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Transit competitiveness in polycentric metropolitan regions

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  • Casello, Jeffrey M.

Abstract

This paper analyzes the potential to, and impacts of, increasing transit modal split in a polycentric metropolitan area - the Philadelphia, Pennsylvania region. Potential transit riders are preselected as those travelers whose trips begin and end in areas with transit-supportive land uses, defined as "activity centers," areas of high-density employment and trip attraction. A multimodal traffic assignment model is developed and solved to quantify the generalized cost of travel by transit services and private automobile under (user) equilibrium conditions. The model predicts transit modal split by identifying the origin-destination pairs for which transit offers lower generalized cost. For those origin-destination pairs for which transit does not offer the lowest generalized cost, I compute a transit competitiveness measure, the ratio of transit generalized cost to auto generalized cost. The model is first formulated and solved for existing transit service and regional pricing schemes. Next, various transit incentives (travel time or fare reductions, increased service) and auto disincentives (higher out of pocket expenses) are proposed and their impacts on individual travel choices and system performance are quantified. The results suggest that a coordinated policy of improved transit service and some auto disincentives is necessary to achieve greater modal split and improved system efficiency in the region. Further, the research finds that two levels of coordinated transit service, between and within activity centers, are necessary to realize the greatest improvements in system performance.

Suggested Citation

  • Casello, Jeffrey M., 2007. "Transit competitiveness in polycentric metropolitan regions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(1), pages 19-40, January.
    • Handle: RePEc:eee:transa:v:41:y:2007:i:1:p:19-40
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    References listed on IDEAS

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    Cited by:

    1. Cats, Oded & Wang, Qian & Zhao, Yu, 2015. "Identification and classification of public transport activity centres in Stockholm using passenger flows data," Journal of Transport Geography, Elsevier, vol. 48(C), pages 10-22.
    2. Clifton, Geoffrey T. & Mulley, Corinne, 2016. "A historical overview of enhanced bus services in Australian cities: What has been tried, what has worked?," Research in Transportation Economics, Elsevier, vol. 59(C), pages 11-25.
    3. Carlos Romero & Clara Zamorano & Emilio Ortega & Belén Martín, 2021. "Access to Secondary HSR Stations in the Urban Periphery: A Generalised Cost-Based Assessment," Sustainability, MDPI, vol. 13(21), pages 1-19, November.
    4. Lee, Hasik & Park, Ho-Chul & Kho, Seung-Young & Kim, Dong-Kyu, 2019. "Assessing transit competitiveness in Seoul considering actual transit travel times based on smart card data," Journal of Transport Geography, Elsevier, vol. 80(C).
    5. Albalate, Daniel & Bel, Germà, 2012. "Speed limit laws in America: The role of geography, mobility and ideology," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 337-347.
    6. Lopes, Miguel & Dias, Ana Mélice, 2022. "Changing perspectives in times of crisis. The impact of COVID-19 on territorial accessibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 285-301.
    7. Lopez-Lambas, María Eugenia & Monzon, Andres, 2010. "Private funding and management for public interchanges in Madrid," Research in Transportation Economics, Elsevier, vol. 29(1), pages 323-328.
    8. Scott, Ryan P. & Scott, Tyler A. & Greer, Robert A., 2019. "The environmental and safety performance of gas utilities in the United States," Energy Policy, Elsevier, vol. 133(C).
    9. Joseph DeSalvo & Sisinnio Concas, 2013. "The Effect of Density and Trip-Chaining on the Interaction between Urban Form and Transit Demand," Working Papers 0413, University of South Florida, Department of Economics.

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