas 11.86 8.60 - 27.50 Cleveland 14.21 7.97 - 43.95
Denver 9.17 7.47 - 18.51 Hartford 10.24 6.77 - 33.93
Miami 11.38 10.34 - 9.13 Houston 9.81 9.56 - 2.62
New Orleans 14.64 8.68 - 40.72 Jacksonville 7.56 5.64 - 25.44
Portland 8.41 12.25 45.53 Memphis 6.67 8.18 22.61
Sacramento 11.35 9.14 - 19.52 Minneapolis- St
. Paul 9.70 8.19 - 15.63
St. Louis 7.77 9.16 17.92 Pittsburgh 10.05 7.18 - 28.59
San Diego 10.95 11.15 1.77 Salt Lake City 6.05 5.56 - 8.11
Seattle 11.21 9.38 - 16.29
Median 11.28 9.27 - 12.71 Median 9.81 7.18 - 25.44
Multidestination
Bus Only MSAs 1984 2004
Percent
Change
( 1984– 2004)
Radial Bus
Only MSAs 1984 2004 Percent Change
( 1984– 2004)
Las Vegas 10.90 11.23 3.05 Albany 9.15 6.97 - 23.83
Milwaukee 9.29 7.19 - 22.64 Austin 7.24 6.98 - 3.55
Norfolk 8.31 7.65 - 7.96 Birmington 6.83 5.98 - 12.50
Phoenix 8.88 6.29 - 29.18 Charlotte 9.46 6.90 - 27.02
Rochester 8.97 6.59 - 26.45 Cincinnati 9.95 9.11 - 8.45
San Antonio 8.59 8.01 - 6.74 Columbus 12.75 4.81 - 62.27
Dayton 12.96 5.56 - 57.12
Grand Rapids 5.63 5.73 1.79
Greensboro 8.57 4.92 - 42.63
Greenville 5.42 7.00 33.73
Indianapolis 10.66 6.31 - 40.76
Kansas City 6.59 4.74 - 28.03
Louisville 11.96 6.47 - 45.89
Nashville 8.53 5.28 - 38.13
Oklahoma City 4.80 5.11 6.46
Orlando 7.22 9.37 29.93
Providence 8.72 7.19 - 17.55
Raleigh 6.18 4.55 - 26.29
Richmond 11.57 5.96 - 48.50
Tampa 8.26 5.99 - 27.52
Median 8.92 7.42 - 15.30 Median 8.55 5.98 - 26.65
Source: Brown and Thompson, 2008.
Mineta Transportation Institute
Introduction 11
Transit Performance in Eleven Metropolitan Areas
The descriptive examination presented above suggests that transit agencies in
multidestination bus- and- rail metropolitan areas are making planning decisions that lead to
better performance outcomes than their radial counterparts. The authors explored this issue in
more detail by looking closely at eleven metropolitan areas in the 1 million to 5 million
population class that have bus and rail transit systems. These metropolitan areas are located in
different parts of the United States. The authors selected eight multidestination metropolitan
areas and three radial metropolitan areas. The eight multidestination metropolitan areas are
Atlanta, Dallas, Denver, Miami, Portland, Sacramento, San Diego, and San José. ( San José is
included in the set of multidestination MSAs because fewer than 55% of its bus routes serve
the San José CBD.) All of these metropolitan areas, save San José, were included in the
descriptive examination discussed earlier. In the earlier study, San José was considered part of
the consolidated San Francisco Metropolitan Statistical Area, a region whose aggregated
population was outside of the 1 to 5 million population range of metropolitan areas we
examined. The three radial metropolitan areas are Minneapolis, Pittsburgh, and Salt Lake City.
These eleven metropolitan areas have experienced very different trends with respect to riding
habit and service productivity in recent years. Figure 1 graphs riding habit for each of the
eleven metropolitan areas for every year from 1984 to 2004. The figure shows wide variation
among the metropolitan areas with respect to the magnitude of riding habit and both the
magnitude and direction of riding habit change. Particularly striking are the divergent trends
among the metropolitan areas. Portland, for example, enjoyed high riding habit in 1984 and
experienced increased riding habit since that time. Other metropolitan areas, including
Minneapolis, Pittsburgh and San José, had moderate riding habit in 1984 but have
experienced falling riding habit since that time.
12 Introduction
Mineta Transportation Institute
Figure 1 Riding habit for 11 metropolitan areas
Figure 2 graphs service productivity for the set of eleven metropolitan areas over the same
time period. As was true for riding habit, the figure shows considerable variation in service
productivity among the metropolitan areas. Most metropolitan areas experienced declining
productivity over the period, but they began the period with very different levels of service
productivity. Three metropolitan areas stand out as having begun with high service
productivity in 1984 and experienced stable or increased service productivity since that time.
These three metropolitan areas are Atlanta, Portland, and San Diego. The latter two
metropolitan areas increased their service productivity over this time, with Portland
increasing service productivity in excess of 40%. On the other hand, there are metropolitan
areas that began with low productivity and experienced productivity declines. These
metropolitan areas include Minneapolis, Salt Lake City, and San José. Dallas and Pittsburgh
are also noteworthy for their productivity declines.
Mineta Transportation Institute
Introduction 13
Figure 2 Service productivity for 11 metropolitan areas ( 1984− 2004)
The purpose of this research is to understand the reasons for the trends shown in Figure 1 and
Figure 2. In particular, the authors wanted to understand the roles that service planning
decisions played in affecting these trends. All eleven metropolitan areas have both bus and rail
transit systems today, but the rail systems were introduced, expanded, or modernized at
different times during the study period for each metropolitan area, and transit system decision
makers in each area used the rail lines and bus services in different ways to change transit
mobility in their regions.
The outcomes in terms of overall performance of the respective transit systems have varied
widely. What explains this variation? Do service planning decisions play a role in explaining
the variation in performance? What lessons can be drawn from both positive and negative
experiences? How should these lessons influence service planning decisions in other cities that
have ( or are contemplating) rail transit investments? The authors discussed all of these
questions, and numerous others, in the course of this investigation. It is their hope that the
discussion presented in this volume will be of practical benefit to transit planners and
managers and of scholarly benefit to other researchers attempting to better understand the role
transit does ( and can) play in today’s increasingly decentralized urban environments.
The authors purposefully titled one section of the report a Guidebook in the hope that its
contents can be of direct practical benefit to transit industry policymakers and planners. It is
14 Introduction
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written in a way that facilitates its use as a stand- alone document. The Guidebook highlights
the key lessons from the detailed individual case studies contained in the first eleven report
appendices. These case studies themselves tell interesting stories about the different
approaches to transit planning and policy taken by transit agency managers, local
policymakers, and other interested actors in each metropolitan area, and our sense of the
results of these approaches. The authors direct the reader’s attention to these case studies for
the detailed stories and numerous lessons they contain.
