Due to the scarcity of railway network capacity resources, fluctuations in the transportation market still lead to overstocking of railcar flow in certain periods or regions, thereby causing numerous temporary or periodic dynamic capacity bottlenecks in the railway network. In this paper, the contradiction between supply and demand of the future railway network is deduced and predicted, and the identification method of railway dynamic bottleneck is studied. A discrete time-space network is established to depict the transportation process of railcar flow on the railway network. A railcar flow distribution model is established with the aim of minimizing the total vehicle hours, taking into account constraints such as initial railcars, loading plan, station storage capacity, and segment transport capacity. Based on this, a dynamic capacity bottleneck identification method is proposed by computing bottleneck indicators of the railway network. To validate the effectiveness of the proposed approach, a case study is conducted on a railway network operated by a Railway Bureau, S31 and S2 are identified as dynamic capacity bottlenecks. Meanwhile, after the adjustment of transportation plan, the capacity utilization rate of each station and line during the period is maintained below 90% in the case of 200 railcars not being transported. The results demonstrate that the proposed method is effective in predicting the railcar flow congestion.


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    Titel :

    Identification Method of Railway Dynamic Capacity Bottleneck


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jia, Limin (Herausgeber:in) / Wang, Yanhui (Herausgeber:in) / Easa, Said (Herausgeber:in) / Liu, Jiayin (Autor:in)

    Kongress:

    International Conference on SmartRail, Traffic and Transportation Engineering ; 2024 ; Chongqing, China October 25, 2024 - October 27, 2024



    Erscheinungsdatum :

    19.07.2025


    Format / Umfang :

    19 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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