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Effectiveness of dynamic reordering and rerouting of trains in a complicated and densely occupied station area

Author

Listed:
  • F. Corman
  • A. D'Ariano
  • M. Pranzo
  • I.A. Hansen

Abstract

Railway traffic experiences disturbances during operations that cause conflicts between train paths or even deadlock situations. Dispatchers need actions to restore feasibility and limit spreading of delays through the network. To help them in such a task, the dispatching support tool ROMA (Railway traffic Optimization by Means of Alternative graphs) has been implemented in a laboratory environment. This paper reports on enhancements to the underlying train dispatching model as well as to the solution algorithms studied in order to tackle the increased complexity of busy stations with multiple conflicting paths and high service frequencies. Advanced train reordering and rerouting techniques are compared with straightforward rules and the current approach in the Netherlands. Extensive computational studies based on accepted statistical distributions of train delays for Utrecht Central Station assess the effectiveness of the ROMA tool in terms of solution quality and computation time.

Suggested Citation

  • F. Corman & A. D'Ariano & M. Pranzo & I.A. Hansen, 2011. "Effectiveness of dynamic reordering and rerouting of trains in a complicated and densely occupied station area," Transportation Planning and Technology, Taylor & Francis Journals, vol. 34(4), pages 341-362, March.
    • Handle: RePEc:taf:transp:v:34:y:2011:i:4:p:341-362
    DOI: 10.1080/03081060.2011.577152
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    Citations

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    Cited by:

    1. Xu, Peijuan & Corman, Francesco & Peng, Qiyuan & Luan, Xiaojie, 2017. "A train rescheduling model integrating speed management during disruptions of high-speed traffic under a quasi-moving block system," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 638-666.
    2. Gao, Yuan & Kroon, Leo & Yang, Lixing & Gao, Ziyou, 2018. "Three-stage optimization method for the problem of scheduling additional trains on a high-speed rail corridor," Omega, Elsevier, vol. 80(C), pages 175-191.
    3. Bettinelli, Andrea & Santini, Alberto & Vigo, Daniele, 2017. "A real-time conflict solution algorithm for the train rescheduling problem," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 237-265.
    4. 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).
    5. Li, Yang & Yang, Xin & Wu, Jianjun & Sun, Huijun & Guo, Xin & Zhou, Li, 2021. "Discrete-event simulations for metro train operation under emergencies: A multi-agent based model with parallel computing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    6. Luan, Xiaojie & Wang, Yihui & De Schutter, Bart & Meng, Lingyun & Lodewijks, Gabriel & Corman, Francesco, 2018. "Integration of real-time traffic management and train control for rail networks - Part 1: Optimization problems and solution approaches," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 41-71.
    7. 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.

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