The metro passenger route choice, influenced by both train schedule and time constraints, is important to metro operation and management. Smart card data (Automatic Fare Collection (AFC) data in metro system) including inbound and outbound swiping time are useful for analysis of the characteristics of passengers’ route choices in metro while they could not reflect the property of train schedule directly. Train schedule is used in this paper to trim smart card data through removing inbound and outbound walking time to/from platforms and waiting time. Thus, passengers’ pure travel time in accord with trains’ arrival and departure can be obtained. Synchronous clustering (SynC) algorithm is then applied to analyze these processed data to calculate passenger route choice probability. Finally, a case study was conducted to illustrate the effectiveness of the proposed algorithm. Results showed the proposed algorithm works well to analyze metro passenger route choice. It was shown that passenger route choice during both peak period and flat period could be clustered automatically, and noise data are isolated. The probability of route choice calculated through SynC algorithm can be used to revise traditional model results.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering


    Beteiligte:
    Wei Li (Autor:in) / Qin Luo (Autor:in) / Qing Cai (Autor:in) / Xiongfei Zhang (Autor:in)


    Erscheinungsdatum :

    2018




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems

    Zhao, Juanjuan / Zhang, Fan / Tu, Lai et al. | ArXiv | 2016

    Freier Zugriff




    Predicting Express Train Choice of Metro Passengers from Smart Card Data

    Kim, Kyung Min / Hong, Sung-Pil / Ko, Suk-Joon et al. | Transportation Research Record | 2016