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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.
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 45 (2011)
    Issue (Month): 2 (February)
    Pages: 91-104

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    Handle: RePEc:eee:transa:v:45:y:2011:i:2:p:91-104

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    Related research

    Keywords: Transfer cost Transfer penalty Path choice Public transport London Underground;

    References

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    1. Shafahi, Yousef & Khani, Alireza, 2010. "A practical model for transfer optimization in a transit network: Model formulations and solutions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(6), pages 377-389, July.
    2. Menghini, G. & Carrasco, N. & Schüssler, N. & Axhausen, K.W., 2010. "Route choice of cyclists in Zurich," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(9), pages 754-765, November.
    3. Bhat, Chandra R. & Pulugurta, Vamsi, 1998. "A comparison of two alternative behavioral choice mechanisms for household auto ownership decisions," Transportation Research Part B: Methodological, Elsevier, vol. 32(1), pages 61-75, January.
    4. Azevedo, JoseAugusto & Santos Costa, Maria Emilia O. & Silvestre Madeira, Joaquim Joao E. R. & Vieira Martins, Ernesto Q., 1993. "An algorithm for the ranking of shortest paths," European Journal of Operational Research, Elsevier, vol. 69(1), pages 97-106, August.
    5. Guihaire, Valérie & Hao, Jin-Kao, 2008. "Transit network design and scheduling: A global review," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(10), pages 1251-1273, December.
    6. Liu, Henry X. & Recker, Will & Chen, Anthony, 2004. "Uncovering the contribution of travel time reliability to dynamic route choice using real-time loop data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(6), pages 435-453, July.
    7. Hine, J. & Scott, J., 2000. "Seamless, accessible travel: users' views of the public transport journey and interchange," Transport Policy, Elsevier, vol. 7(3), pages 217-226, July.
    8. Rietveld, P. & Bruinsma, F. R. & van Vuuren, D. J., 2001. "Coping with unreliability in public transport chains: A case study for Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(6), pages 539-559, July.
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    Cited by:
    1. 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.
    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. 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.
    4. 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.

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