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Considering a dynamic impact zone for real-time railway traffic management

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  • Van Thielen, Sofie
  • Corman, Francesco
  • Vansteenwegen, Pieter

Abstract

In a railway system, a conflict occurs when two trains require the same part of the infrastructure at the same time. Currently, such conflicts are typically resolved manually by experienced dispatchers. However, it is impossible for them to fully anticipate the impact of their actions on the entire network. This paper proposes a conflict prevention strategy which focuses only on the relevant part of the network and traffic and, consequently, proposes a solution for that part only. The proposed strategy first looks for possible rerouting options by using an optimization model. If no solution is found, a solution based on delaying one of the trains is required. This retiming/reordering heuristic uses information from an offline calculation, for determining related conflicts that frequently occur. In this way, a so-called dynamic impact zone is created online for each conflict. When deciding which train to delay, the potential conflicts and the incurred delays of all trains in this dynamic impact zone are taken into account.

Suggested Citation

  • Van Thielen, Sofie & Corman, Francesco & Vansteenwegen, Pieter, 2018. "Considering a dynamic impact zone for real-time railway traffic management," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 39-59.
    • Handle: RePEc:eee:transb:v:111:y:2018:i:c:p:39-59
    DOI: 10.1016/j.trb.2018.03.003
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    References listed on IDEAS

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    2. Meloni, Carlo & Pranzo, Marco & Samà, Marcella, 2021. "Risk of delay evaluation in real-time train scheduling with uncertain dwell times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    3. Luan, Xiaojie & De Schutter, Bart & Meng, Lingyun & Corman, Francesco, 2020. "Decomposition and distributed optimization of real-time traffic management for large-scale railway networks," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 72-97.
    4. Yang, Songpo & Liao, Feixiong & Wu, Jianjun & Timmermans, Harry J.P. & Sun, Huijun & Gao, Ziyou, 2020. "A bi-objective timetable optimization model incorporating energy allocation and passenger assignment in an energy-regenerative metro system," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 85-113.

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