As an important part of the railway operation program, high-speed railway stop schedule plan focuses on meeting the spatial distribution of passenger transport demand in traditional research, and less consideration is given to the time fluctuation of passenger demand, which leads to the mismatch between capacity and demand in different periods, and affects passengers' satisfaction with the timeliness of high-speed railway. We propose an optimization method for high-speed railway stop schedule plan based on time-segmented passenger demand, to improve the matching degree between the train plan capacity and passenger demand. Firstly, simulate the process of passengers choosing the most satisfying train in different periods, and build stop schedule plan optimization model which aims to minimize total dissatisfaction with arrival time of all passengers. Secondly, genetic algorithm (GA) is used in the solution of the model. Take the reciprocal of the objective function of the model as the fitness and keep the best individual to the next generation by elitist strategy. Finally, apply the model to Nanchang-Fuzhou High-Speed Railway Line. As the maximum of stop choice change increases, the total dissatisfaction of passengers with arrival time would decline gradually. The dissatisfaction of passengers with the optimal scheme was reduced by 2.51 minutes per capita. The research results show that this methodology could enhance the matching between train diagram and passenger demand in different periods, and improve passengers’ satisfaction effectively.


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    Titel :

    Train Stop Schedule Plan Optimization of High-Speed Railway Based on Time-Segmented Passenger Flow Demand


    Beteiligte:
    Xu, Ruihua (Autor:in) / Wang, Fangsheng (Autor:in) / Zhou, Feng (Autor:in)

    Kongress:

    Sixth International Conference on Transportation Engineering ; 2019 ; Chengdu, China


    Erschienen in:

    ICTE 2019 ; 899-906


    Erscheinungsdatum :

    13.01.2020




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch