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Analyzing the Determinants of Transit Ridership Using a Two-Stage Least Squares Regression on a National Sample of Urbanized Areas

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  • Taylor, Brian D.
  • Miller, Douglas
  • Iseki, Hiroyuki
  • Fink, Camille

Abstract

Understanding the factors influencing transit ridership is central to decisions on transportation system investments and the pricing and deployment of transit services. Yet most previous analyses of transit ridership have examined one or just a few systems, have not included many of the control variables thought to influence transit use, and have not addressed the simultaneous relationship between transit supply and demand. This study addresses the shortcomings in the previous research by (1) conducting a cross-sectional analysis of transit use in 265 urbanized areas, (2) testing an array of variables measuring transit system characteristics, auto system characteristics, geography, metropolitan economy, and population characteristics, and (3) constructing two-stage least squares regression models to account for simultaneity between supply and demand. We find that most of the variation in transit ridership can be explained by (1) the size (population and area) of the metropolitan area, (2) the vitality of the regional economy (median housing costs), and (3) the share of the population with low levels of private vehicle access (carless households). We find further that transit patronage is to a lesser extent, explained by transit service levels and fares. The observed influence of fares on ridership is consistent with the literature. Likewise, the relative influence of transit service levels on ridership is greater than the influence of transit fares. Finally, our separation of transit service supply into two variables – an instrumental control variable and a residual policy variable – allows for more nuanced assessments of the ridership effects of changes in transit service.

Suggested Citation

  • Taylor, Brian D. & Miller, Douglas & Iseki, Hiroyuki & Fink, Camille, 2003. "Analyzing the Determinants of Transit Ridership Using a Two-Stage Least Squares Regression on a National Sample of Urbanized Areas," University of California Transportation Center, Working Papers qt7xf3q4vh, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt7xf3q4vh
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    Cited by:

    1. Yuxin He & Yang Zhao & Kwok Leung Tsui, 2021. "An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership," Transportation, Springer, vol. 48(3), pages 1185-1216, June.
    2. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 2020. "Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    3. Junfeng Jiao & Yefu Chen, 2023. "Is the relationship between bus and light rail transit a competition substitution or a congestion substitution? An empirical study in Seattle," Public Transport, Springer, vol. 15(2), pages 575-594, June.
    4. Cummings, Christopher & Mahmassani, Hani, 2022. "Does intercity rail station placement matter? Expansion of the node-place model to identify station location impacts on Amtrak ridership," Journal of Transport Geography, Elsevier, vol. 99(C).
    5. Moniruzzaman, Md & Páez, Antonio, 2012. "Accessibility to transit, by transit, and mode share: application of a logistic model with spatial filters," Journal of Transport Geography, Elsevier, vol. 24(C), pages 198-205.
    6. Jun, Myung-Jin & Choi, Keechoo & Jeong, Ji-Eun & Kwon, Ki-Hyun & Kim, Hee-Jae, 2015. "Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul," Journal of Transport Geography, Elsevier, vol. 48(C), pages 30-40.
    7. Wang, Jing & Wan, Feng & Dong, Chunjiao & Yin, Chaoying & Chen, Xiaoyu, 2023. "Spatiotemporal effects of built environment factors on varying rail transit station ridership patterns," Journal of Transport Geography, Elsevier, vol. 109(C).

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    Social and Behavioral Sciences;

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