Intelligent transportation systems (ITS) include large numbers of traffic sensors that collect enormous quantities of data. The historical traffic data is in fact capable of proving abundant of information that can aid in the development of improved current traffic control. However, many bad observations are hidden in databases with various faces. Data mining tools such as the k-means clustering approach are the keys to explore the information in the traffic data. This paper uses the k-means clustering approach to identify time-of-day (TOD) break points based on the historical data to support the design of signal timing plan. A case study using an intersection corridor was conducted to demonstrate the method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Using Data Mining to Analyze the Traffic Data of Intersection


    Beteiligte:
    Yang, Jun (Autor:in) / Cheng, Wei (Autor:in) / Li, Xuemin (Autor:in) / Yuan, Manrong (Autor:in)

    Kongress:

    Second International Conference on Transportation Engineering ; 2009 ; Southwest Jiaotong University, Chengdu, China



    Erscheinungsdatum :

    2009-07-29




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Using Data Mining to Analyze the Traffic Data of Intersection

    Yang, J. / Cheng, W. / Li, X. et al. | British Library Conference Proceedings | 2009


    Using Data Mining to Analyze Archived Traffic Related Data

    Amado, Vanessa / Virkler, Mark R. | ASCE | 2006


    Using Data Mining to Analyze Archived Traffic Related Data

    Amado, V. / Virkler, M. R. / American Society of Civil Engineers | British Library Conference Proceedings | 2005


    Using data mining techniques for optimizing traffic signal plans at an urban intersection

    Last, Mark / Avrahami, Gil / Kandel, Abraham | Tema Archiv | 2011


    Traffic Intersection Detection Using Floating Car Data

    Hu, Rong / Xia, Ye / Hsu, Chih-Yu et al. | IEEE | 2020