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Erratum to: 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
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Suggested Citation

  • David Hensher & Zheng Li, 2012. "Erratum to: Ridership drivers of bus rapid transit systems," Transportation, Springer, vol. 39(6), pages 1223-1224, November.
    • Handle: RePEc:kap:transp:v:39:y:2012:i:6:p:1223-1224
    DOI: 10.1007/s11116-012-9416-7
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Cleary, Brendan & Duffy, Aidan & Bach, Bjarne & Vitina, Aisma & O’Connor, Alan & Conlon, Michael, 2016. "Estimating the electricity prices, generation costs and CO2 emissions of large scale wind energy exports from Ireland to Great Britain," Energy Policy, Elsevier, vol. 91(C), pages 38-48.
    8. 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.
    9. Rongrong Guo & Francisco Antunes & Jin Zhang & Jingcai Yu & Wenquan Li, 2024. "Joint optimization of headway and number of stops for bilateral bus rapid transit," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-19, March.
    10. Currie, Graham & Delbosc, Alexa, 2014. "Assessing Bus Rapid Transit system performance in Australasia," Research in Transportation Economics, Elsevier, vol. 48(C), pages 142-151.
    11. 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.
    12. 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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