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%, +∞).


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    Title :

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


    Contributors:
    Li, Binbin (author) / Yao, Enjian (author)

    Conference:

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


    Published in:

    CICTP 2017 ; 1886-1896


    Publication date :

    2018-01-18




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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