Suburban passenger demand typically exhibits unbalanced distributions, characterized by highly heterogeneous spatial mobility patterns and flow sizes. Designing an efficient, regular, and stable train timetable is critical for long-term use in suburban railways. This study adopts optimization methods to tackle the multi-period train timetable problem, considering both periodic and aperiodic nature to accommodate strongly heterogeneous train services. An MP-TTP model is constructed to minimize travel time and maximize timetable robustness. Additionally, the study introduces a multi-scenario demand input method to address the uncertainty of daily passenger demand. A hybrid particle swarm optimization and genetic algorithm (PSO-GA) is designed. The proposed approach is verified by a real-world case study. The results suggest that the multi-scenario-based train timetable generally outperforms the independent scenario-based method in terms of improving robustness. Furthermore, the multi-periodic train plan can effectively balance efficiency and accessibility while considering regularity.


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    Titel :

    A Multi-Periodic Train Timetable Optimization Model for Suburban Railway Considering Uncertain Passenger Demand


    Beteiligte:
    Shao, Juan (Autor:in) / Sun, Lishan (Autor:in) / Xu, Yan (Autor:in)

    Kongress:

    24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China


    Erschienen in:

    CICTP 2024 ; 1142-1154


    Erscheinungsdatum :

    11.12.2024




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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