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Are retrospective rail punctuality indicators useful? Evidence from users perceptions

Author

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  • Thierry Blayac

    (CEE-M - Centre d'Economie de l'Environnement - Montpellier - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro - Montpellier SupAgro - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement)

  • Maïté Stéphan

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

Abstract

This study analyzes the perceptions of individuals on retrospective rail punctuality indicators to determine the most useful indicator according to socio-demographic characteristics, regular trip behavior variables, and railways transportation habits variables. In choice situations, individuals must choose between four punctuality indicators and an out option. Common punctuality indicators have been selected among those proposed by the authority for quality of service in transport, as well as a new punctuality indicator from the financial literature: Delay-at-Risk. Thus, via an online survey and econometric modeling, we show that respondents appreciate the usefulness of punctuality indicators for planning their long-distance rail trips. The usefulness is reinforced by the fact that respondents employ several modes for regular trips and frequent train users. Moreover, they have already experienced missed appointments or connections. The risk attitude and prudence of respondents also play an important role but not totally in the expected direction. Lastly, Delay-at-Risk, although unknown and more complex in its formulation, exhibits some characteristics that are appreciated by users..

Suggested Citation

  • Thierry Blayac & Maïté Stéphan, 2021. "Are retrospective rail punctuality indicators useful? Evidence from users perceptions," Post-Print hal-03162550, HAL.
  • Handle: RePEc:hal:journl:hal-03162550
    DOI: 10.1016/j.tra.2021.01.013
    Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-03162550v1
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    Cited by:

    1. Jaime Larumbe, 2021. "Evaluating Sustainability of Mass Rapid Transit Stations in Dubai," Sustainability, MDPI, vol. 13(23), pages 1-16, November.
    2. Adrian Barchański & Renata Żochowska & Marcin Jacek Kłos, 2022. "A Method for the Identification of Critical Interstop Sections in Terms of Introducing Electric Buses in Public Transport," Energies, MDPI, vol. 15(20), pages 1-37, October.
    3. Sobrie, Léon & Verschelde, Marijn & Hennebel, Veerle & Roets, Bart, 2023. "Capturing complexity over space and time via deep learning: An application to real-time delay prediction in railways," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1201-1217.

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    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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