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Daily Line Planning Optimization for High-Speed Railway Lines

Author

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  • Jinfei Wu

    (China Academy of Railway Sciences, Beijing 100081, China
    Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Xinghua Shan

    (Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Jingxia Sun

    (CCCC Railway Consultants Group Co., Ltd., Beijing 100088, China)

  • Shengyuan Weng

    (Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Shuo Zhao

    (Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

Abstract

Daily line planning in the operation stage should satisfy the fluctuating travel demand on different days and ensure the operation stability. In this paper, we propose an approach of daily line planning optimization for high-speed railway (HSR) lines to trade off the system costs and operation stability. The line plan is optimized by adjusting the reference line plan based on the baseline plan. A bi-level programming model is constructed based on Stackelberg game theory to describe the interaction and conflicts between railway companies and passengers. We propose the thought of “trigger decision, space-time coupling and joint iteration” to solve the model under the framework of the Simulated Annealing Algorithm (SAA). The case study on the Beijing–Shanghai HSR Line demonstrates that the adjusted line plan can not only optimize the system costs but also ensure the operation stability. It can provide sufficient transit capacity to satisfy the travel requirements of passengers and present the obvious advantage of operation cost reduction.

Suggested Citation

  • Jinfei Wu & Xinghua Shan & Jingxia Sun & Shengyuan Weng & Shuo Zhao, 2023. "Daily Line Planning Optimization for High-Speed Railway Lines," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3263-:d:1064515
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    References listed on IDEAS

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