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Stochastic optimization of an urban rail timetable under time‐dependent and uncertain demand

Author

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  • Masoud Shakibayifar
  • Erfan Hassannayebi
  • Hossein Jafary
  • Arman Sajedinejad

Abstract

Urban rail planning is extremely complex, mainly because it is a decision problem under different uncertainties. In practice, travel demand is generally uncertain, and therefore, the timetabling decisions must be based on accurate estimation. This research addresses the optimization of train timetable at public transit terminals of an urban rail in a stochastic setting. To cope with stochastic fluctuation of arrival rates, a two‐stage stochastic programming model is developed. The objective is to construct a daily train schedule that minimizes the expected waiting time of passengers. Due to the high computational cost of evaluating the expected value objective, the sample average approximation method is applied. The method provided statistical estimations of the optimality gap as well as lower and upper bounds and the associated confidence intervals. Numerical experiments are performed to evaluate the performance of the proposed model and the solution method.

Suggested Citation

  • Masoud Shakibayifar & Erfan Hassannayebi & Hossein Jafary & Arman Sajedinejad, 2017. "Stochastic optimization of an urban rail timetable under time‐dependent and uncertain demand," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(6), pages 640-661, November.
    • Handle: RePEc:wly:apsmbi:v:33:y:2017:i:6:p:640-661
    DOI: 10.1002/asmb.2268
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    Cited by:

    1. 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.
    2. Zhan, Shuguang & Xie, Jiemin & Wong, S.C. & Zhu, Yongqiu & Corman, Francesco, 2024. "Handling uncertainty in train timetable rescheduling: A review of the literature and future research directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    3. Zhou, Li & Yang, Xin & Wang, Huan & Wu, Jianjun & Chen, Lei & Yin, Haodong & Qu, Yunchao, 2020. "A robust train timetable optimization approach for reducing the number of waiting passengers in metro systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    4. Han, Zhenyu & Han, Baoming & Li, Dewei & Ning, Shangbin & Yang, Ruixia & Yin, Yonghao, 2021. "Train timetabling in rail transit network under uncertain and dynamic demand using Advanced and Adaptive NSGA-II," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 65-99.
    5. Lian, Deheng & Mo, Pengli & D’Ariano, Andrea & Gao, Ziyou & Yang, Lixing, 2024. "Energy-saving time allocation strategy with uncertain dwell times in urban rail transit: Two-stage stochastic model and nested dynamic programming framework," European Journal of Operational Research, Elsevier, vol. 317(1), pages 219-242.

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