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Railway disruption management challenges and possible solution directions

Author

Listed:
  • Nadjla Ghaemi

    (Delft University of Technology)

  • Oded Cats

    (Delft University of Technology)

  • Rob M. P. Goverde

    (Delft University of Technology)

Abstract

This paper investigates the challenges of railway traffic controllers in dealing with big disruptions and the kind of support tools that could help to improve their task in terms of performance, lead time and workload. The disruption handling process can be partitioned into three phases resembling a bathtub. For each phase the essential decision making process has been identified. Currently, the support to rail traffic controllers in case of severe disruptions is limited to predefined contingency plans that are not always feasible or applicable. In the literature, models and algorithms have been identified that could be used in the different parts of the three phases of the disruption handling process. This paper investigates the processes of disruption management in practice and the challenges that traffic controllers are facing during a disruption. The literature of models applicable to disruption management is reviewed and classified based on the three phases of the traffic state during disruptions. Finally, a rescheduling optimization model is applied to a case of complete blockage on a corridor of the Dutch railway network. The case study shows how a microscopic model could support the traffic controllers by providing real-time solutions for different phases of a disruption.

Suggested Citation

  • Nadjla Ghaemi & Oded Cats & Rob M. P. Goverde, 2017. "Railway disruption management challenges and possible solution directions," Public Transport, Springer, vol. 9(1), pages 343-364, July.
    • Handle: RePEc:spr:pubtra:v:9:y:2017:i:1:d:10.1007_s12469-017-0157-z
    DOI: 10.1007/s12469-017-0157-z
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    Cited by:

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    6. Wang, Yihui & Zhao, Kangqi & D’Ariano, Andrea & Niu, Ru & Li, Shukai & Luan, Xiaojie, 2021. "Real-time integrated train rescheduling and rolling stock circulation planning for a metro line under disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 152(C), pages 87-117.
    7. Knoester, Max J. & Bešinović, Nikola & Afghari, Amir Pooyan & Goverde, Rob M.P. & van Egmond, Jochen, 2024. "A data-driven approach for quantifying the resilience of railway networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
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    10. Wen Hua & Ghim Ping Ong, 2018. "Effect of information contagion during train service disruption for an integrated rail-bus transit system," Public Transport, Springer, vol. 10(3), pages 571-594, December.
    11. Åse Jevinger & Jan A. Persson, 2019. "Exploring the potential of using real-time traveler data in public transport disturbance management," Public Transport, Springer, vol. 11(2), pages 413-441, August.
    12. Gkiotsalitis, K. & Cats, O., 2021. "At-stop control measures in public transport: Literature review and research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
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    14. van Lieshout, R.N. & Bouman, P.C. & Huisman, D., 2018. "Determining and Evaluating Alternative Line Plans in (Near) Out-of-Control Situations," Econometric Institute Research Papers EI2018-20, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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