Mineta Transportation Institute
15
WHAT WE DO KNOW ABOUT THE FACTORS
ASSOCIATED WITH ( RAIL) TRANSIT SUCCESS OR FAILURE
Our research focus is to evaluate the influence of service planning decisions on rail transit
success or failure. In particular, we are interested in how rail service planning decisions
influence metropolitan transit ridership. In conducting this examination, we need to take into
account an array of other factors that may also influence transit ridership. To identify these
factors and consider their likely effects on ridership, we consulted an extensive literature. ( We
include an annotated bibliography of sources cited in this section as Appendix M.)
The literature, which largely consists of works that examine transit ridership in general, as
opposed to rail transit in particular, classifies these factors into two general categories. The
first category consists of factors that are outside the control of transit agency managers and
hence are called external factors. These include: the urban structure of a metropolitan area,
land use patterns around bus or rail stations, levels of automobile ownership in the
community, automobile costs ( including fuel and parking prices), regional economic health,
personal and household incomes, and the race, ethnicity, and immigrant profiles of the
metropolitan area. All these factors have been linked ( either positively or negatively) to the
level of transit usage by area residents.
The second category consists of factors that are at least partially under the control of transit
agency managers and hence are called internal factors. These include: fare structures and
policies, service coverage, service frequency, service orientation, amenities, and special services
targeted to specific groups of users. All these factors have been linked ( either positively or
negatively) to the level of transit usage by area residents.
In this chapter, we briefly review the literature on factors that affect transit ridership. First, we
present literature that examines transit ridership in general. We then present literature that
considers rail transit ridership in particular. Both types of literature are relevant to our case
study, because both identify factors whose influence we need to account for in conducting our
examination of the influence of service planning decisions on rail transit ridership. We close
the chapter with a brief summary of key insights from the literature.
LITERATURE ON TRANSIT RIDERSHIP IN GENERAL
The literature review discussion proceeds as follows: 1) works that provide a descriptive
overview of transit ridership; 2) works that emphasize external factors that affect ridership; and
3) works that emphasize internal factors that affect ridership.
16 What We Do Know About the Factors Associated with ( Rail) Transit Success or Failure
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Descriptive Overview of Transit Ridership
In the postwar period, transit ridership experienced a long decline followed by a number of
recent peaks and valleys. Jones and Vuchic provide general discussion of the longer- term
trend, emphasizing the decentralization of urban areas and competition with the automobile
as among the primary causes for transit’s postwar decline. 4 By the 1970s and 1980s, Jones
observed that transit was largely limited to serving two markets: transit- dependent
individuals and commuters traveling to and from jobs in the central business districts in the
nation’s largest cities. 5
Transit- dependent individuals are defined as individuals who for reasons of age, income, or
disability, lack either access to or the ability to use an automobile and thus rely on public
transit as a primary means of transportation. Researchers have typically measured transit
dependency using variables such as household income, age, race, ethnicity, immigrant status,
and number of automobiles in the household. National transportation surveys ( such as those
conducted in 1983, 1990, 1995, and 2001) regularly report that individuals who fall into
certain demographic group categories ( defined using these variables) are disproportionately
transit users. Using data from the 2001 National Household Travel Survey, Pucher and Renne
found that the poor, blacks, Hispanics, and those with low levels of vehicle ownership are more
likely to use transit than are other groups. Particularly important is the latter variable. 6 The
same survey found, however, that the numbers of individuals placed into the demographic
categories we use to define transit dependency declined between the 1995 and 2001 surveys.
The surveys also reported that even for transit dependent groups, transit is not their primary
mode of transportation— the automobile is.
During the mid and late 1990s, a series of articles appeared documenting a large decline in
transit ridership during the early part of the decade and speculated that public transit was
headed for rough times. However, in the late 1990s and on to the present, ridership ( measured
in terms of unlinked passenger trips, but not mode share) increased. Pucher identified the
economic recession of the early 1990s, and particularly its effect on employment in New York,
as the driving force behind the ridership decline of the early 1990s. 7 He cites the economic
recovery of the 1990s, rising gasoline prices, stable fares, improved service quality, and the
expansion of rail transit services as among the key contributing factors for the ridership
rebound of the latter part of the decade. The limitation of this article is that it is purely
descriptive; Pucher makes no effort to examine other potential causes using more sophisticated
multivariate techniques.
Thompson and his coauthors examine the ridership trend in the nation’s largest cities.
Focusing on the period between 1990 and 2000 in all metropolitan statistical areas that had
more than 500,000 persons, they paint a picture of ridership that grew faster than population
growth in areas that most researchers would not suspect, namely in the metropolitan areas of
the auto- oriented west. 8 They note that service grew in most parts of the country as well. They
also find that service productivity ( measured in terms of load factor, or the ratio of passenger
miles to vehicle miles) declined throughout the country, but experienced the smallest decline
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What We Do Know About the Factors Associated with ( Rail) Transit Success or Failure 17
in the West. In short, western cities added a lot of service and gained a lot of riders in doing so.
However, this purely descriptive piece does not explain why transit is growing in many
“ surprising” places.
The External Factors that Influence Transit Ridership
The external factors that influence transit ridership include: urban structure ( decentralization),
local land use patterns, automobile ownership levels and costs, and regional economic
conditions.
Urban Structure ( Decentralization)
Meyer, Kain and Wohl, Jones, and Vuchic cite urban decentralization as one of the primary
causes of the long- term decline in transit use in the postwar period. 9 The corollary is that
transit use is positively tied to the degree of urban centralization, and in particular, the
strength of the central business district ( CBD) as a locus of economic activity. Mierzejewski
and Ball found some support for this notion, where choice riders ( those who have access to an
automobile but choose to use transit) are concerned. 10 In a survey of 4,000 persons in 17
metropolitan areas, they found that 82% of choice riders who used transit worked in the
central city.
The conventional wisdom is that transit works best when it focuses on serving the CBD
commute market. 11 The implication is that transit agencies should structure their service to
feed the CBD and provide high quality service to that destination, because that, the literature
would suggest, is where riders wish to travel. An agency decision to serve other destinations,
particularly those dispersed throughout the suburbs, is criticized for being an inefficient use of
public subsidy12 and for resulting in low service productivity. 13
There have been a handful of studies that have examined the link between urban structure and
transit ridership using statistical techniques. Some studies have found a close link between
decentralization and transit ridership while others have found a more complicated set of
relationships between these variables. Most studies have used the relative strength of the
central business district as the measure of urban structure.
