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An Automatic Train Operation Based Real-Time Rescheduling Model for High-Speed Railway

Author

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  • Fan Liu

    (School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Jing Xun

    (School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China)

Abstract

With the continuous development of the Automatic Train Operation (ATO) system in high-speed railways, automatic driving is progressively supplanting manual operations, ushering in a new era of predictability and reliability for high-speed railway transport. Concurrently, the advent of the ATO system provides a notable impact on real-time rescheduling during disruptions, as it equips dispatchers with precise insights into train operation statuses. This paper is dedicated to a thorough analysis of how the transition to automatic driving in train operations influences the real-time rescheduling model. Based on the distinctive impact of the ATO system on real-time rescheduling, we have proposed a mixed-integer linear programming model that combines train re-timing, reordering, and the minimization of passenger delays. To validate the effectiveness of our model, we present several experiments conducted using data from the Beijing–Shanghai high-speed railway line. The results unequivocally demonstrate that our ATO-based model significantly mitigates train delay time, demonstrating its practical value in optimizing high-speed railway operations.

Suggested Citation

  • Fan Liu & Jing Xun, 2023. "An Automatic Train Operation Based Real-Time Rescheduling Model for High-Speed Railway," Mathematics, MDPI, vol. 11(21), pages 1-15, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4546-:d:1274098
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    References listed on IDEAS

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    1. D'Ariano, Andrea & Pacciarelli, Dario & Pranzo, Marco, 2007. "A branch and bound algorithm for scheduling trains in a railway network," European Journal of Operational Research, Elsevier, vol. 183(2), pages 643-657, December.
    2. Törnquist, Johanna & Persson, Jan A., 2007. "N-tracked railway traffic re-scheduling during disturbances," Transportation Research Part B: Methodological, Elsevier, vol. 41(3), pages 342-362, March.
    3. Dorfman, M. J. & Medanic, J., 2004. "Scheduling trains on a railway network using a discrete event model of railway traffic," Transportation Research Part B: Methodological, Elsevier, vol. 38(1), pages 81-98, January.
    4. Meng, Lingyun & Zhou, Xuesong, 2011. "Robust single-track train dispatching model under a dynamic and stochastic environment: A scenario-based rolling horizon solution approach," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 1080-1102, August.
    5. Wu, Weitiao & Lin, Yue & Liu, Ronghui & Jin, Wenzhou, 2022. "The multi-depot electric vehicle scheduling problem with power grid characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 322-347.
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