Disclosed is a traffic flow forecasting method based on a multi-mode dynamic residual graph convolution network, including following steps: constructing a relationship matrix and an adaptive matrix to learn the site dependence relationship for historical traffic data of traffic stations; using multi-mode dynamic graph convolution to extract traffic characteristics corresponding to different traffic modes; embedding the graph convolution into the gated cyclic neural network to realize the combination of space dependence and time dependence of traffic flow; connecting the network by using the dynamic residual, and combining the input traffic data with the decoding data to obtain the final forecasting value. The application utilizes two different methods to construct adjacency matrix, effectively captures traffic flow characteristics corresponding to different traffic modes, and dynamically fuses traffic flow characteristics of two different modes.


    Access

    Download


    Export, share and cite



    Title :

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


    Contributors:
    HUANG XIAOHUI (author) / YE YUMING (author) / LING JIAHAO (author) / JIANG YUAN (author) / XIONG LIYAN (author)

    Publication date :

    2023-10-19


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



    Traffic flow forecasting method based on multi-mode dynamic residual graph convolution network

    HUANG XIAOHUI / YE YUMING / LING JIAHAO et al. | European Patent Office | 2025

    Free access

    Contrastive learning for traffic flow forecasting based on multi graph convolution network

    Kan Guo / Daxin Tian / Yongli Hu et al. | DOAJ | 2024

    Free access

    Contrastive learning for traffic flow forecasting based on multi graph convolution network

    Guo, Kan / Tian, Daxin / Hu, Yongli et al. | Wiley | 2024

    Free access

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

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

    Free access