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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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