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OTRECat PSU 
Friday Transportation Seminar, 
October 10, 2014 
Examining the Role of Internal Planning Decisions in Improv...
What are Internal Planning Decisions? 
Transit improvement strategies 
focused on adjusting the 
internal performance fact...
Transit Performance Factors 
External Factors 
• 
Characterize the external setting of the environmentserved by transit wh...
Background of the study 
Scholars investigating performance of U.S. transit systems noticed substantial differences in rid...
Higher frequency 
(incl. off-peak periods) 
Scholars have identified several types of 
internal planning decisions that ap...
Two Archetypes of Transit Network Layouts 
Radial 
(CBD-oriented) 
Multi-destination 
(decentralized) 
Multi-destination n...
19842004Change19842004ChangeMultidestination, Bus & Rail101281499%11.39.3-13% Multidestination, Bus only65368-11%8.97.4-15...
Purpose of the Study 
Previous studies have evaluated the 
internal decisions focusing primarily on ridership indicators 
...
Major Research Question 
How are the economic outcomes of transit 
influenced by internal transit planning decisions such ...
Area of Study 
The analysis focused on 
13 U.S. bus & light rail systems. 
All fixed-route services were considered. 
Sele...
Research Design 
 
Evaluation of transit 
economic 
outcomes 
(measured as net benefits) 
 
Examination of the statistic...
Stage I Benefit-Cost Analysis 
 
Economic outcomes of transit systems were evaluated by a 
benefit-cost analysis framewor...
Stage I Benefit-Cost Analysis 
Benefit / Cost Category 
Description,Method of Estimation 
Key Assumptions 
BENEFITS 
Direc...
Benefits were discounted by passenger miles to make the results comparable across cases. 
Net benefits per passenger mile,...
Stage II Factors of Economic Outcomes 
Is there any relationship between performance factors 
and the net benefits generat...
Variable 
Hypothesizedinfluence on benefits 
Description 
Internal factors 
Decentralization 
Ratio 
+ 
% ofservice not en...
Frequency and service density appear to be positively correlated with benefits 
TTI is negatively correlated (transit syst...
Additional Route-Level Analysis 
Stage II Factors of Economic Outcomes 
The effects of internal planning decisions 
were i...
Route-Level Analysis 
Categorization: Non-CBD ServiceCBD ServiceRoutes Serving Rail StationsRoutes Not Serving Rail Statio...
Another category of internal decisions: 
The literature indicates thatspecificdecisions regarding transit management and o...
Additional FocusOwnership and Management Factors 
Two additional variables, reflecting the share of contracting and the de...
Results indicate that contractingand consolidation of regional transit governance are positive influences on transit econo...
Conclusions and Implications 
Two types of internal planning decisions: 
increasing frequency and increasing service densi...
Conclusions and Implications 
Network decentralization seems to have no significant influence on transit benefits and cost...
Future Research 
Similar analysis for other systems 
Perform robust evaluation of internal and external factors at route-o...
Questions? Comments? 
Michal Jaroszynski 
mjaroszynski@fsu.edu 
http://myweb.fsu.edu/maj09e/ 
Oregon Transportation Resear...
Liberal scenarioConservative scenarioAverage change of the benefits (or costs) after adopting liberal or conservative assu...
Direct Revenue per Pass. Mile20012002200320042005200620072008200920102011 Buffalo 0.38$ 0.36$ 0.34$ 0.39$ 0.36$ 0.36$ 0.35...
Consumer Surplus per Pass. Mile20012002200320042005200620072008200920102011Buffalo0.29$ 0.28$ 0.27$ 0.31$ 0.29$ 0.28$ 0.27...
decheadwayrevmpe~mpop_densunemplveh_hhmed_incgastticontractregionalDecentralizationdec1.00Headwayheadway0.351.00Service De...
Decentralization ratio (Percentage of Total Service Volume Allocated to Routes Not Serving the CBD) 2001200220032004200520...
Selected References 
Brown, J. R. and G. L. Thompson (2008a). Examining the Influence of Multidestination Service Orientat...
