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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    Title :

    Traffic flow forecast with urban transport network


    Contributors:
    Wang, Di (author) / Zhang, Qi (author) / Wu, Shunyao (author) / Li, Xinmin (author) / Wang, Ruixue (author)


    Publication date :

    2016-08-01


    Size :

    559667 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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