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Detecting train reroutings with process mining

Author

Listed:
  • Gert Janssenswillen

    (Hasselt University
    Research Foundation Flanders (FWO))

  • Benoît Depaire

    (Hasselt University)

  • Sabine Verboven

    (Infrabel)

Abstract

One of the objectives of railway infrastructure managers is to improve the punctuality of their operations while satisfying safety requirements and coping with limited capacity. To fulfil this objective, capacity planning and monitoring have become an absolute necessity. Railway infrastructure managers possess tremendous amounts of data about the railway operations, which are recorded in so-called train describer systems. In this paper, a set of methods is proposed to guide the analysis of capacity usage based on these data. In particular, train connections are categorized according to the severity of train reroutings as well as the diversity of these reroutings. The applied method is able to highlight areas in the railway network, where trains have a higher tendency to diverge from their allocated route. The method is independent from the underlying infrastructure, and can, therefore, be reused effortlessly on new cases. The analysis provides a starting point to improve the planning of capacity usage and can be used to facilitate the communication between capacity planning at one hand and operations on the other hand. At the same time, it presents an illustration on how process mining can be used for analysis of train describer data.

Suggested Citation

  • Gert Janssenswillen & Benoît Depaire & Sabine Verboven, 2018. "Detecting train reroutings with process mining," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(1), pages 1-24, March.
    • Handle: RePEc:spr:eurjtl:v:7:y:2018:i:1:d:10.1007_s13676-017-0105-8
    DOI: 10.1007/s13676-017-0105-8
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    References listed on IDEAS

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    1. Yuan, Jianxin & Hansen, Ingo A., 2007. "Optimizing capacity utilization of stations by estimating knock-on train delays," Transportation Research Part B: Methodological, Elsevier, vol. 41(2), pages 202-217, February.
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    Cited by:

    1. Potoniec, Jedrzej & Sroka, Daniel & Pawlak, Tomasz P., 2022. "Continuous discovery of Causal nets for non-stationary business processes using the Online Miner," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1304-1320.
    2. Zerbino, Pierluigi & Stefanini, Alessandro & Aloini, Davide, 2021. "Process Science in Action: A Literature Review on Process Mining in Business Management," Technological Forecasting and Social Change, Elsevier, vol. 172(C).

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