Urban road networks have complex spatial and temporal correlations, driving a surge of research interest in spatial-temporal traffic flow prediction. However, prior approaches often overlook the temporal-scale differentiation of spatial-temporal features, limiting their ability to extract complex structural information. In this work, we design the multibranch adaptive fusion graph convolutional network (MBAF-GCN) that explicitly exploits the prior spatial-temporal characteristics at different temporal scales, and each branch is responsible for extracting spatial-temporal features at a specific scale. Besides, we design the spatial-temporal feature fusion (STFF) module to refine the prediction results. Based on the multibranch complementary features, the module adopts a coarse-to-fine fusion strategy, incorporating different spatial-temporal scale features to obtain recalibrated prediction results. Finally, we evaluate the MBAF-GCN using two real-world traffic datasets. Experimentally, the newly designed multibranch can efficaciously utilize the prior information of different temporal scales. Our MBAF-GCN achieved better performance in the comparative model, indicating its potential and validity.


    Access

    Download


    Export, share and cite



    Title :

    Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction


    Contributors:


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Traffic flow prediction method based on adaptive dynamic fusion graph convolutional network

    ZHANG SHUAI / YU WANGZHI / LEE HAE KWANG et al. | European Patent Office | 2024

    Free access

    Space-time adaptive dynamic graph convolutional network traffic flow prediction method

    CUI WENTIAN / LOU JUNGANG / SHEN QING et al. | European Patent Office | 2024

    Free access

    Traffic flow prediction method based on time-varying fusion graph convolutional network

    ZUO KAIZHONG / WANG RUI / WANG CHEN et al. | European Patent Office | 2024

    Free access

    Attention-Based Spatiotemporal Adaptive Graph Diffusion Convolutional Network For Traffic Flow Prediction

    He, Qiansong / Xia, Dawen / Li, Jianjun et al. | Transportation Research Record | 2025


    Traffic flow prediction method based on graph convolutional network

    XU HUI / MENG FANYU / REN QIANQIAN et al. | European Patent Office | 2025

    Free access