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Examining the role of internal planning decisions in improving transit performance and economic outcomes

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Michal Jaroszynski, Ph.D. Candidate, Department of Urban and Regional Planning, Florida State University

Scholars and practitioners continuously seek best practices to increase transit ridership, efficiency, and modal share. The ongoing suburbanization and decentralization of U.S. metropolitan regions brings new challenges for accomplishing these goals. Investigating possible strategies for improving transit outcomes in the existing socioeconomic setting, scholars from the Florida State University have pointed to the role of internal performance factors. In a series of research studies, they have found that improving transit service characteristics, such as frequency, connectivity, regional coverage, intermodal integration, as well as decentralization of network structures, could result in increased transit ridership and productivity. These positive effects could be observed even in auto-oriented, low-density environments.

This presentation will briefly summarize the previous findings regarding the role of internal factors in improving transit performance and elaborate on the most recent study, which has attempted to assess the economic effects of implementing planning strategies based on adjusting the internal factors. While the previous research utilized average ridership and average vehicle load as primary measures of transit outcomes, this study evaluates the benefits and costs of adjusting specific internal factors, with the objective of determining whether the additional costs of improved service could be balanced with increased revenues and social benefits. The study focuses on 13 U.S. bus and light rail systems during the 2001 – 2011 period. The results indicate that higher service frequency and service density are positively correlated with the amount of net benefits generated by bus and light rail systems. Simultaneously, the degree of network decentralization appears to have no significant influence on the benefits and costs.

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Examining the role of internal planning decisions in improving transit performance and economic outcomes

  1. 1. OTRECat PSU Friday Transportation Seminar, October 10, 2014 Examining the Role of Internal Planning Decisions in Improving Transit Performance and Economic Outcomes Michal Jaroszynski, Ph.D. Candidate Dept. of Urban & Regional Planning Florida State University, Tallahassee
  2. 2. What are Internal Planning Decisions? Transit improvement strategies focused on adjusting the internal performance factors
  3. 3. Transit Performance Factors External Factors • Characterize the external setting of the environmentserved by transit which is not fully controlled by transit planners • Population density • Employment density • Urban form • Affordability of other modes (e.g. automobile) • Related to service parameters and other characteristics controlled directly by transit planners and managers • Frequency • Service accessibility • Travel Speed • Network coverage • Network layout Internal Factors Source: Taylor and Fink (2003)
  4. 4. Background of the study Scholars investigating performance of U.S. transit systems noticed substantial differences in ridership patterns. They have also noticed various approaches to internal factorsamong the transit agencies. They have focused on the role of internal planning decisions, trying to determine whether specific decisions result in increased ridership.
  5. 5. Higher frequency (incl. off-peak periods) Scholars have identified several types of internal planning decisions that appear to have significant influence on transit ridership: Improved connectivity: • shorter transfer times • better intermodal integration (bus and rail) Multi-destination network structure Better coverage ofresidentialand employmentlocations
  6. 6. Two Archetypes of Transit Network Layouts Radial (CBD-oriented) Multi-destination (decentralized) Multi-destination network layout appears to be better adjusted to the current spatial distribution of population & employment.
  7. 7. 19842004Change19842004ChangeMultidestination, Bus & Rail101281499%11.39.3-13% Multidestination, Bus only65368-11%8.97.4-15% Radial, Bus & Rail98281-12%9.87.2-25% Radial, Bus only203729-13%8.65.9-26% System category# of casesMedian Riding Habit (Pass miles per capita) Median Productivity (Pass miles per revenue miles) Excerpt from the Literature: Selected results of the studies evaluating the role of internal decisions Thompson & Matoff, 2003 (9 systems) Brown & Thompson, 2008 (45 systems)
  8. 8. Purpose of the Study Previous studies have evaluated the internal decisions focusing primarily on ridership indicators (boardings per mile, trips per capita, average vehicle load, etc.) However, these studies have notdetermined full economic outcomes (benefits & costs) of adjusting the internal factors. Ridership goes up… But maybe these strategies are too costly to implement?
  9. 9. Major Research Question How are the economic outcomes of transit influenced by internal transit planning decisions such as increasing frequency, expanding network coverage, and network decentralization?
