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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    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 :

    2016-08-01


    Format / Umfang :

    559667 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Urban Traffic Flow Forecast Based on FastGCRNN

    Ya Zhang / Mingming Lu / Haifeng Li | DOAJ | 2020

    Freier Zugriff

    MAS-Based Urban Rail Transport Ridership Forecast System and Traffic Generation Analysis

    Ding, Fei ;Zhao, Jing Bo ;Bei, Shao Yi | Trans Tech Publications | 2010


    The Forecast of Dynamic Traffic Flow

    Yuan, Z. / Li, W. / Liu, H. et al. | British Library Conference Proceedings | 2000


    Traffic Distribution Forecast of Urban External Traffic Hub Based on Uncertainty

    Zhu, Shunying / Guan, Juxiang / Peng, Wuxiong et al. | ASCE | 2008


    Traffic forecast

    Online Contents | 1998