There is congestion in urban rail transit carriage, which directly exerts an effect on the comfort of passengers and operational efficiency of urban transportation networks. Based on different physical and psychological requirements of passengers and the calculations on passengers’ rate of mixture in urban rail transit carriage, with the investigation results of passengers’ choice behavior of standing position, age, gender, and other indicators, density of standing passenger’s evaluation criteria is established based on calculation of passengers mixed degree. To accurately identify the number of passengers, gender, and age in the key points and regions of carriage, the paper selects the method of regional probability estimation and deep learning. According to the output model, it can be judged whether or not the carriage is congested. The method can rapidly identify the congestion of carriage situation and determine whether the type of carriage congestion belongs to frequent or disequilibrium congestion.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Congested Situation Identification of Urban Rail Transit Carriage Based on Deep Learning


    Beteiligte:
    Wang, Bo (Autor:in) / Yang, Guixin (Autor:in) / Zhou, Jinyao (Autor:in) / Ye, Mao (Autor:in) / Cheng, Hui (Autor:in)

    Kongress:

    20th COTA International Conference of Transportation Professionals ; 2020 ; Xi’an, China (Conference Cancelled)


    Erschienen in:

    CICTP 2020 ; 2851-2862


    Erscheinungsdatum :

    2020-08-12




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Congested Situation Identification of Urban Rail Transit Carriage Based on Deep Learning

    Wang, Bo / Yang, Guixin / Zhou, Jinyao et al. | TIBKAT | 2020


    Novel rail transit train carriage

    XU CHAO / TAN YUQIN / ZHANG RONG | Europäisches Patentamt | 2020

    Freier Zugriff

    Urban rail transit train carriage fire high-temperature alarm system

    LU ZHIYONG / LIU CHUNJIE / ZHANG XINGYAN et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Timetabling for a congested urban rail transit network based on mixed logic dynamic model

    Hao, Sijia / Song, Rui / He, Shiwei | Taylor & Francis Verlag | 2022


    Rail transit carriage locking wheelchair device

    LIU BIAO | Europäisches Patentamt | 2021

    Freier Zugriff