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Joint optimization model for train scheduling and train stop planning with passengers distribution on railway corridors

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  • Jianguo Qi
  • Shukai Li
  • Yuan Gao
  • Kai Yang
  • Pei Liu

Abstract

Aiming to provide a more practical modeling framework for railway optimization problem, this paper investigates the joint optimization model for train scheduling, train stop planning and passengers distributing by considering the passenger demands over each origin and destination (OD) pair on a high-speed railway corridor. Specifically, through introducing new decision variables associated with the number of passengers distributed in each train over each OD pair and formulating the connection constraints between the train stop plan and passenger distributions, the total travel time of all the trains is firstly adopted as the objective function to optimize the train stop plan and timetable with the passenger demands being guaranteed. Then, based on the generated train stop plan and timetable, the passenger distribution plan is further optimized with the purpose of minimizing the total travel time of all the passengers. Finally, the effectiveness and efficiency of the proposed approaches are verified by the obtained train stop plans, timetables and passenger distribution plans for a sample railway corridor and Wuhan–Guangzhou high-speed railway corridor. The computational results showed that the proposed methods can effectively obtain the train stop plan, timetable and passenger distribution plan at the same time.

Suggested Citation

  • Jianguo Qi & Shukai Li & Yuan Gao & Kai Yang & Pei Liu, 2018. "Joint optimization model for train scheduling and train stop planning with passengers distribution on railway corridors," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(4), pages 556-570, April.
    • Handle: RePEc:taf:tjorxx:v:69:y:2018:i:4:p:556-570
    DOI: 10.1057/s41274-017-0248-x
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    Citations

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    Cited by:

    1. Pu, Song & Zhan, Shuguang, 2021. "Two-stage robust railway line-planning approach with passenger demand uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    2. Ali Shahabi & Sadigh Raissi & Kaveh Khalili-Damghani & Meysam Rafei, 2021. "Designing a resilient skip-stop schedule in rapid rail transit using a simulation-based optimization methodology," Operational Research, Springer, vol. 21(3), pages 1691-1721, September.
    3. Feng, Tao & Lusby, Richard M. & Zhang, Yongxiang & Peng, Qiyuan, 2024. "Integrating train service route design with passenger flow allocation for an urban rail transit line," European Journal of Operational Research, Elsevier, vol. 313(1), pages 146-170.
    4. Cacchiani, Valentina & Qi, Jianguo & Yang, Lixing, 2020. "Robust optimization models for integrated train stop planning and timetabling with passenger demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 136(C), pages 1-29.
    5. Xu, Xiaoming & Li, Chung-Lun & Xu, Zhou, 2021. "Train timetabling with stop-skipping, passenger flow, and platform choice considerations," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 52-74.
    6. Zilong Fan & Di Liu & Wenyu Rong & Chengrui Li, 2022. "A Multi-Objective Optimization Model for the Intercity Railway Train Operation Plan: The Case of Beijing-Xiong’an ICR," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    7. Jin Qin & Xiqiong Li & Kang Yang & Guangming Xu, 2022. "Joint Optimization of Ticket Pricing Strategy and Train Stop Plan for High-Speed Railway: A Case Study," Mathematics, MDPI, vol. 10(10), pages 1-17, May.

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