Predicting urban traffic flow is a challenging task, due to the complicated spatio-temporal dependencies on traffic networks. Urban traffic flow usually has both short-term neighboring and long-term periodic temporal dependencies. It is also noticed that the spatial correlations over different traffic nodes are both local and non-local. What’s more, the traffic flow is affected by various external factors. To capture the non-local spatial correlations, we propose a Dilated Attentional Graph Convolution (DAGC). The DAGC utilizes a dilated graph convolution kernel to expand the nodes’ receptive field and exploit multi-order neighborhood. Technically, the lower-order neighborhood corresponds to local spatial dependencies, while the higher-order neighborhood corresponds to non-local spatial dependencies between nodes. Based on DAGC, a Multi-Source Spatio-Temporal Network (MS-Net) is designed, which suffices to integrate long-range historical traffic data as well as multi-modal external information. MS-Net consists of four components: a spatial feature extraction module, a temporal feature fusion module, an external factors embedding module, and a multi-source data fusion module. Extensive experiments on three real traffic datasets demonstrates that the proposed model performs well on both the public transportation networks, road networks, and can handle large-scale traffic networks in particular the Beijing bus network which has more than 4,000 traffic nodes.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction


    Beteiligte:
    Fang, Shen (Autor:in) / Prinet, Veronique (Autor:in) / Chang, Jianlong (Autor:in) / Werman, Michael (Autor:in) / Zhang, Chunxia (Autor:in) / Xiang, Shiming (Autor:in) / Pan, Chunhong (Autor:in)


    Erscheinungsdatum :

    01.07.2022


    Format / Umfang :

    4942787 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Spatio-Temporal AutoEncoder for Traffic Flow Prediction

    Liu, Mingzhe / Zhu, Tongyu / Ye, Junchen et al. | IEEE | 2023


    Global spatio‐temporal dynamic capturing network‐based traffic flow prediction

    Haoran Sun / Yanling Wei / Xueliang Huang et al. | DOAJ | 2023

    Freier Zugriff

    Global spatio‐temporal dynamic capturing network‐based traffic flow prediction

    Sun, Haoran / Wei, Yanling / Huang, Xueliang et al. | Wiley | 2023

    Freier Zugriff

    Deep Spatio-Temporal Convolutional Neural Network for City Traffic Flow Prediction

    Zhou, Zhiyuan / Qin, Yanjun / Luo, Haiyong | IEEE | 2021