  10. 10. Area of Study The analysis focused on 13 U.S. bus & light rail systems. All fixed-route services were considered. Selection Criteria: All metro areas with a modern light rail system, except for systems that also include heavy rail Previous research placed much emphasis on evaluating multimodal transit systems: Good performance is essential for systems that include rail mode. Otherwise the rail investment appears to be inefficient and redundant. • Buffalo • Charlotte • Dallas • Denver • Houston • Minneapolis • Phoenix • Pittsburgh • Portland • Sacramento • Salt Lake City • San Diego • St. Louis Period of analysis: 2001-2011
  11. 11. Research Design  Evaluation of transit economic outcomes (measured as net benefits)  Examination of the statistical relationship between performance factors and net benefits Additional analyses will be discussed later.
  12. 12. Stage I Benefit-Cost Analysis  Economic outcomes of transit systems were evaluated by a benefit-cost analysis framework.  The B/C Analysis gives a broader overview of transit economic effects if compared to a simple financial analysis. The non-direct benefits play an important role in assessing transit spending. Net Benefits = direct revenues + non-direct benefits – operating costs – capital costs
  13. 13. Stage I Benefit-Cost Analysis Benefit / Cost Category Description,Method of Estimation Key Assumptions BENEFITS Directrevenue Faresand other transit-related revenues Consumer Surplus (riderbenefits) Difference between riders’ willingness-to- pay for transit and the actual fare CS = ridership x (fare/elasticity) x 0.5 Rail elasticity = -0.3 Buselasticity = -0.4 Externalities Reduction in the costs of negative impacts of motorization: -Environmental impacts -Costs of accident victims recovery -Social costs of traffic congestion For environmental impacts: $0.089/passenger mile For accident recovery: $0.138/passengermile Congestion coststaken from the Urban Mobility Report COSTS Operating Costs Operating costsreported by agencies CapitalCosts Annualizedcosts of capital expansion, estimated considering deprecation and capital lifecycle Lifecycles from 15 to 70 yrs., depending on category. Discount rates: 1.0% to 1.9%
  14. 14. Benefits were discounted by passenger miles to make the results comparable across cases. Net benefits per passenger mile, 2011 dollars 20012002200320042005200620072008200920102011Buffalo-$0.35-$0.52-$0.60-$0.68-$0.68-$0.73-$0.84-$0.79-$0.73-$0.77-$0.75Charlotte-$0.42-$0.43-$0.17-$0.46-$0.42Dallas-$0.11-$0.26-$0.25-$0.27-$0.27-$0.23-$0.41-$0.60-$0.76-$0.77-$0.79Denver$0.02-$0.01-$0.09-$0.09-$0.12-$0.11-$0.11-$0.03-$0.11-$0.19-$0.17Houston-$0.15-$0.24-$0.18-$0.23-$0.27-$0.34-$0.44-$0.52Minneapolis-$0.19-$0.08-$0.07-$0.06-$0.07-$0.12-$0.13-$0.18Phoenix-$0.57-$0.54-$0.44Pittsburgh-$0.03-$0.14-$0.16-$0.29-$0.39-$0.47-$0.39-$0.49-$0.50-$0.62-$0.78Portland$0.16$0.07$0.02$0.03-$0.06-$0.04-$0.02-$0.10-$0.07-$0.14-$0.10Sacramento$0.04$0.09-$0.13-$0.33-$0.44-$0.41-$0.36-$0.35-$0.24-$0.29-$0.33Salt Lake-$0.26-$0.29-$0.18-$0.34-$0.21$0.06-$0.04-$0.03-$0.27-$0.27-$0.38San Diego$0.30$0.32$0.27$0.21$0.17$0.14$0.29$0.27$0.34$0.29$0.49St. Louis-$0.07-$0.03-$0.11-$0.16-$0.18-$0.23-$0.22-$0.21-$0.27-$0.33-$0.34 Benefit-Cost Analysis Results We see substantial differences across our cases. What factors are responsible for these differences?
