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Isolating high-priority metro and feeder bus transfers using smart card data

Author

Listed:
  • De Zhao

    (Southeast University
    University of Pennsylvania)

  • Wei Wang

    (Southeast University)

  • Amber Woodburn

    (University of Pennsylvania)

  • Megan S. Ryerson

    (University of Pennsylvania
    University of Pennsylvania)

Abstract

Fixed-rail metro (or ‘subway’) infrastructure is generally unable to provide access to all parts of the city grid. Consequently, feeder bus lines are an integral component of urban mass transit systems. While passengers prefer a seamless transfer between these two distinct transportation services, each service’s operations are subject to a different set of factors that contribute to metro-bus transfer delay. Previous attempts to understand transfer delay were limited by the availability of tools to measure the time and cost associated with passengers’ transfer experience. This paper uses data from smart card systems, an emerging technology that automatically collects passenger trip data, to understand transfer delay. The primary objective of this study is to use smart card data to derive a reproducible methodology that isolates high priority transfer points between the metro system and its feeder-bus systems. The paper outlines a methodology to identify transfer transactions in the smart card dataset, estimate bus headways without the aid of geographic location information, estimate three components of the total transfer time (walking time, waiting time, and delay time), and isolate high-priority transfer pairs. The paper uses smart card data from Nanjing, China as a case study. The results isolate eight high priority metro-bus transfer pairs in the Nanjing metro system and finally, offers several targeted measures to improve transfer efficiency.

Suggested Citation

  • De Zhao & Wei Wang & Amber Woodburn & Megan S. Ryerson, 2017. "Isolating high-priority metro and feeder bus transfers using smart card data," Transportation, Springer, vol. 44(6), pages 1535-1554, November.
    • Handle: RePEc:kap:transp:v:44:y:2017:i:6:d:10.1007_s11116-016-9713-7
    DOI: 10.1007/s11116-016-9713-7
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    References listed on IDEAS

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    Cited by:

    1. Wu, Pan & Xu, Lunhui & Zhong, Lingshu & Gao, Kun & Qu, Xiaobo & Pei, Mingyang, 2022. "Revealing the determinants of the intermodal transfer ratio between metro and bus systems considering spatial variations," Journal of Transport Geography, Elsevier, vol. 104(C).
    2. Xiang Li & Qipeng Yan & Yafeng Ma & Chen Luo, 2023. "Spatially Varying Impacts of Built Environment on Transfer Ridership of Metro and Bus Systems," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    3. Merkert, Rico & Bushell, James & Beck, Matthew J., 2020. "Collaboration as a service (CaaS) to fully integrate public transportation – Lessons from long distance travel to reimagine mobility as a service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 267-282.
    4. Li, Jin-Yang & Teng, Jing & Wang, Hui, 2023. "Integrating bipartite network modelling and overlapping community detection: A new method to evaluate transit line coordination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    5. Pan Wu & Jinlong Li & Yuzhuang Pian & Xiaochen Li & Zilin Huang & Lunhui Xu & Guilin Li & Ruonan Li, 2022. "How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method," Sustainability, MDPI, vol. 14(15), pages 1-31, August.
    6. Chen, Enhui & Stathopoulos, Amanda & Nie, Yu (Marco), 2022. "Transfer station choice in a multimodal transit system: An empirical study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 337-355.
    7. Yi Cao & Dandan Jiang & Shan Wang, 2022. "Optimization for Feeder Bus Route Model Design with Station Transfer," Sustainability, MDPI, vol. 14(5), pages 1-15, February.
    8. Meina Zheng & Feng Liu & Xiucheng Guo & Xinyue Lei, 2019. "Assessing the Distribution of Commuting Trips and Jobs-Housing Balance Using Smart Card Data: A Case Study of Nanjing, China," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    9. Park, Chung & Lee, Jungpyo & Sohn, So Young, 2019. "Recommendation of feeder bus routes using neural network embedding-based optimization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 329-341.

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