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Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks

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  • Harshad Khadilkar

    (TATA Consultancy Services, Andheri East, 400093 Mumbai, India)

Abstract

This paper evaluates the robustness of a railway network with respect to operational delays. It assumes that trains in the network operate on fixed routes and with reference to a timetable. A stochastic delay propagation model is proposed for identifying primary (externally imposed) delays and for computing the resultant secondary (knock-on) delays. Delay probability distributions are computed for each train at each station on its journey, using timetable and infrastructure data for identifying potential station resource conflicts with other trains. The delay predictions are used to evaluate schedule robustness using two newly proposed metrics. Individual robustness measures the ability of trains to limit the adverse effects of their own primary delays. On the other hand, collective robustness measures the ability of the network as a whole, to limit the knock-on effects of primary delays imposed on a small fraction of trains. The two metrics provide stochastic guarantees on the punctuality of trains when the published schedule is put in operation. The applicability of the proposed methodology is validated using empirical data from a portion of the Indian Railways network, containing more than 38,000 train arrival/departure records. While a railway network is used as a case study, the same ideas can be applied to any scheduled transportation network.

Suggested Citation

  • Harshad Khadilkar, 2017. "Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks," Transportation Science, INFORMS, vol. 51(4), pages 1161-1176, November.
    • Handle: RePEc:inm:ortrsc:v:51:y:2017:i:4:p:1161-1176
    DOI: 10.1287/trsc.2016.0703
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    References listed on IDEAS

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    Cited by:

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    3. Yu-Jun Zheng, 2018. "Emergency Train Scheduling on Chinese High-Speed Railways," Transportation Science, INFORMS, vol. 52(5), pages 1077-1091, October.

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