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An exact method for the integrated optimization of subway lines operation strategies with asymmetric passenger demand and operating costs

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  • Mo, Pengli
  • D’Ariano, Andrea
  • Yang, Lixing
  • Veelenturf, Lucas P.
  • Gao, Ziyou

Abstract

Subway lines connecting different urban functional zones in large cities have direction-dependent and time-variant passenger demand, namely, asymmetry in passenger demand. Most existing studies adopt a symmetric strategy to design operations in both directions and sequentially optimize the different problems associated with operations, thereby failing to meet the asymmetry in passenger demand. This study formulates an asymmetric operation strategy as an integrated mixed-integer non-linear model to optimize the entire operational process of rolling stock from the perspective of service quality and operating costs. Based on the proposed model, an exact algorithm is proposed with speed-up techniques to quickly generate an optimal solution. To this end, the original model is decomposed into several sub-problems that can be exactly solved by using a forward dynamic programming algorithm. Based on actual data from the Beijing subway’s Yizhuang line, numerical experiments are conducted to investigate the effectiveness of the asymmetric operation strategy, to identify managerial insights on the integrated optimization, and to evaluate the performance of the proposed methodology.

Suggested Citation

  • Mo, Pengli & D’Ariano, Andrea & Yang, Lixing & Veelenturf, Lucas P. & Gao, Ziyou, 2021. "An exact method for the integrated optimization of subway lines operation strategies with asymmetric passenger demand and operating costs," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 283-321.
  • Handle: RePEc:eee:transb:v:149:y:2021:i:c:p:283-321
    DOI: 10.1016/j.trb.2021.05.009
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    Cited by:

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    4. Lu, Yahan & Yang, Lixing & Yang, Hai & Zhou, Housheng & Gao, Ziyou, 2023. "Robust collaborative passenger flow control on a congested metro line: A joint optimization with train timetabling," Transportation Research Part B: Methodological, Elsevier, vol. 168(C), pages 27-55.
    5. Wang, Xuekai & D’Ariano, Andrea & Su, Shuai & Tang, Tao, 2023. "Cooperative train control during the power supply shortage in metro system: A multi-agent reinforcement learning approach," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 244-278.
    6. Yuan, Yin & Li, Shukai & Yang, Lixing & Gao, Ziyou, 2022. "Real-time optimization of train regulation and passenger flow control for urban rail transit network under frequent disturbances," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    7. 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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