Traffic flow prediction has become a hot spot in the intelligent transportation system study. In this paper, novel methods are proposed to predict traffic flow. We divide 24 hours into 4 stages according to the bimodal distribution of traffic flow, and integrate topology features of urban traffic network into 4 typical machine learning methods. Experiments on the traffic flow of Qinhuangdao city demonstrate the effectiveness and potential of the proposed methods.


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    Titel :

    Traffic flow forecast with urban transport network


    Beteiligte:
    Wang, Di (Autor:in) / Zhang, Qi (Autor:in) / Wu, Shunyao (Autor:in) / Li, Xinmin (Autor:in) / Wang, Ruixue (Autor:in)


    Erscheinungsdatum :

    01.08.2016


    Format / Umfang :

    559667 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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