Henderson examined the relationship between transit commute mode share and the number of
jobs in the central business district in 1970 and 1980 for 25 large metropolitan areas using a
series of multivariate models. 14 The first multivariate model estimated ridership in 1970 as a
function of CBD employment in 1970 ( R square = .96), the second model estimated ridership
in 1980 as a function of CBD employment in 1980 ( R square = .90), and the third model
estimated ridership in 1970 as a function of both CBD employment and the total number of
workers in the metropolitan area ( R square = .98). He then estimated two change models, one
with a dummy variable for Sunbelt cities ( R square =. 77) and one without ( R square = .66).
Finally, he estimated a change model with dummy variables for both Sunbelt cities and those
with fixed rail systems ( R square = .81).
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Hendrickson found strong relationships between CBD employment and transit commute
mode share. 15 He found positive, statistically significant effects on transit commute mode
share from the Sunbelt dummy variable, and negative, statistically significant effects from the
fixed- rail dummy variable. However, his study suffers from two shortcomings, which include:
1) lack of control variables and 2) mixing of cities with significant differences in both the size
of the CBD and the transit commute mode share. Particularly problematic is the inclusion of
New York, which dwarfs the other cities on both variables, in the same analysis.
Gomez- Ibanez conducted a more sophisticated analysis of the relationship between transit
ridership and decentralization in Boston. 16 He used a time series approach that examined
ridership between 1970 and 1990, and included variables that controlled for fare, per capita
income, and service level. His measure of decentralization was the number of jobs in the city of
Boston. He found: 1) a 1% decline in the percent of jobs in the city of Boston was associated
with between a 1.24% and 1.75% decline in ridership; 2) a 1% increase in real per- capita
incomes was associated with a 0.71% decline in ridership; 3) a 1% increase in fares was
associated with a .22% to .23% decline in ridership; and 4) a 1% increase in vehicle miles of
service was associated with a .30 to .36% increase in ridership. His models accounted for
nearly 90% of the variation in transit ridership from 1970– 1990.
Gomez- Ibanez concluded that transit ridership in Boston has been strongly influenced by the
decentralization of employment. However, the definition of employment is problematic and
measures jobs throughout the city of Boston as opposed to jobs inside the central business
districts of Boston and Cambridge, which the author states he had hoped to measure.
Two recent statistical studies have found very different results. Brown and Neog examined the
relationship between transit ridership and urban structure in all U. S. metropolitan statistical
areas with more than 500,000 persons in 1990 and 2000.17 They define urban structure as the
percent of metropolitan statistical area ( MSA) employment in the CBD and use two measures
of transit ridership, passenger kilometers per capita and transit commute mode share. The
authors controlled for variables measuring fare, service frequency, service coverage, motor fuel
price, urban area population density, regional unemployment rate, and the percent of
households in each metropolitan area that lacked access to an automobile. They found no
statistically significant links between the percent of MSA employment in the CBD and transit
ridership. The authors found the strongest links between two service variables ( service
frequency and service coverage) and transit ridership. They also found a strong relationship
between the percent of MSA households that do not own an automobile and transit ridership.
Brown and Thompson examined the relationship between transit ridership and urban
decentralization in Atlanta from 1978 to 2003.18 The authors used linked passenger trips as
their ridership variable. They created three employment variables to measure the degree of
employment decentralization: percent of employment in the CBD, percent of employment
outside the CBD but inside the transit service area, and percent of employment outside the
transit service area. They controlled for fare, service level, motor fuel price, and population
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decentralization in their time- series analysis. They also included a variable measuring the
percent of transit service delivered by rail transit.
They found that transit ridership is strongly and positively linked to the strength of
employment inside the transit agency service area ( outside the CBD) and is strongly and
negatively linked to the strength of employment beyond the transit agency service area. The
authors found no association between the strength of the CBD and transit ridership in Atlanta.
The authors also noted that transit ridership is more strongly linked to the decentralization of
employment than to the decentralization of population, and that fare levels and the absolute
amount of transit service are also associated with transit ridership. The authors infer that the
Metropolitan Atlanta Rapid Transit Authority ( MARTA) is successfully connecting transit
patrons to dispersed employment locations.
Local Land Use Patterns ( Transit- Oriented Development)
Over the past two decades, there has been a great deal of interest in the relationship between
local land use patterns near bus and rail transit lines, stops, and stations and transit ridership.
Often lumped under the label of transit- oriented development ( TOD), this body of literature
hypothesizes that the density, land use mix, and urban design characteristics of a
neighborhood can influence individual mode choice decisions. 19There is an extensive literature
on the subject, much of which builds on work by Robert Cervero.
The primary hypotheses about transit- oriented development and its relationship to ridership
are voiced in books by the team of Bernick and Cervero, and Cervero on his own. 20 Both books
rely on case study analysis to argue that developments characterized by higher density, more
mixed uses, and more pedestrian- friendly designs tend to have higher transit ridership.
Therefore, the suggestion is made that if metropolitan areas promote these kinds of
developments they should expect to see auto use decline, while transit use, walking, and
perhaps bicycling increase in importance. Indeed, Parker and co- authors found associations
between transit- oriented development and transit mode share in their case study of
transit- oriented development in California. 21
Lund and Willson, on the other hand, found weak ridership results in their case study of
transit- oriented development along the gold line light rail line in suburban Los Angeles. 22
They surveyed the residents in 37 multi- family buildings located within 1/ 3 mile of rail
stations. Of 1,595 housing units surveyed, they obtained responses from 221 units recording
information about 477 trips. They found few transit- dependent residents in their survey.
Respondents were primarily white, worked in professional occupations, and owned one or
more automobiles. Few residents had low incomes. About 75% of respondents rarely or never
used transit, while 15% regularly used transit. Lund and Wilson noted that respondents were
more frequent transit users after they moved to their current place of residence, but noted that
there might be a self- selection bias at work. Essentially, they found that TOD in this
particular corridor was too expensive to be occupied by transit riders and was instead occupied
by wealthier professionals, who tend not be transit riders. The mismatch between TOD
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residential profiles and transit user profiles is frequently noted by TOD skeptics. Residential
self- selection has also been cited by TOD skeptics who assert that the people who live in
residential TODs are people who were already predisposed to engage in more use of
non- automobile transport modes.
