Cluster analysis is a good tool to classify urban rail transit stations and figure out the difference between stations. By using clustering analysis, the intention of this paper is to find the difference between different kinds of metro stations. The method we used in this study is known as K-medoids, the input of which is decided by principal components analysis (PCA), and the efficiency of the K-medoids algorithm is guaranteed by a density-based method because it can select the best start values. The data used was the passenger entry flow of the metro network during five workdays of one week in Beijing, China. By applying this method on the data, the metro stations are clustered into six categories, and the stations are put on the map, so the difference between the stations was determined.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Metro Stations Classification Based on Clustering Analysis—A Case Study of Beijing Metro


    Beteiligte:
    Lu, Dongliang (Autor:in) / He, Min (Autor:in) / Shuai, Chunyan (Autor:in)

    Kongress:

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


    Erschienen in:

    CICTP 2019 ; 1707-1717


    Erscheinungsdatum :

    02.07.2019




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Classification of Beijing Metro Stations Based on Multi-source Data and Gaussian Mixture Model

    Wan, Feng / Miao, Jianrui / Wang, Shuling | Springer Verlag | 2020


    Classification of Beijing Metro Stations Based on Multi-source Data and Gaussian Mixture Model

    Wan, Feng / Miao, Jianrui / Wang, Shuling | British Library Conference Proceedings | 2020


    Beijing opens three metro lines

    British Library Online Contents | 2008


    Electro Acoustic Analysis in Metro Stations

    Yilmaztürk, L. / Pervane, S. | DataCite | 2019