As a critical foundation for train traffic management, a train stop plan is associated with several other plans in high-speed railway train operation strategies. The current approach to train stop planning in China is based primarily on passenger demand volume information and the preset high-speed railway station level. With the goal of efficiently optimising the stop plan, this study proposes a novel method that uses machine learning techniques without a predetermined hypothesis and a complex solution algorithm. Clustering techniques are applied to assess the features of the service nodes (e.g., the station level). A modified Markov decision process (MDP) is conducted to express the entire stop plan optimisation process considering several constraints (service frequency at stations and number of train stops). A restrained MDP-based stop plan model is formulated, and a numerical experiment is conducted to demonstrate the performance of the proposed approach with real-world train operation data collected from the Beijing-Shanghai high-speed railway.


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    Titel :

    Passenger Demand-Oriented High-Speed Train Stop Planning with Service-Node Features Analysis


    Beteiligte:
    Fanxiao Liu (Autor:in) / Qiyuan Peng (Autor:in) / Gongyuan Lu (Autor:in) / Guangyuan Zhang (Autor:in)


    Erscheinungsdatum :

    2020




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt






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