There are, however, a number of quantitative studies that have found a connection between
TOD- associated elements and ridership. These studies have examined the relationship
between transit ridership and distance, density, diversity, and design. Cervero discussed several
studies that examine the ridership characteristics of projects located near rail transit stations. 23
He cites a 1989 San Francisco Bay Area study found that 35 to 40% of residents living near
three Bay Area Rapid Transit District ( BART) stations used public transit. He also cited a
1987 Washington DC study that found that rail and bus transit mode share declines by 0.65%
for every 100- foot increase in distance of a residential site from a rail transit station. The same
1987 study found that ridership was higher at downtown than at suburban work sites and that
ridership declined steadily as distance to the station increased. All these studies essentially
examined the correlation between transit mode share and distance to a rail station. They did
not control for other factors that might influence an individual’s decision to use public transit
( fare, service quality, auto access and cost, or the ease with which travelers could reach their
destinations).
The Institute of Urban and Regional Development reported the descriptive results of
residential studies showing that: 1) workers living near the San Francisco area’s Bay Area
Rapid Transit District ( BART) heavy rail line were six times more likely to use it for commute
trips than the average Bay Area resident; 2) workers living near light rail transit in Silicon
Valley were five times more likely to use transit for commute trips than average area residents;
and 3) people living near transit in Washington DC have high transit mode shares that decline
with increased distance from a transit station. 24 The authors also summarized a set of office
and retail studies that showed: 1) 50% of those working within 1,000 feet of a downtown
Washington Metro station used rail to get to work; 2) 60% of customers at a downtown San
Diego shopping center located two blocks from light rail arrived either by transit or by foot;
and 3) 34% of patrons at a downtown San Francisco shopping center that has a direct
connection to BART arrived by transit.
More studies have focused on the link between density and transit ridership than any other
factor. These studies have their roots in early work by Pushkarev and Zupan. 25 Parsons
Brinckerhoff found, in a study of 17 cities with light rail or commuter rail, that residential
densities had a strong effect on transit boardings. 26 Spillar and Rutherford also documented a
density effect in their analysis of Denver, Portland, Salt Lake City, San Diego, and Seattle. 27
They noted, however, that density appeared to have a stronger relationship with transit
ridership in low- income neighborhoods. The Institute of Urban and Regional Development
also presented a set of multivariate models from studies for the San Francisco Bay Area and
Arlington County, Virginia that indicate particularly strong relationships between the density
of the land use and transit ridership. 28 Overall, the authors concluded that residents living in
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TODs usually patronize transit five to six times as often as the typical resident of a region. The
authors acknowledged that self- selection bias might be an issue in the residential studies they
discuss. Cervero found a modest density effect on ridership ( elasticity between 0.2 and 0.6) in
his study of Montgomery County, Maryland. 29
Kuzmyak and his coauthors also reported that transit ridership tends to be higher at higher
densities. 30 Citing work by Parsons Brinckerhoff for the city of Chicago, they reported that a
10% increase in residential density is correlated with an 11% increase in per- capita transit
trips and a 13% increase in transit mode share. Citing work by Levinson and Kumar for a
national study of the U. S., they reported that density only becomes relevant to mode choice at
densities higher than 7,500 persons per square mile. Citing work by Frank and Pivo in Seattle,
they also noted that transit requires workplace densities of 50– 75 employees per gross acre and
residential densities of 10– 15 dwelling unit per net residential acre to achieve significant
commute mode shifts. Citing a study by Nelson/ Nygaard for Portland, Oregon, they noted
that housing density and employment density accounted for 93% of the variation in daily
transit trip productions and attractions across the region. The authors cautioned that in many
of these studies, self- selection bias may be a concern.
Kuzmyak and his coauthors also presented the results of studies indicating that transit use
tends to be higher in areas characterized by mixed land uses. 31 However, they cautioned that
many of these environments tend to also be characterized by higher densities, so separating the
mixed- use effect from the density effect is difficult. Citing work by Messenger and Ewing in
Florida, they noted that more balanced ( jobs and workers) areas tend to have higher transit
mode share. Citing a study by Cervero of 57 suburban activity centers, the authors noted that
centers with on- site housing had 3 to 5% more transit, bike, and walk trips.
Transit- oriented development is also characterized by more transit and pedestrian- friendly
urban design. Urban design is the hardest of the 3 Ds ( density, diversity, design) to measure,
but there have been a few studies on the effect of urban design on transit ridership. Cervero
found that urban design, and particularly sidewalk provisions and street dimensions,
significantly influence whether someone reaches a rail stop by foot or not in his study in
Montgomery County, Maryland. 32 He asserted that conversion of park- and- ride lots to
transit- oriented developments holds considerable promise for promoting walk- and- ride transit
usage in years to come. Cervero found a relationship between street connectivity and an
individual’s decision to use transit in his study of people living near rail stations in
California. 33
Other External Factors
The literature has also identified a number of other factors beyond the control of agency
managers that can influence transit ridership. These factors include population and population
growth, 34 regional economic conditions, 35housing costs, 36 and personal income. 37
Some particularly important additional external factors relate to the automobile. Studies by
Brown and Neog, Liu, and Taylor and Miller have all highlighted the important relationship
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between the share of carless households in a metropolitan area and transit ridership. 38 Studies
by Dueker and his coauthors and Mierzejewski and Ball have noted the important role played
by parking availability and cost in influencing transit use. 39
The Internal Factors that Influence Transit Ridership
The internal ( agency- controlled) factors that influence transit ridership include: fare policy,
service frequency, service coverage, service orientation, and targeted marketing efforts.
General Discussion
There is a sizeable descriptive literature that introduces service strategies that might influence
transit ridership in particular settings— without evaluating the performance of the particular
strategy. One author who has conducted significant past research in this area is Robert
Cervero. Cervero identified timed transfer systems, paratransit services, reverse commute and
specialized runs, employer- sponsored van pools, and high- occupancy- vehicle and dedicated
busway facilities as transit service strategies that might result in higher ridership in
decentralized areas. 40 He reemphasized these kinds of service strategies in his international
case study of transit metropolises. Working with Beutler he discussed the use of bus rapid
transit services and free market paratransit services as possible service strategies in certain
urban environments. 41
Using case studies of eight transit agencies in the United States and Canada, Charles River
Associates identify feeder bus, fare integration, Express bus, times transfer, pass programs with
universities, and a fareless square as promising strategies in certain environments. 42 However,
these same authors conclude that policies that make private vehicle use less attractive will have
a larger positive effect on ridership than policies that make transit more attractive.