  15. 15. Stage II Factors of Economic Outcomes Is there any relationship between performance factors and the net benefits generated by a transit system? A panel regression fixed-effects model was designed to examine that relationship. 푌푌푖푖푖푖=훽훽0+훽훽1푋푋1,푖푖푖푖+…+훽훽푘푘푋푋푘푘,푖푖푖푖+…+훿훿2푇푇2+…+훿훿푡푇푇푡+푢푢푖푖푖푖 푌푌−푑푑푑푑푑푑푑푑푑푑푑푑푑푑푑푑푑푑푣푣푣푣푣푣푣푣푣푣푣푣푣푣푣,푋푋1,2,…,푘푘−푖푣푣푣푣푣푣푣푣푣푣푣푣푣푣푣푣푣, 훽훽1,2,…,푘푘−푐푐푐푐푐푐푐푐푐푐푐,푇푇2,…,푡−푡푡푡푡푝푦푦푦, 훿훿2,…,푡−푓푓푓푓푡푡푡푡푒푓푦푦푦푡,푖−표푔푔푔푐푐푐 - Net benefits serve as the dependent variable, - Performance factors are the independent variables
  16. 16. Variable Hypothesizedinfluence on benefits Description Internal factors Decentralization Ratio + % ofservice not entering the CBD. Service Density + Servicevolume per service area AverageHeadway - Average time between departures. External factors Population density + The external factors were added tothe modelto reinforce its explanatory power and control for possible external influences on the economic outcomes. Unemployment - Median Income + Zero-vehicle households + Gas Price + TTI(congestion) + Travel Time Index –the ratio of actual travel time compared to travel time in uncongested conditions Model Specification Stage II Factors of Economic Outcomes
  17. 17. Frequency and service density appear to be positively correlated with benefits TTI is negatively correlated (transit systems serving less congested cities yield more benefits) Other variables, incl. decentralization are not significant determinants of the net benefits Model Results Dependent Variable: net benefits per passenger mileVariableCoefficientStd. errortP>[t] Decentralization-0.2960.310-0.950.34Headway-0.0100.005-2.020.05Service Density0.0010.0003.260.00Pop. Density0.2200.2270.970.33Unemployment0.0260.0151.800.08Zero-veh. H-holds-0.0240.026-0.910.37Median Income0.0140.0091.650.10Gas Price 0.1020.1750.580.56TTI (congestion)-2.0450.626-3.270.00Dummy variables2002-0.0670.083-0.810.422003-0.1770.059-3.020.002004-0.2580.058-4.410.002005-0.2900.083-3.480.002006-0.3500.159-2.200.032007-0.4250.164-2.590.012008-0.6910.295-2.340.022009-0.6510.131-4.990.002010-0.7770.246-3.160.002011-0.8000.303-2.640.01Constant1.5070.9601.570.12
  18. 18. Additional Route-Level Analysis Stage II Factors of Economic Outcomes The effects of internal planning decisions were investigated primarily at the system-level. An additional analysis aimed to determine the economic outcomes of specific route categories, important from the perspective of specific internal decisions: - bus routes not serving the CBD - bus routes providing access to rail stations
  19. 19. Route-Level Analysis Categorization: Non-CBD ServiceCBD ServiceRoutes Serving Rail StationsRoutes Not Serving Rail StationsBuffalo3.69$ 3.36$ 3.25$ 9.05$ Denver5.11$ 4.53$ 4.56$ 5.61$ Houston4.41$ 5.26$ 4.97$ 4.57$ Minneapolis3.64$ 3.37$ 3.35$ 4.23$ Phoenix4.07$ 3.26$ 3.67$ 3.92$ Portland2.75$ 2.74$ 2.70$ 5.81$ Sacramento *1.32$ 1.35$ 1.33$ 1.73$ San Diego2.45$ 2.58$ 2.53$ 2.30$ * - Operating Cost per Passenger Mile for SacramentoBuffalo0.260.290.300.11Denver0.260.340.300.28Houston0.150.200.190.13Minneapolis0.240.320.300.24Phoenix0.220.280.240.22Sacramento0.210.220.220.20Farebox RecoveryEnters the CBD?Serves a rail station? Operating Cost per Boarding Economic outcomes of routes running outside the CBD are comparable to the CBD-bound routes. Routes serving a rail station seem to have lower per-rider costs and higher farebox recovery than the remaining services. Year of Analysis: 2011
