Highlights We proposed an efficient and effective data-mining procedure that models the travel patterns of transit riders using the transit smart card data. Transit riders’ trip chains are identified based on the temporal and spatial characteristics of smart card transaction data. A Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to detect each transit rider’s historical travel patterns. The K-Means++ clustering algorithm and the rough-set theory are jointly applied to clustering and classifying the travel pattern regularities.

    Abstract To mitigate the congestion caused by the ever increasing number of privately owned automobiles, public transit is highly promoted by transportation agencies worldwide. A better understanding of travel patterns and regularity at the “magnitude” level will enable transit authorities to evaluate the services they offer, adjust marketing strategies, retain loyal customers and improve overall transit performance. However, it is fairly challenging to identify travel patterns for individual transit riders in a large dataset. This paper proposes an efficient and effective data-mining procedure that models the travel patterns of transit riders in Beijing, China. Transit riders’ trip chains are identified based on the temporal and spatial characteristics of their smart card transaction data. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm then analyzes the identified trip chains to detect transit riders’ historical travel patterns and the K-Means++ clustering algorithm and the rough-set theory are jointly applied to cluster and classify travel pattern regularities. The performance of the rough-set-based algorithm is compared with those of other prevailing classification algorithms. The results indicate that the proposed rough-set-based algorithm outperforms other commonly used data-mining algorithms in terms of accuracy and efficiency.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Mining smart card data for transit riders’ travel patterns


    Beteiligte:
    Ma, Xiaolei (Autor:in) / Wu, Yao-Jan (Autor:in) / Wang, Yinhai (Autor:in) / Chen, Feng (Autor:in) / Liu, Jianfeng (Autor:in)


    Erscheinungsdatum :

    2013-07-18


    Format / Umfang :

    12 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Travel Assistance Device (TAD) to Help Transit Riders

    P. L. Winters / S. Barbeau / N. Labib | NTIS | 2010



    Travel Time and Transfer Analysis Using Transit Smart Card Data

    Jang, Wonjae | Transportation Research Record | 2010