The categorization of high-speed railway passenger value reflects the demand differentiation from passengers. This is essential for optimizing high-speed railway price strategy and the revenue. This paper extracts RFM of passenger value as the core features, analyzes the weight for each core feature based on AHP and high-speed railway expert strategy, and adopts fuzzy clustering algorithm for clustering analysis, finally comes out the passenger value segmentation model. Based on the passenger flow for Beijing-Shanghai high-speed railway, this paper divides the passengers into five categories including high-value passengers, growth passengers, commuters, potential passengers, and general passengers. This passenger value segmentation and portraits can be applied in revenue optimization and provide better experience for passengers.


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    Titel :

    High-Speed Railway Passenger Categorization Based on Fuzzy Clustering


    Beteiligte:
    Li, Li-Hui (Autor:in) / Zhu, Jian-Sheng (Autor:in) / Shan, Xing-Hua (Autor:in) / Xu, Yan (Autor:in)

    Kongress:

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


    Erschienen in:

    CICTP 2020 ; 2469-2482


    Erscheinungsdatum :

    09.12.2020




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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