  20. 20. Another category of internal decisions: The literature indicates thatspecificdecisions regarding transit management and ownership forms, such as: • service contracting • unified regional transit governance are likely to improve the financial sustainability of transit. However, the evidence is still limited: • Previous studies focused on small numbers of cases. • Not much research exists on economic effects of certain governance forms (most studies limited to policy analysis). Ownership and Management
  21. 21. Additional FocusOwnership and Management Factors Two additional variables, reflecting the share of contracting and the degree of regional consolidation, were added to the regression model. Variable Hypothesizedinfluence on net benefits Description Contractingratio + % oftransit service contracted to third- party entities Number of independentgoverning organizations - Number of separate authorities (agencies) possessing transit planning and management functions within a specific metro area
  22. 22. Results indicate that contractingand consolidation of regional transit governance are positive influences on transit economic outcomes. Model Resultswith ownership/governance variables Dependent Variable: ben_per_pm (net benefits per passenger mile) with network planning variableswithout network planning variablesVariableCoeffStd. errortP>[t]CoeffStd. errortP>[t] Contracting Ratio0.7840.243.240.0020.6840.242.790.006# of separate agencies-0.0610.02-2.630.010-0.0880.02-3.820.000Decentralization-0.0880.30-0.300.768Headway-0.0090.00-2.080.040Service Density0.0010.002.850.005Pop. Density0.2980.211.410.1610.3590.221.640.104Unemployment0.0100.020.620.5400.0250.021.600.114Zero-veh. H-holds-0.0150.02-0.630.5300.0140.020.600.550Median Income0.0060.010.730.470-0.0010.01-0.150.878Gas Price 0.1220.160.750.4520.0680.170.410.684TTI (congestion)-1.6190.59-2.770.007-1.3220.57-2.330.022(Dummy time variables not presented in the results) Constant1.1650.891.310.1930.8560.761.120.266
  23. 23. Conclusions and Implications Two types of internal planning decisions: increasing frequency and increasing service density appear to positively influence economic outcomes of bus & light rail transit systems These results correspond with the previous studies, which have identified the positive role of these factors in determining ridership and average vehicle load.
  24. 24. Conclusions and Implications Network decentralization seems to have no significant influence on transit benefits and costs, at least in this case. Future research should investigate more deeply the outcomes of network decentralization. Service contracting and strong regional governance are positive, significant factors of net benefits in the case of the analyzed 13 bus & rail systems.
  25. 25. Future Research Similar analysis for other systems Perform robust evaluation of internal and external factors at route-or stop-level Evaluate transit governance forms, focusing on ridership and economic effects Analyze the effects of internal decisions on riders: travel behavior, accessibility, mobility
