It is essential for metro operations to identify typical daily OD matrix from the long-term automatic fare collection (AFC) data. A daily metro travel demand pattern identification method is proposed, using eight-month AFC data from Shanghai metro networks in China. For the high dimension problem of the long-term OD matrix, principal component analysis (PCA) was applied to reduce the dimension of the OD matrix. Then, fuzzy C-means clustering was used to identify the daily metro travel demand patterns. The results show metro operating days can be clustered into three typical daily travel categories, namely weekdays, weekends, and holidays. The metro OD travel flow data processing framework proposed in this study identifies the typical operating daily travel patterns of complex networks, integrating dimensionality reduction technology and clustering methods, which provides a new solution for understanding the daily mobility structure, which is helpful for short-term travel demand forecasting and metro operation schedule.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Metro Travel Pattern Identification: Cluster Analysis Based on Daily OD Matrix


    Beteiligte:
    Gu, Mingxiang (Autor:in) / Duan, Zhengyu (Autor:in)

    Kongress:

    22nd COTA International Conference of Transportation Professionals ; 2022 ; Changsha, Hunan Province, China


    Erschienen in:

    CICTP 2022 ; 2670-2679


    Erscheinungsdatum :

    2022-09-08




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Metro Train Operation Plan Analysis Based on Station Travel Time Reliability

    Ruihua Xu / Fangsheng Wang / Feng Zhou | DOAJ | 2021

    Freier Zugriff

    Impact of individual daily travel pattern on value of time

    Paleti, Rajesh / Vovsha, Peter / Givon, Danny et al. | Online Contents | 2015


    Optimization of metro wheel reprofiling based on cluster analysis

    Lin, Zonghui / Lu, Zhenggang / Yong, Xinjian et al. | SAGE Publications | 2022


    Impact of individual daily travel pattern on value of time

    Paleti, Rajesh / Vovsha, Peter / Givon, Danny et al. | Online Contents | 2015