A number of authors emphasize the role of targeted marketing and market segmentation as
strategies to increase ridership among specific rider groups. 43 Cambridge Systematics uses
repeated surveys of agencies that experienced ridership increases to identify fare policies,
service adjustments, and marketing efforts as key factors that affect transit ridership. 44 Miller
and his coauthors champion the use of service integration, including infrastructure, fare
payment, and/ or special events/ emergency service integration, as positive service strategies. 45
Haas discusses the use of Eco pass programs, guaranteed ride home programs, day passes, and
online fare media sales programs. 46 Rosenbloom and Fielding identify targeted use of reverse
commute services, services to large employers ( including universities), vanpool incentives,
route restructuring, and feeder services as key service strategies. 47
Skinner found, however, that transit services targeted toward particular ridership markets
might have unexpected negative effects. 48 Miami- Dade Transit operates a number of routes
that seek to serve the elderly population, and connect social service and other destinations to
residential areas where the elderly reside. However, these routes have low elderly and
non- elderly ridership, and as a result, very poor performance, because they are slow and
indirect.
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Project for Public Spaces discusses the role of amenities, including the use of low- floor buses in
Ann Arbor, commuter buses in Aspen, transit shelters in Portland, and Rochester, and historic
streetcars in San Francisco. 49 The report includes some data on cost and ridership for each of
the case studies. There is no discussion of other factors that might explain the ridership
increases documented for the case studies nor is data collected that would enable a reader to do
so.
Finally, the California Department of Transportation uses a survey of actual and potential
riders to identify service reliability, convenience, comfort, and safety as key factors that might
influence an individual’s decision to ride transit. 50As noted above, none of these articles
evaluates the performance of the strategy or factor that the authors describe.
Fare Policy
There is an extensive body of literature that documents the relationship between fare levels
and ridership. 51 Kyte found an important relationship between fare and ridership in his study
of Portland. 52 Taylor and his coauthors documented the importance of fare policy in their U. S.
national study, 53 and so did Kohn in his Canadian study. 54 Kain and Liu noted the importance
of fares in their study of Houston and San Diego, 55 as did McLeod, et al. in their time- series
analysis of Honolulu. 56
TRL Limited summarizes the results of an extensive set of empirical studies. 57 They report
that fare elasticities vary depending on both mode and timeframe. Bus fare elasticities average
around - 0.4 in the short run, - 0.56 in the medium run, and - 1.0 in the long run. Rail transit
elasticities tend to be higher than those for bus for suburban rail services and smaller than
those for bus for heavy rail. Off- peak ridership tends to be twice as responsive to fare changes
as peak period ridership.
McCollom and Pratt provide a similar review of empirical work. 58 For bus transit, the authors
report elasticities at around - 0.4 and for rail transit they report elasticities at around - 0.18.
They found that riders are more sensitive to off- peak fares than to peak period fares, and that
elasticities decrease as the size of the city increases.
Service Frequency and Coverage
There is also a large group of literature that documents the relationship between the service
provided by an agency and transit ridership. 59 A smaller number of literature has broken down
service into two components: frequency and coverage. Both are hypothesized to positively
influence ridership. Brown and Neog, and Thompson and Brown60 found positive effects of
both service frequency and service coverage in their national analyses of transit ridership in
large U. S. metropolitan areas in 1990 and 2000. Brown and Neog report elasticities for both
service and coverage in the 0.7 to 1.0 range. 61
Evans provides an overview of empirical work on the relationship between transit service
frequency and ridership. 62 He found that ridership does respond to service frequency and
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schedule changes ( elasticity = 0.5), and that the largest responses are found in higher income
areas that previously had very infrequent service. In more traditional transit areas, the
ridership response was more modest.
Pratt and Evans examined the relationship between coverage and ridership in a routing
study. 63 The authors found elasticities in the range of 0.6 to 1.0. The authors noted that the
largest ridership increases occurred when the system emphasized “ high service level core
routes, consistency in scheduling, enhancement of direct travel and ease of transferring.” 64 The
authors claim that new and expanded systems of the hub- and- spoke variety produced slightly
higher ridership than grid systems, although there were no controls for other possible
variables. 65 Taylor, et al. also noted that route coverage was an import influence on transit
ridership. 66
Service Orientation
A particular interest in this project is the role of service orientation as a factor influencing
transit ridership. Regrettably, there have been few studies that explicitly examine service
orientation. Thompson and Matoff conducted an early case study analysis of nine cities in
which they distinguished between radial and multidestination ( grid) oriented transit
systems. 67 The authors obtained data on transit system profiles and transit performance from
1983 to 1998 for transit systems in Cleveland, Columbus, Houston, Minneapolis, Pittsburgh,
Portland, Sacramento, San Diego, and Seattle. The performance measures include: cost per
passenger mile, peak- to- base ratio, passenger miles per capita, and vehicle miles per capita.
The authors then compared systems that met their definitions of multidestination versus
radial service orientations on each of these measures. The authors found that multidestination
systems were more effective ( that is, had higher ridership), nearly as efficient ( about the same
cost), and more equitable ( lower peak- to- base ratio) than radial systems.
More recently, Thompson and Brown explored the relationship between service orientation
and ridership using a statistical analysis. 68 The same authors have also recently explored the
relationship between service orientation and service productivity. 69 In their ridership study,
identify and examine the key determinants of transit ridership change between 1990 and 2000
in U. S. MSAs with more than 500,000 persons. Among the key variables they examine is a
service orientation that distinguishes between multidestination and traditional service
orientations. The authors found that transit is growing most rapidly in the non- traditional
markets of the West but that much of the regional variation is a function of the particular
service coverage, frequency, and orientation decisions made by transit agencies in this region.
Service coverage and frequency are the most powerful explanatory variables for variation in
ridership change among MSAs with 1 million to 5 million people, while a multidestination
service orientation is the most important explanation for variation in ridership change among
MSAs with 500,000 to 1 million people. A weakness of the analysis is the definition of the
service orientation variable as a binary variable, as opposed to a continuous one.
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Their productivity paper substitutes a quantitative variable that measures the percent of
transit routes that do not serve the CBD. 70They find that decentralized service orientation
does not lead to diminished productivity. In fact, the signs on the coefficient for this variable
in their statistical models are positive, although not statistically significant.
LITERATURE ON RAIL TRANSIT RIDERSHIP IN PARTICULAR
The literature review discussion proceeds as follows: 1) works that provide a descriptive
overview of rail ridership; 2) works that emphasize external factors that affect rail ridership;
and 3) works that emphasize internal factors that affect rail ridership. Many of the sources
discussed in the section on transit ridership in general also have important insights to provide
to rail transit, but the works discussed in the next few pages are focused solely on rail transit.