  26. 26. Questions? Comments? Michal Jaroszynski mjaroszynski@fsu.edu http://myweb.fsu.edu/maj09e/ Oregon Transportation Research and Education Consortium Thank You! Acknowledgments: DeVoe L. Moore Center at Florida State University
  27. 27. Liberal scenarioConservative scenarioAverage change of the benefits (or costs) after adopting liberal or conservative assumptions for a specific category of benefits (base values for other parameters): Consumer Surplus18%-8% Congestion Costs7%-7% Externalities - Air Pollution7%-8% Externalities - Accidents9%-9% Capital Costs-12%17% Average change in benefits if a particular scenario is applied to all benefit and cost categories42%-62% Average change in the amount of costs if a particular scenario is applied to all benefit and cost categories-2%3% Appendix: Additional Results and References Sensitivity Analysis for Benefit-Cost estimations
  28. 28. Direct Revenue per Pass. Mile20012002200320042005200620072008200920102011 Buffalo 0.38$ 0.36$ 0.34$ 0.39$ 0.36$ 0.36$ 0.35$ 0.33$ 0.31$ 0.37$ 0.35$ Charlotte 0.34$ 0.26$ 0.46$ 0.20$ 0.20$ Dallas 0.20$ 0.26$ 0.19$ 0.22$ 0.19$ 0.23$ 0.16$ 0.18$ 0.25$ 0.42$ 0.29$ Denver 0.24$ 0.25$ 0.22$ 0.23$ 0.21$ 0.23$ 0.17$ 0.29$ 0.27$ 0.22$ 0.22$ Houston 0.13$ 0.12$ 0.14$ 0.15$ 0.12$ 0.17$ 0.16$ 0.18$ Minneapolis 0.28$ 0.27$ 0.28$ 0.26$ 0.24$ 0.30$ 0.27$ 0.27$ Phoenix 0.20$ 0.24$ 0.24$ Pittsburgh 0.22$ 0.26$ 0.29$ 0.29$ 0.28$ 0.28$ 0.26$ 0.30$ 0.31$ 0.33$ 0.40$ Portland 0.26$ 0.21$ 0.20$ 0.20$ 0.19$ 0.21$ 0.29$ 0.27$ 0.28$ 0.28$ 0.27$ Sacramento 0.26$ 0.33$ 0.30$ 0.24$ 0.22$ 0.25$ 0.30$ 0.27$ 0.27$ 0.27$ 0.28$ Salt Lake 0.22$ 0.25$ 0.23$ 0.23$ 0.22$ 0.22$ 0.16$ 0.22$ 0.29$ 0.26$ 0.24$ San Diego 0.16$ 0.20$ 0.19$ 0.20$ 0.19$ 0.19$ 0.36$ 0.37$ 0.40$ 0.43$ 0.61$ St. Louis 0.20$ 0.19$ 0.18$ 0.18$ 0.19$ 0.21$ 0.20$ 0.23$ 0.16$ 0.19$ 0.18$ Congestion Savings per Pass. Mile20012002200320042005200620072008200920102011Buffalo0.16$ 0.16$ 0.16$ 0.16$ 0.16$ 0.16$ 0.16$ 0.16$ 0.14$ 0.15$ 0.17$ Charlotte0.19$ 0.19$ 0.17$ 0.17$ 0.18$ Dallas0.36$ 0.38$ 0.30$ 0.30$ 0.29$ 0.30$ 0.30$ 0.31$ 0.33$ 0.34$ 0.30$ Denver0.22$ 0.23$ 0.23$ 0.23$ 0.23$ 0.23$ 0.23$ 0.23$ 0.23$ 0.22$ 0.21$ Houston0.23$ 0.23$ 0.24$ 0.24$ 0.24$ 0.26$ 0.27$ 0.26$ Minneapolis0.24$ 0.24$ 0.24$ 0.24$ 0.24$ 0.26$ 0.24$ 0.27$ Phoenix0.19$ 0.21$ 0.25$ Pittsburgh0.25$ 0.25$ 0.24$ 0.24$ 0.24$ 0.24$ 0.24$ 0.24$ 0.23$ 0.24$ 0.27$ Portland0.25$ 0.25$ 0.25$ 0.24$ 0.25$ 0.25$ 0.25$ 0.25$ 0.25$ 0.26$ 0.26$ Sacramento0.21$ 0.21$ 0.21$ 0.21$ 0.21$ 0.21$ 0.21$ 0.21$ 0.21$ 0.21$ 0.21$ Salt Lake0.21$ 0.23$ 0.24$ 0.25$ 0.26$ 0.24$ 0.23$ 0.27$ 0.39$ 0.35$ 0.29$ San Diego0.27$ 0.27$ 0.27$ 0.27$ 0.27$ 0.27$ 0.27$ 0.27$ 0.27$ 0.27$ 0.27$ St. Louis0.19$ 0.20$ 0.19$ 0.20$ 0.20$ 0.20$ 0.20$ 0.20$ 0.19$ 0.22$ 0.19$
