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Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground

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  • Guo, Zhan
  • Wilson, Nigel H.M.

Abstract

Few studies have adequately assessed the cost of transfers2 in public transport systems, or provided useful guidance on transfer improvements, such as where to invest (which facility), how to invest (which aspect), and how much to invest (quantitative justification of the investment). This paper proposes a new method based on path choice,3 taking into account both the operator's service supply and the customers' subjective perceptions to assess transfer cost and to identify ways to reduce it. This method evaluates different transfer components (e.g., transfer walking, waiting, and penalty) with distinct policy solutions and differentiates between transfer stations and movements. The method is applied to one of the largest and most complex public transport systems in the world, the London Underground (LUL), with a focus on 17 major transfer stations and 303 transfer movements. This study confirms that transfers pose a significant cost to LUL, and that cost is distributed unevenly across stations and across platforms at a station. Transfer stations are perceived very differently by passengers in terms of their overall cost and composition. The case study suggests that a better understanding of transfer behavior and improvements to the transfer experience could significantly benefit public transport systems.

Suggested Citation

  • Guo, Zhan & Wilson, Nigel H.M., 2011. "Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 91-104, February.
  • Handle: RePEc:eee:transa:v:45:y:2011:i:2:p:91-104
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    References listed on IDEAS

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    Cited by:

    1. Kim, Kyung Min & Hong, Sung-Pil & Ko, Suk-Joon & Kim, Dowon, 2015. "Does crowding affect the path choice of metro passengers?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 292-304.
    2. Cats, Oded & Koutsopoulos, Haris N. & Burghout, Wilco & Toledo, Tomer, 2013. "Effect of real-time transit information on dynamic path choice of passengers," Working papers in Transport Economics 2013:28, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    3. van Wee, Bert & Bohte, Wendy & Molin, Eric & Arentze, Theo & Liao, Feixiong, 2014. "Policies for synchronization in the transport–land-use system," Transport Policy, Elsevier, vol. 31(C), pages 1-9.
    4. Cheng, Yung-Hsiang & Chen, Ssu-Yun, 2015. "Perceived accessibility, mobility, and connectivity of public transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 386-403.
    5. repec:eee:transa:v:109:y:2018:i:c:p:89-107 is not listed on IDEAS
    6. Peer, Stefanie & Knockaert, Jasper & Verhoef, Erik T., 2016. "Train commuters’ scheduling preferences: Evidence from a large-scale peak avoidance experiment," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 314-333.
    7. Hernandez, Sara & Monzon, Andres & de Oña, Rocío, 2016. "Urban transport interchanges: A methodology for evaluating perceived quality," Transportation Research Part A: Policy and Practice, Elsevier, vol. 84(C), pages 31-43.
    8. Guo, Zhan, 2011. "Mind the map! The impact of transit maps on path choice in public transit," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(7), pages 625-639, August.
    9. Suman, Hemant K. & Bolia, Nomesh B. & Tiwari, Geetam, 2017. "Comparing public bus transport service attributes in Delhi and Mumbai: Policy implications for improving bus services in Delhi," Transport Policy, Elsevier, vol. 56(C), pages 63-74.
    10. Canca, David & Barrena, Eva & De-Los-Santos, Alicia & Andrade-Pineda, José Luis, 2016. "Setting lines frequency and capacity in dense railway rapid transit networks with simultaneous passenger assignment," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 251-267.
    11. Xu, Xin-yue & Liu, Jun & Li, Hai-ying & Jiang, Man, 2016. "Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 130-148.
    12. Nassir, Neema & Hickman, Mark & Malekzadeh, Ali & Irannezhad, Elnaz, 2016. "A utility-based travel impedance measure for public transit network accessibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 26-39.
    13. Yang, Lixing & Zhang, Yan & Li, Shukai & Gao, Yuan, 2016. "A two-stage stochastic optimization model for the transfer activity choice in metro networks," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 271-297.
    14. Ceder, Avishai & Chowdhury, Subeh & Taghipouran, Nima & Olsen, Jared, 2013. "Modelling public-transport users’ behaviour at connection point," Transport Policy, Elsevier, vol. 27(C), pages 112-122.
    15. Cheng, Yung-Hsiang & Tseng, Wei-Chih, 2016. "Exploring the effects of perceived values, free bus transfer, and penalties on intermodal metro–bus transfer users' intention," Transport Policy, Elsevier, vol. 47(C), pages 127-138.
    16. repec:eee:ecolec:v:140:y:2017:i:c:p:58-65 is not listed on IDEAS
    17. Chowdhury, Subeh & Ceder, Avishai (Avi), 2016. "Users’ willingness to ride an integrated public-transport service: A literature review," Transport Policy, Elsevier, vol. 48(C), pages 183-195.

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