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The impact of lateness and reliability on passenger rail demand

Author

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  • Batley, Richard
  • Dargay, Joyce
  • Wardman, Mark

Abstract

To date, forecasts of the demand impacts of lateness and reliability have been derived largely from individual-level models taken at a snapshot in time. The contribution of this paper is to develop a dynamic model of rail demand at the market-level, yielding short and long-run elasticities with respect to lateness. Whereas individual-level models have suggested a high valuation of lateness and reliability, our market-level models indicate a relatively mooted demand response. Reconciling these findings, we reason that, whilst rail travellers show considerable disdain for experiences of lateness, such experiences will not necessarily dissuade them from travelling by train.

Suggested Citation

  • Batley, Richard & Dargay, Joyce & Wardman, Mark, 2011. "The impact of lateness and reliability on passenger rail demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(1), pages 61-72, January.
  • Handle: RePEc:eee:transe:v:47:y:2011:i:1:p:61-72
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    Citations

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    Cited by:

    1. Stathopoulos, Amanda & Hess, Stephane, 2012. "Revisiting reference point formation, gains–losses asymmetry and non-linear sensitivities with an emphasis on attribute specific treatment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1673-1689.
    2. Abate, Megersa & Lijesen, Mark & Pels, Eric & Roelevelt, Adriaan, 2013. "The impact of reliability on the productivity of railroad companies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 51(C), pages 41-49.
    3. Mark Wardman & Richard Batley, 2014. "Travel time reliability: a review of late time valuations, elasticities and demand impacts in the passenger rail market in Great Britain," Transportation, Springer, vol. 41(5), pages 1041-1069, September.
    4. Piening, J. & Ehrmann, T. & Meiseberg, B., 2013. "Competing risks for train tickets – An empirical investigation of customer behavior and performance in the railway industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 51(C), pages 1-16.
    5. 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.
    6. Rahman, Syed & Balijepalli, Chandra, 2016. "Understanding the determinants of demand for public transport: Evidence from suburban rail operations in five divisions of Indian Railways," Transport Policy, Elsevier, vol. 48(C), pages 13-22.
    7. Amanda Stathopoulos & Stephane Hess, 2011. "Referencing, Gains-Losses Asymmetry And Non-Linear Sensitivities In Commuter Decisions: One Size Does Not Fit All!," Working Papers 0511, CREI Università degli Studi Roma Tre, revised 2011.

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