Descriptive Overview of Rail Ridership
Rail transit investments have been both applauded, particularly by advocates of
transit- oriented development, and criticized, particularly by economists. On the pro- rail side,
advocates like Litman have argued that cities with large, well established rail systems have
significantly higher per capita transit ridership, lower average per capita vehicle ownership
and annual mileage, less traffic congestion, lower traffic death rates, lower consumer
expenditures on transportation, and higher transit service cost recovery than otherwise
comparable cities with less or no rail transit service. 71 Litman suggests this indicates that rail
transit systems provide economic, social and environmental benefits, and he insists that these
benefits tend to increase as a system expands and matures.
Polzin and Page found increasing transit ridership for 24 light rail transit systems constructed
between 1980 and 2001.72 The authors found that ridership trends for the rail projects, in the
authors’ words, “ matured quickly.” Ridership increases tended to be substantial in the
immediate aftermath of system opening and then became relatively stable. They attribute
subsequent growth in ridership to changes in system extent and service frequently. Despite the
positive effects of the light rail transit ( LRT) lines on overall transit ridership, the authors note
that transit continues to play a modest role in overall metropolitan travel. Nevertheless, the
authors believe the LRT investments may be important in stimulating community attention
and further investment in transit in the metropolitan area. One caution in their work is the use
of unlinked passenger trips as their ridership measure. Unlinked passenger trips are influenced
by the number of transfers, which tend to be higher in systems with rail transit.
There are, however, rail transit critics who have singled out the high costs and/ or low ridership
results of many rail projects. O’Toole paints portraits of a series of great rail transit disasters. 73
Clearly no fan of rail transit, he found that transit ridership is falling in 13 of the 23
metropolitan areas that implemented rail between 1982 and 2003, is increasing slower after
rail construction than before it in four metropolitan areas, is increasing but slower than the
growth in vehicle travel in three metropolitan areas, is growing just as fast as auto use in one
metropolitan area, and is growing faster than auto use in two metropolitan areas ( Boston and
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San Diego). The author then examined four metropolitan areas that have bus- only transit
where transit ridership is growing faster than auto use ( Austin, Charlotte, Las Vegas,
Louisville, and Raleigh- Durham), as cases of transit success. O’Toole’s central argument is that
metropolitan areas that have invested in rail transit have wasted their citizens’ money. He
contends that the investment has often resulted in less transit ridership because agencies have
frequently responded to rail cost overruns by raising fares and/ or cutting bus service.
Moore made similar complaints about the Blue Line light rail transit line in Los Angeles,
although ridership on the line today is very strong. 74 Richmond echoes these arguments while
also criticizing the motivations of planners and public officials who have made the choice to
invest in rail transit. 75
Pickrell has criticized rail transit planners for their roles in these disputes. 76 He compared the
forecast and actual ridership and forecast and actual capital costs for eight rail transit projects
( four light rail and four heavy rail) in eight cities ( Atlanta, Baltimore, Buffalo, Miami,
Portland, Sacramento, Washington) in an attempt to verify the accuracy of the forecasts and,
when forecasts were inaccurate, to identify the reasons for the inaccuracies. He found that
planners consistently overestimated ridership and underestimated costs for these rail projects.
He also determined that the errors are not associated with flawed assumptions about key
variables like population and downtown employment ( which turned out be fairly accurate) nor
are they the result of changes in the design of the projects. Instead, he attributes these
overoptimistic forecasts to the structure of the federal transit grant programs.
Several authors have developed single or comparative case studies of transit ridership in cities
with rail transit systems. Tennyson’s discussion of postwar transit ridership trends in Saint
Louis emphasizes the role of rail transit in positively affecting overall agency performance. 77
He notes that light rail service began as part of an effort to restore the viability of transit
service in the metropolitan area. He points out that the results were “ immediate and
positive;” 78 transit ridership increased 40% and the cost of providing service stabilized after a
period of continued increases.
Allen and Hufstedler provide a comparative case study of Dallas ( a bus- and- rail city) with
Houston ( at the time a bus- only city) between 1985 and 2003.79 The authors found that both
systems experienced increased ridership over the period. The two systems have experienced
similar ridership peaks and valleys. The authors report that Dallas’s light rail system expansion
resulted in overall transit ridership increases, despite some decline in bus transit ridership.
Houston’s heavy commitment to its all- bus system has resulted in both higher service and
ridership levels than Dallas Area Rapid Transit ( DART), although the two systems have
comparable populations. In general, the authors conclude that light rail transit in Dallas has
had a positive effect on transit ridership. The paper is purely descriptive and does not attempt
to identify causes for the findings.
Schumann80 provides a comparison of Columbus and Sacramento80 in 1985 and 2002. These
two state capitals pursued different transit paths during this period; Columbus remained an
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What We Do Know About the Factors Associated with ( Rail) Transit Success or Failure 27
all- bus system, while Sacramento opened a light rail transit system. In 1985, the transit
system in Columbus ( Central Ohio Transit Authority, or COTA) outperformed the system in
Sacramento Regional Transit ( RT), but by 2002, the roles had reversed. In the intervening
period, Sacramento had successfully opened a light rail transit system and then restructured its
bus system to provide riders with the ability to reach a wider array of destinations. Columbus
failed to build light rail and instead retained an all- bus system. The author notes that different
levels of local financial support explain both Sacramento’s ability to develop light rail and
Columbus’s failure to do so. 81
Schumann states that “ in Sacramento, willing political leadership took advantage of a
one- time opportunity for federal funding to build an LRT starter line, that adding LRT made
transit more visible and effective, encouraging voter approval of additional local operating and
capital funding, and that all of this resulted in a synergy that attracted more riders to the total
LRT and bus system, and led to extension of the rail system to a third corridor in 2003.
Although planning for light rail transit also started in Columbus during these same years, a
serious interruption in the flow of local funds hampered transit development, requiring cuts in
bus service and preventing development of that region’s LRT line which, had it been built,
could have enhanced transit’s attractiveness.” 82
Statistical Studies of External Influences on Rail Ridership
There have been a few statistical studies that have examined rail transit performance by
focusing almost entirely on the external factors that influence rail ridership. Baum- Snow and
Kahn evaluate whether rail transit improvements made between 1970 and 2000 in sixteen
metropolitan areas led to new transit ridership. 83 They define transit ridership using the
journey- to- work mode shares. The authors estimate multivariate models ( for each of sixteen
metropolitan areas) that predict transit mode share ( at the census tract level) as a function of
distance to the central business district ( CBD) and distance to the nearest rail line. The authors
do not control for any other socioeconomic factors.