  29. 29. Consumer Surplus per Pass. Mile20012002200320042005200620072008200920102011Buffalo0.29$ 0.28$ 0.27$ 0.31$ 0.29$ 0.28$ 0.27$ 0.25$ 0.24$ 0.28$ 0.27$ Charlotte0.11$ 0.11$ 0.14$ 0.14$ 0.15$ Dallas0.11$ 0.10$ 0.09$ 0.10$ 0.09$ 0.15$ 0.09$ 0.12$ 0.13$ 0.13$ 0.12$ Denver0.12$ 0.13$ 0.14$ 0.14$ 0.13$ 0.13$ 0.14$ 0.15$ 0.17$ 0.16$ 0.16$ Houston0.09$ 0.09$ 0.09$ 0.08$ 0.08$ 0.11$ 0.11$ 0.12$ Minneapolis0.18$ 0.18$ 0.19$ 0.17$ 0.17$ 0.19$ 0.17$ 0.18$ Phoenix0.11$ 0.15$ 0.15$ Pittsburgh0.14$ 0.17$ 0.19$ 0.20$ 0.19$ 0.18$ 0.16$ 0.19$ 0.21$ 0.22$ 0.26$ Portland0.16$ 0.15$ 0.11$ 0.14$ 0.15$ 0.16$ 0.18$ 0.18$ 0.18$ 0.19$ 0.20$ Sacramento0.16$ 0.21$ 0.17$ 0.18$ 0.17$ 0.18$ 0.20$ 0.19$ 0.19$ 0.19$ 0.19$ Salt Lake0.12$ 0.15$ 0.12$ 0.15$ 0.13$ 0.09$ 0.08$ 0.11$ 0.16$ 0.15$ 0.16$ San Diego0.17$ 0.18$ 0.18$ 0.19$ 0.19$ 0.18$ 0.18$ 0.18$ 0.19$ 0.21$ 0.20$ St. Louis0.14$ 0.13$ 0.14$ 0.13$ 0.14$ 0.15$ 0.15$ 0.15$ 0.15$ 0.16$ 0.14$ Externalities per Pass. Mile20012002200320042005200620072008200920102011Buffalo0.21$ 0.20$ 0.19$ 0.18$ 0.17$ 0.16$ 0.15$ 0.14$ 0.14$ 0.13$ 0.13$ Charlotte0.14$ 0.14$ 0.14$ 0.14$ 0.13$ Dallas0.23$ 0.21$ 0.20$ 0.19$ 0.18$ 0.17$ 0.16$ 0.15$ 0.15$ 0.14$ 0.14$ Denver0.21$ 0.20$ 0.20$ 0.19$ 0.18$ 0.17$ 0.17$ 0.15$ 0.15$ 0.15$ 0.14$ Houston0.19$ 0.18$ 0.18$ 0.17$ 0.15$ 0.15$ 0.14$ 0.13$ Minneapolis0.19$ 0.18$ 0.17$ 0.17$ 0.16$ 0.15$ 0.15$ 0.14$ Phoenix0.14$ 0.14$ 0.13$ Pittsburgh0.22$ 0.21$ 0.20$ 0.19$ 0.17$ 0.17$ 0.16$ 0.15$ 0.15$ 0.14$ 0.13$ Portland0.23$ 0.23$ 0.22$ 0.21$ 0.19$ 0.18$ 0.17$ 0.16$ 0.16$ 0.16$ 0.15$ Sacramento0.23$ 0.22$ 0.21$ 0.20$ 0.18$ 0.17$ 0.16$ 0.15$ 0.16$ 0.15$ 0.14$ Salt Lake0.20$ 0.19$ 0.19$ 0.17$ 0.17$ 0.18$ 0.17$ 0.16$ 0.14$ 0.14$ 0.14$ San Diego0.24$ 0.23$ 0.22$ 0.20$ 0.19$ 0.18$ 0.17$ 0.16$ 0.16$ 0.16$ 0.15$ St. Louis0.22$ 0.22$ 0.21$ 0.20$ 0.19$ 0.17$ 0.17$ 0.16$ 0.16$ 0.15$ 0.14$
  30. 30. decheadwayrevmpe~mpop_densunemplveh_hhmed_incgastticontractregionalDecentralizationdec1.00Headwayheadway0.351.00Service Densityrevmpersqm0.02-0.291.00Pop. Densitypop_dens0.18-0.25-0.041.00Unemploymentunempl0.070.090.04-0.051.00Zero-veh. h-holdsveh_hh-0.530.14-0.03-0.280.021.00Median Incomemed_inc0.410.100.330.210.13-0.571.00Gas Pricegas0.020.060.10-0.030.310.050.491.00Congestion indextti-0.27-0.390.180.47-0.37-0.09-0.18-0.341.00Contracting Ratiocontract0.480.500.100.060.20-0.230.430.16-0.111.00# of regional auth.regional-0.160.21-0.15-0.030.190.11-0.130.100.22-0.021.00 Correlation Coefficients
  31. 31. Decentralization ratio (Percentage of Total Service Volume Allocated to Routes Not Serving the CBD) 20012002200320042005200620072008200920102011Buffalo30%30%30%30%31%32%33%37%40%44%48% Charlotte27%28%28%29%29% Dallas54%53%53%52%53%55%57%59%62%64%66% Denver61%62%62%63%63%64%64%62%61%59%58% Houston35%35%37%38%38%37%37%37% Minneapolis37%38%37%37%37%37%36%36% Phoenix67%64%62% Pittsburgh18%18%19%17%16%14%14%14%14%14%15% Portland46%47%45%42%40%38%35%36%37%38%39% Sacramento60%61%63%63%63%63%62%61%60%63%65% Salt Lake City40%40%40%42%47%53%54%54%54%54%59% San Diego65%65%65%65%65%65%64%63%62%61%60% St. Louis60%60%63%66%69%74%74%74%74%74%75% Average headway (in minutes) 20012002200320042005200620072008200920102011Buffalo26.124.024.724.523.922.622.623.021.621.923.8Charlotte28.622.319.621.921.5Dallas13.314.413.610.010.111.111.63.612.913.411.9Denver22.323.922.020.620.221.821.218.820.421.421.8Houston9.514.414.515.917.316.016.216.2Minneapolis18.018.416.916.818.219.216.917.2Phoenix27.024.221.8Pittsburgh17.416.716.916.217.817.919.219.520.119.320.6Portland12.610.910.210.410.210.510.610.410.310.811.1Sacramento37.034.433.923.831.433.133.337.832.832.530.9Salt