Accurate prediction of traffic flow plays an important role in ensuring public traffic safety and solving traffic congestion. Because graph convolutional neural network (GCN) can perform effective feature calculation for unstructured data, doing research based on GCN model has become the main way for traffic flow prediction research. However, most of the existing research methods solving this problem are based on combining the graph convolutional neural network and recurrent neural network for traffic prediction. Such research routines have high computational cost and few attentions on impaction of different time and nodes. In order to improve the accuracy of traffic flow prediction, a gated attention graph convolution model based on multiple spatiotemporal channels was proposed in this paper. This model takes multiple time period data as input and extracts the features of each channel by superimposing multiple gated temporal and spatial attention modules. The final feature vector is obtained by means of weighted linear superposition. Experimental results on two sets of data show that the proposed method has good performance in precision and interpretability.


    Access

    Download


    Export, share and cite



    Title :

    Traffic Flow Prediction Based on Multi-Spatiotemporal Attention Gated Graph Convolution Network


    Contributors:
    Yun Ge (author) / Jian F. Zhai (author) / Pei C. Su (author)


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Road network traffic flow prediction method based on gated loop graph convolution attention network

    REN YILONG / CHEN YUE / MA TIAN et al. | European Patent Office | 2023

    Free access


    Traffic flow prediction method based on graph attention convolution network

    ZHENG HONG / ZHANG SIKAI / LIU JIAMOU et al. | European Patent Office | 2020

    Free access

    GSTGAT: Gated spatiotemporal graph attention network for traffic demand forecasting

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

    Free access

    GSTGAT: Gated spatiotemporal graph attention network for traffic demand forecasting

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

    Free access