Considering the congestion in the metro, the operator usually uses congestion degree information to guide passenger flow which can alleviate the operation pressure. However, the current congestion degree classifications are mostly based on subjective judgment, without fully considering passengers’ psychological perceptions under different load factors. This paper aims to set up a reasonable classification of congestion degree for urban rail transit system. First, passengers’ perception differences of congestion degree are analyzed from the views of different properties (i.e., age, gender, travel purpose, and travel time), based on the questionnaire data collected in Beijing Metro. Then, the distributions of passengers’ congestion degree perception under different load factors are calculated, and the load factors are clustered based on hierarchical cluster analysis. Finally, the congestion degree for urban rail transit is classified into four levels (i.e., comfortable, slight crowded, crowded, and heavy crowded), corresponding to the load factor interval (0, 40%), (40%, 75%), (75%, 100%), and (100%, +∞).


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Congestion Degree Classification for Urban Rail Transit System Based on Hierarchical Cluster Analysis


    Beteiligte:
    Li, Binbin (Autor:in) / Yao, Enjian (Autor:in)

    Kongress:

    17th COTA International Conference of Transportation Professionals ; 2017 ; Shanghai, China


    Erschienen in:

    CICTP 2017 ; 1886-1896


    Erscheinungsdatum :

    18.01.2018




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Analysis method of congestion degree of rail transit station platform

    Jian, Jinshan / Liang, Xiao | British Library Conference Proceedings | 2022



    Congestion avoidance routing in urban rail transit networks

    He, Kun / Wang, Junjie / Lianbo Deng et al. | IEEE | 2014


    Study on the Traffic Congestion Index of Urban Rail Transit Stations

    Wang, Bo / Li, Chen / Bai, Yunyun et al. | ASCE | 2018


    Research on Time-Based Fare Discount Strategy for Urban Rail Transit Peak Congestion

    Ding, Xiaobing / Hong, Chen / Wu, Jinlong et al. | Springer Verlag | 2023

    Freier Zugriff