As a typical spatiotemporal problem, there are three main challenges in traffic forecasting. First, the road network is a nonregular topology, and it is difficult to extract complex spatial dependence accurately. Second, there are short- and long-term dependencies between traffic dates. Third, there are many other factors besides the influence of spatiotemporal dependence, such as semantic characteristics. To address these issues, we propose a spatiotemporal DeepWalk gated recurrent unit model (ST-DWGRU), a deep learning framework that fuses spatial, temporal, and semantic features for traffic speed forecasting. In the framework, the spatial dependency between nodes of an entire road network is extracted by graph convolutional network (GCN), whereas the temporal dependency between speeds is captured by a gated recurrent unit network (GRU). DeepWalk is used to extract semantic information from road networks. Three publicly available datasets with different time granularities of 15, 30, and 60 min are used to validate the short- and long-time prediction effect of this model. The results show that the ST-DWGRU model significantly outperforms the state-of-the-art baselines.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Spatiotemporal DeepWalk Gated Recurrent Neural Network: A Deep Learning Framework for Traffic Learning and Forecasting


    Beteiligte:
    Jian Yang (Autor:in) / Jinhong Li (Autor:in) / Lu Wei (Autor:in) / Lei Gao (Autor:in) / Fuqi Mao (Autor:in)


    Erscheinungsdatum :

    2022




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt





    Fast Spatiotemporal Learning Framework for Traffic Flow Forecasting

    Guo, Canyang / Chen, Chi-Hua / Hwang, Feng-Jang et al. | IEEE | 2023


    GSTGAT: Gated spatiotemporal graph attention network for traffic demand forecasting

    Shuilin Yao / Huizhen Zhang / Chenxi Wang et al. | DOAJ | 2024

    Freier Zugriff

    GSTGAT: Gated spatiotemporal graph attention network for traffic demand forecasting

    Yao, Shuilin / Zhang, Huizhen / Wang, Chenxi et al. | Wiley | 2024

    Freier Zugriff

    ST-AGRNN: A Spatio-Temporal Attention-Gated Recurrent Neural Network for Traffic State Forecasting

    Jian Yang / Jinhong Li / Lu Wei et al. | DOAJ | 2022

    Freier Zugriff