Accurate traffic flow forecasting is important for intelligent traffic management and control. To address the inability of existing methods to simultaneously capture the spatiotemporal dependence of traffic flows and the significant trend differences between predicted and real values, a traffic flow forecasting method based on a Spatiotemporal Interactive Dynamic Adaptive Convolutional Network (STIDAAG) is proposed. First, an interactive learning structure is designed to dynamically aggregate the spatiotemporal characteristics of the hidden nodes in the traffic network. Second, a dynamic adaptive graph generation network is designed based on the current and historical state to further capture the dynamic spatiotemporal characteristics. Finally, the adversarial graph convolutional network is used to optimize the loss for adversarial training to reduce the trend difference between the predicted and true values. The results of the experiment on four publicly available datasets indicate that STIDAAG outperforms both typical and advanced methods in terms of predictive performance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Spatiotemporal interactive dynamic adaptive adversarial graph convolution network for traffic flow forecasting


    Weitere Titelangaben:

    TRANSPORTMETRICA B: TRANSPORT DYNAMICS
    H. ZHANG ET AL.


    Beteiligte:
    Zhang, Hong (Autor:in) / Chen, Linbiao (Autor:in) / Cao, Jie (Autor:in)


    Erscheinungsdatum :

    31.12.2024


    Format / Umfang :

    20 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    TRAFFIC FLOW FORECASTING METHOD BASED ON MULTI-MODE DYNAMIC RESIDUAL GRAPH CONVOLUTION NETWORK

    HUANG XIAOHUI / YE YUMING / LING JIAHAO et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Graph transformer based dynamic multiple graph convolution networks for traffic flow forecasting

    Hu, Yongli / Peng, Ting / Guo, Kan et al. | Wiley | 2023

    Freier Zugriff

    Graph transformer based dynamic multiple graph convolution networks for traffic flow forecasting

    Yongli Hu / Ting Peng / Kan Guo et al. | DOAJ | 2023

    Freier Zugriff

    Spatio-Temporal Graph Attention Convolution Network for Traffic Flow Forecasting

    Liu, Kun / Zhu, Yifan / Wang, Xiao et al. | Transportation Research Record | 2024