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Ridership drivers of bus rapid transit systems

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  • David Hensher
  • Zheng Li

Abstract

We have collected information on 46 bus rapid transit (BRT) systems throughout the world to investigate the potential patronage drivers. From a large number of candidate explanatory variables (quantitative and qualitative), 11 sources of systematic variation are identified which have a statistically significant impact on daily passenger-trip numbers. These sources are fare, headway, the length of the BRT network, the number of corridors, average distance between stations; whether there is: an integrated network of routes and corridors, modal integration at BRT stations, pre-board fare collection and fare verification, quality control oversight from an independent agency, at-level boarding and alighting, as well as the location of BRT. The findings of this paper offer important insights into features of BRT systems that are positive contributors to growing patronage and hence should be taken into account in designing and planning BRT systems. Copyright Springer Science+Business Media, LLC. 2012

Suggested Citation

  • David Hensher & Zheng Li, 2012. "Ridership drivers of bus rapid transit systems," Transportation, Springer, vol. 39(6), pages 1209-1221, November.
    • Handle: RePEc:kap:transp:v:39:y:2012:i:6:p:1209-1221
    DOI: 10.1007/s11116-012-9392-y
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    References listed on IDEAS

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    1. Tirachini, Alejandro & Hensher, David A., 2011. "Bus congestion, optimal infrastructure investment and the choice of a fare collection system in dedicated bus corridors," Transportation Research Part B: Methodological, Elsevier, vol. 45(5), pages 828-844, June.
    2. Hensher, David A., 2008. "Assessing systematic sources of variation in public transport elasticities: Some comparative warnings," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(7), pages 1031-1042, August.
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    4. David Hensher & Thomas Golob, 2008. "Bus rapid transit systems: a comparative assessment," Transportation, Springer, vol. 35(4), pages 501-518, July.
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    Citations

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    Cited by:

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    3. An, Kun & Lo, Hong K., 2016. "Two-phase stochastic program for transit network design under demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 157-181.
    4. Merkert, Rico & Mulley, Corinne & Hakim, Md Mahbubul, 2017. "Determinants of bus rapid transit (BRT) system revenue and effectiveness – A global benchmarking exercise," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 75-88.
    5. Kiani Mavi, Reza & Zarbakhshnia, Navid & Khazraei, Armin, 2018. "Bus rapid transit (BRT): A simulation and multi criteria decision making (MCDM) approach," Transport Policy, Elsevier, vol. 72(C), pages 187-197.
    6. Guzman, Luis A. & Beltran, Carlos & Bonilla, Jorge & Gomez Cardona, Santiago, 2021. "BRT fare elasticities from smartcard data: Spatial and time-of-the-day differences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 335-348.
    7. Hensher, David A. & Li, Zheng & Mulley, Corinne, 2014. "Drivers of bus rapid transit systems – Influences on patronage and service frequency," Research in Transportation Economics, Elsevier, vol. 48(C), pages 159-165.
    8. Li, Zheng & Hensher, David A., 2020. "Performance contributors of bus rapid transit systems: An ordered choice approach," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 154-161.
    9. Vergel-Tovar, C. Erik & Rodriguez, Daniel A., 2018. "The ridership performance of the built environment for BRT systems: Evidence from Latin America," Journal of Transport Geography, Elsevier, vol. 73(C), pages 172-184.
    10. Miriam Rocha & Cristina Albuquerque Moreira Silva & Reinaldo Germano Santos Junior & Michel Anzanello & Gabrielli Harumi Yamashita & Luis Antonio Lindau, 2020. "Selecting the most relevant variables towards clustering bus priority corridors," Public Transport, Springer, vol. 12(3), pages 587-609, October.
    11. Jinbao Zhao & Wei Deng & Yan Song & Yueran Zhu, 2014. "Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models," Transportation, Springer, vol. 41(1), pages 133-155, January.

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