The scientific classification of metro-bus hubs can obtain the commonality of traffic characteristics in various categories. Based on the POI and smart card (SC) data, this paper captures the hub’s attribute and surrounding environment variables by multi-source data fusion technology. Principal component analysis is made to extract factors from variables. Using K-means cluster analysis, 113 metro-bus intermodal hubs in Nanjing are classified into five categories. In addition, this paper analyzes the demand characteristics of hubs. The classification results show three categories of hubs have a great influence on the traffic network, which are the city-center, metro-bus oriented, and traffic-expansion hubs. The main reasons for the delay in the hub are analyzed, and the optimization for the transfer design on different categories of hubs is provided.


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    Titel :

    A Classification Method of Metro-Bus Intermodal Hubs Based on Principal Component Analysis and Cluster Analysis


    Beteiligte:
    Guo, Haomin (Autor:in) / Chen, Jun (Autor:in) / Ye, Jiao (Autor:in) / Du, Zexingjian (Autor:in)

    Kongress:

    19th COTA International Conference of Transportation Professionals ; 2019 ; Nanjing, China


    Erschienen in:

    CICTP 2019 ; 1420-1432


    Erscheinungsdatum :

    02.07.2019




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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