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Integrated Optimization of Rolling Stock Scheduling and Flexible Train Formation Based on Passenger Demand for an Intercity High-Speed Railway

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  • Peng Zhao

    (Department of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
    College of Rail Transit, Cangzhou Jiaotong College, Cangzhou 061199, China)

  • Yawei Li

    (Department of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Baoming Han

    (Department of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Ruixia Yang

    (Department of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Zhiping Liu

    (College of Rail Transit, Cangzhou Jiaotong College, Cangzhou 061199, China)

Abstract

The key features of an intercity high-speed railway (IHSR) include its high frequency, the short intervals, and the short distances covered. The mode of rolling stock scheduling generally uses fixed segments. In view of the fact that intercity passenger demand has the characteristics of large fluctuations in terms of time and direction, the use of the traditional rolling stock scheduling plan with a fixed train formation will result in a mismatch between the train formation and passenger demand. In order to improve the matching of train formation and passenger demand and increase the utilization rate of rolling stocks, this paper puts forward the concept of flexible train formation by time period and constructs an integrated optimization model of the rolling stock scheduling and flexible train formation based on passenger demand. The model aims at minimizing the number of rolling stocks, the amount of coupling/decoupling necessary, and the deadhead time. The model takes into account constraints such as the connection method used, the source and destination of the rolling stock, the total amount of rolling stock, and the use of a flexible train formation. In addition, the Gurobi solver is used to accurately solve the problem through the linearization of the model. This paper also provides an example of the Beijing–Tianjin IHSR as a verification of the feasibility and effectiveness of the proposed model. The example compares the indicators in the fixed and flexible train formation modes. The results of the research show that, on the premise of meeting passenger demand, the flexible train formation mode can reduce the cost of rolling stock; increase the efficiency of rolling stock; improve the balance of rolling stock scheduling; and, consequently, provide a reference for the optimization of rolling stock scheduling plan with the background of “cost reduction and efficiency increase” in the railway industry.

Suggested Citation

  • Peng Zhao & Yawei Li & Baoming Han & Ruixia Yang & Zhiping Liu, 2022. "Integrated Optimization of Rolling Stock Scheduling and Flexible Train Formation Based on Passenger Demand for an Intercity High-Speed Railway," Sustainability, MDPI, vol. 14(9), pages 1-22, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5650-:d:810530
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    References listed on IDEAS

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    1. Wang, Yihui & D’Ariano, Andrea & Yin, Jiateng & Meng, Lingyun & Tang, Tao & Ning, Bin, 2018. "Passenger demand oriented train scheduling and rolling stock circulation planning for an urban rail transit line," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 193-227.
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    Cited by:

    1. Yidong Wang & Rui Song & Shiwei He & Zilong Song, 2022. "Train Routing and Track Allocation Optimization Model of Multi-Station High-Speed Railway Hub," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    2. 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.
    3. Yin, Jiateng & Pu, Fan & Yang, Lixing & D’Ariano, Andrea & Wang, Zhouhong, 2023. "Integrated optimization of rolling stock allocation and train timetables for urban rail transit networks: A benders decomposition approach," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).

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