Baum- Snow and Kahn found decreasing marginal returns of new rail investments for all cities
but Portland and Atlanta. 84 Interestingly, they note that a network effects argument, wherein
later infrastructure connects riders to a broader array of possible destinations, might explain
these two exceptions. The authors also find large potential commute time savings associated
with the rail investments but observe little to no effect on pollution and congestion
externalities.
Chung examined the effects of employment, CBD office occupancy rates, and parking on rail
transit ridership in Chicago when controlling for fare. 85 He found all three variables to be
statistically significant. The ordinary least squares regression model had an R- squared of 0.90,
indicating that variation in these explanatory variables accounted for 90% of the variability in
rail transit ridership over the 1976 to 1995 study period.
28 What We Do Know About the Factors Associated with ( Rail) Transit Success or Failure
Mineta Transportation Institute
Statistical Studies of Internal Influences on Rail Ridership
There have also been a few studies that have considered both external and internal factors that
influence either actual or potential rail transit ridership. Abundo examined commuter rail
ridership in Boston from 1980 to 1997.86 She found that approximately 80% of the recent
ridership growth was due to fare policies and service improvements and 20% was due to
factors outside the agency’s control.
Two statistical studies examine the role of service orientation ( and in particular market focus)
in the rail transit context. Hadj- Chikh and Thompson examined traffic patterns on the
Tri- Rail commuter rail system in south Florida. 87 The station siting process led to the
construction of some stations that seemed well- suited to serving suburban transit markets as
opposed to the central business district- bound market. The authors compare the degree to
which people are using the service to reach suburban destinations versus the central business
district. They gathered ridership data from Tri- Rail staff. These data provided information on
ridership between all pairs of stations ( from automated ticket machines) for one work week
during a twelve- hour period ( 4 a. m. to 4 p. m.). They then classified station pairs as serving the
suburb- to- suburb or suburb- to- CBD market. They made comparisons between the two
markets for six distance categories.
The authors find that both markets have comparable total potential ridership. They identify
potential ridership all along the Tri- Rail corridor, not just where the CBD is the destination.
They also found that Tri- Rail penetrates the suburb- to- CBD market about twice as much as
the average suburb- to- suburb market. The authors also noted that market penetration
increased with distance, although the model left a considerable amount of unexplained
variation in the dependent variable.
The authors use the results to highlight the existence of sizeable suburb- to- suburb demand for
commuter rail service. They observe that commuter rail planners who are developing their
systems to serve CBD markets might be able to tap this potential market at very little
additional cost. 88
Whately, Friel, and Thompson conducted a similar analysis in Southern California and found
that the ridership potential for the average suburb- to- suburb station pair is three times greater
than for suburb- to- CBD. 89 They observed that most of the suburb- to- suburb potential is
found in the shorter trip distance categories ( under 20 miles), that the market potentials are
about even for trips between 21 and 30 miles, and that the market potential for
suburb- to- CBD is greater in the 31- plus mile trip distance category. In addition, they found
that market penetration is negligible for suburb- to- suburb trips in the shorter distance
categories but larger in the longer distance categories. In general, as distance increases, so does
market penetration. They conclude by emphasizing the significant market potential for
suburb- to- suburb trips. Whaley et al. suggest that more frequent service and fare structures
oriented to short distance riders might be strategies to tap these markets. They also note that
rail lines should continue to serve traditional CBDs and attempt to serve nearby suburban
employment clusters as well.
Mineta Transportation Institute
What We Do Know About the Factors Associated with ( Rail) Transit Success or Failure 29
LESSONS FROM THE LITERATURE
The literature review suggests that an array of factors, both outside and under the control of
transit managers, is associated with ( rail) transit success. The literature indicates that the key
factors outside the control of transit managers ( external factors) are urban structure, local land
use patterns, population and population growth, regional economic conditions, and last, but
certainly not least, automobile- related variables, including levels of automobile ownership,
parking availability and cost, and motor fuel price. The most consistently strong external
factors are urban structure and the automobile ownership and price variables. The relationship
between urban structure ( decentralization) and ridership appears to be a particularly complex
one, given recent insights by Brown and Thompson, and Brown and Neog. 90 Past studies have
indicated a close relationship between the strength of the CBD as a locus of economic activity
and transit ridership, but these recent studies indicate that CBD employment is not as
important as non- CBD employment that is accessible by transit. This insight has obvious
relevance for the way transit agencies structure their route systems. The automobile variables
are also among the key determinants of transit ridership. The literature shows that an
individual’s decision to ride or not ride transit is strongly influenced by whether or not the
individual has access to an automobile. The literature review suggests that our examination
should attempt to control for the influence of these key external factors on the level of transit
ridership in the metropolitan areas we study.
The literature review illustrates that the key factors under the control of transit managers are
fare policy and service planning decisions, including service coverage, service frequency, and
service orientation. 91 The literature suggests that all these factors are important influences on
the level of transit ridership, with service frequency and coverage cited as being more
influential than fare policy. The time individuals spend waiting for a vehicle is often cited as
being viewed as particularly onerous by riders and better service frequency means riders do not
have to wait long for the next bus or rail vehicle. Better service coverage provides individuals
with access to more origins and destinations, thus making transit a viable travel option for a
wider array of trips. Combined, better frequency and coverage enhance transit’s relative
attractiveness vis- à- vis the automobile.
Service orientation also appears to be quite important. The few studies that have investigated
the influence of service orientation on actual ( or potential) ridership or service productivity
have found that networks that offer travelers access to a dispersed array of destinations perform
better than networks oriented to serving CBD- bound commuters. 92 Our examination focuses
on the role of service planning decisions in determining transit success or failure, and hence
this literature citing the importance of service coverage, frequency, and, especially, orientation
offers particularly important insights to our investigation.
A critical gap in this service- focused literature, which we hope to fill with this study, is the
interrelationship between bus and rail service. The articles by Allen and Hufstedler, and
Brown and Thompson offer anecdotal evidence indicating the importance of this
interrelationship for increased transit ridership in Atlanta and Dallas, but it has yet to be
30 What We Do Know About the Factors Associated with ( Rail) Transit Success or Failure
Mineta Transportation Institute
examined in any meaningful way— either statistically or through qualitative case studies. 93
This study offers the first attempt to examine this relationship, which we believe explains why
some cities succeed and others fail in their efforts to leverage rail transit investment to increase
transit ridership.