Lake City13.013.113.813.414.113.914.216.316.717.421.3San Diego21.025.026.724.934.932.529.222.821.425.521.9St. Louis19.420.821.626.427.025.922.223.023.727.022.0Service Density (Revenue Miles per Service Area Square Mile, 000s) 20012002200320042005200620072008200920102011Buffalo191185182175190199208226252225233Charlotte143177192187200Dallas321289395365415366352322290280324Denver192186185204223237272277270276295Houston404389417348369293272264Minneapolis499624673722785646702700Phoenix452416408Pittsburgh362323285289269273305253256244209Portland526601608637633663631650709683672Sacramento224192193233184188190213229213202Salt Lake City819010489107166179175117131138San Diego573509491503516539543543570368377St. Louis297355340339356351376396495351405Source: See Table 3.2
  32. 32. Selected References Brown, J. R. and G. L. Thompson (2008a). Examining the Influence of Multidestination Service Orientation on Transit Service Productivity: A Multivariate Analysis. Transportation 35, 2: 237-252. Brown, J. R. and G. L. Thompson (2008b). “The Relationship between Transit Ridership and Urban Decentralization: Insights from Atlanta.” Urban Studies, 45, 5&6: 1119-1139. Brown, J. R. and G. L. Thompson (2008c).Service Orientation, Bus-Rail Service Integration, and Transit Performance: An Examination of 45 U.S. Metropolitan Areas. Transportation Research Record, Journal of the Transportation Research Board 2042: 82-89. Brown, J. R. and G. L. Thompson (2009). Express Bus versus Rail Transit: How the Marriage of Mode and Mission Affects Transit Performance. Transportation Research Record 2110: 45-54. Jaroszynski M. and J. Brown (2014). Do LRT Planning Decisions affect Metropolitan Transit Performance? An Examination of 8 US Metropolitan Areas with LRT Transit Backbones. Transportation Research Record. Mees, Paul (2000). A Very Public Solution: Transport in the Dispersed City. Carlton South, Australia: Melbourne University Press. Mees, Paul (2010). Transport for Suburbia: Beyond the Automobile Age. London and Washington, DC: Earthscan. Thompson, G. L. and J. R. Brown (2006).Explaining Variation in Transit Ridership in U.S. Metropolitan Areas between 1990 and 2000: A Multivariate Analysis. TransportationResearch Record, Journal of the Transportation Research Board, 1986: 172- 181. Thompson, G. L., J. R. Brown, and T. Bhattacharya (2012).What Really Matters for Increasing Transit Ridership: A Statistical Analysis of How Transit Level of Service and Land Use Variables Affect Transit Patronage in Broward County, Florida. UrbanStudies49 (15), 3327-3345. Thompson, G. L., J. R. Brown, T. Bhattacharya, and M. Jaroszynski (2012).Understanding Transit Ridership Demand for a Multi- Destination, Multi-Modal Transit Network in an American Metropolitan Area: Lessons for Increasing Choice Ridership While Maintaining Transit Dependent Ridership.MTI Report 11-06. San Jose, California: Mineta Transportation Institute. Thompson, G. L., and T. G. Matoff (2003). Keeping Up with the Joneses: Planning for Transit in Decentralizing Regions. Journal of the American Planning Association, 69 (3): 296–312.

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