Mineta Transportation Institute
31
RESEARCH METHODOLOGY
This investigation of the influence of planning decisions on rail transit success or failure
required the use of a combination of qualitative and quantitative methods. The authors began
their investigation by developing a timeline of transit planning and system development in
each of our eleven study areas. They constructed these timelines by examining planning
documents, newspaper and journal articles, and secondary literature in each study area. At the
same time, they queried the National Transit Database ( NTD) to develop descriptive statistics
on transit ridership, service, and service productivity for each transit agency in our eleven
study areas. The authors then examined those statistics to identify ridership, service, and
performance trends, which we then related to events contained in each of our study area
timelines.
These initial quantitative and qualitative investigations informed the development of an
interview guide for their telephone interviews with key informants in each of the study areas.
The authors obtained a list of key informants largely by querying contacts developed in earlier
research and through professional relationships. Since 2002, Thompson had been interviewing
participants in the development of the light rail transit movement in North America.
Interviewees included those who planned the first national light rail conference, jointly
sponsored by the Transportation Research Board, the Urban Mass Transit Administration, and
the American Public Transit Association and held in Philadelphia in June 1975. Interviewees
also included those involved in decision- making that led to the decision to build light rail
transit lines in Edmonton, Calgary, San Diego, Portland, Sacramento, and San José. By the
time this research began he had transcripts of 47 interviews. Many interviewees helped
develop the list of key informants for this study. Thompson also chairs the research committee
for the Light Rail Transit Committee of the Transportation Research Board, and that position
led to the identification of additional key informants.
The authors asked study informants about the development of the transit system, its purpose,
and its performance. They also asked these informants to provide us with the names of contacts
inside the metropolitan planning organization ( MPO) and/ or transit agency from whom they
could obtain detailed population and employment data, transit service and ridership data,
on- board passenger survey data about rider demographics and transfer activity, and other
statistical information that allowed them to develop a detailed portrait of the functions and
performance of specific types of transit services and their relationship to the changing urban
structure of each of the study areas.
The analysis of these data served as the fourth phase of the project. The combination of these
analyses allowed the authors to develop the planning and policy recommendations contained
in the body of the report, as well as the more detailed individual case studies contained in the
appendices. Each phase of the research project is discussed in more detail below.
32 Research Methodology
Mineta Transportation Institute
DEVELOPMENT OF TRANSIT PLANNING AND SYSTEM DEVELOPMENT
TIMELINES
The first phase of the research project involved the development of timelines of transit
planning and system development in each of the study areas. The authors began with
information obtained by the authors in earlier inquiries of transit planning history in Atlanta,
Dallas, Portland, Sacramento, and San Diego. 95 They filled in missing information for these
cities, and developed timelines for other cities, using a combination of: 1) planning documents
prepared by transit agencies, metropolitan planning organizations, consulting firms and other
documents; 2) newspaper accounts in the major newspapers in each study area; 3)
contemporary and historical accounts of events in each study area found in scholarly and
non- scholarly periodicals; 4) unpublished papers prepared for scholarly conferences such as the
Annual Meeting of the Transportation Research Board ( TRB); and 5) secondary source
materials, including histories of urban politics, public transit, and the intersection between
public policy decisions and race relations from both a national perspective and in a few of our
study areas. Because the sources consulted in the development of these timelines are too many
to cite here, the authors instead cite the timelines as the sources for information gathered in
this phase of the project.
The purpose of the historical investigation was to get a sense of the transit planning history in
each of the study areas. Particularly important to the authors was gaining understandings of:
1) the changing nature of the regional vision for transit in each study area; 2) the evolution of
rail, bus, and other transit mode transit system plans; and 3) the roles played by different
interest groups in each stage of transit system development. These understandings helped the
authors to frame the questions we posed in the interview phase of the project.
Descriptive Examination of Metropolitan Transit Performance
The second phase of the research project involved the development of a descriptive portrait of
transit performance in each study area. To develop this portrait, the authors queried the
National Transit Database using the Florida Transit Information System ( FTIS) software
developed by the Florida Department of Transportation. At the start of this phase of the
project, they identified the transit systems in each metropolitan area included in the study.
The authors then obtained unlinked passenger trips, passenger miles, vehicle miles, revenue
miles, and route miles ( on a system- wide and mode basis) for each transit system. They were
able to aggregate these data to develop regional measures of riding habit ( passenger miles per
capita) and load factor ( passenger miles per vehicle mile), which they related to information
contained in the timelines developed in phase one of the project. The authors were also able to
develop system- based and mode- based ridership, service, average trip length, and service
productivity trends for all agencies in all study areas. The timeframe for most of these
descriptive analyses is 1984 to 2004. In the case of MARTA in Atlanta, they were able obtain
data back to the agency’s creation in the 1970s. Detailed information about data sources for
Mineta Transportation Institute
Research Methodology 33
this and other phases of the research is contained in the individual case studies in the
appendixes.
Interviews with Key Informants
The third phase of the research project consisted of interviews with key informants. As
interviewees, the authors selected individuals who were able to comment on the evolution of
transit planning in the study area, the roles played by different types of services in facilitating
the vision, the successes and/ or failings of these different services, and the importance of land
use or other non- transit strategies in affecting transit performance. Most interviewees held
responsible positions in the primary transit agency or metropolitan planning organization in
the study area. For most cases, the authors obtained interviews with two informants. they
obtained interviews with three informants for Miami, and with one informant for San Diego
and Salt Lake City. The names of interviewees are listed in the references for the relevant case
study.
The authors used the analysis of qualitative and quantitative data from phases one and two of
the project to develop a generic interview guide, which they then tailored to each
metropolitan area and to each interview, so as to query and interviewee about issues for which
he had some knowledge and/ or expertise. They submitted the questions to our contact prior to
the interview, and ultimately conducted interviews by telephone. The interviews lasted an
average of 90 minutes. One member of the research team took the lead in asking questions,
while the other member of the team listened, took notes, and raised issues that might have
been missed in the course of conversation. The authors cite the interviews as sources of
materials contained in both the main body of the report and the individual cases.
Detailed Case Study Analysis
The fourth phase of the research project involved a detailed examination of information
gathered in the first three phases of the project, plus additional information gathered from
metropolitan planning organizations ( MPO) or transit agencies. From MPOs, the authors
obtained information about regional population and employment patterns which they used to
generate population and employment tables and density maps for the case study analysis.
From transit agencies, they obtained route- based performance statistics, transit passenger
on- board surveys, and rail station boarding and alighting data that allowed them to develop a
finer picture of the types of services that are performing well in each ar