Traffic flow forecasting plays a crucial role in the construction of intelligent transportation. The aims of this paper are to fully exploit the spatial correlation between nodes in a traffic network and to compensate for the inability of graph-based deep learning methods to model multiple relationship types, resulting in inadequate extraction of spatially correlated information about the traffic network. In this paper, we propose a deep spatio-temporal recurrent evolution network based on the graph convolution network (STREGCN) for heterogeneous graphs. Specifically, we transform the traffic network into a multi-relational heterogeneous graph to improve the information representation of the graph. This allows our model to capture multiple types of spatially relevant information. In the temporal dimension, we use one-dimensional causal convolution based on the gated linear unit to extract the temporal correlation information of the traffic flow. In addition, we designed the output of the spatio-temporal convolution module to obtain the final traffic flow predictions after a fully connected layer. Experiments on real datasets illustrate the effectiveness of the proposed STREGCN model and show the importance of representing information through heterogeneous graphs for the task of traffic flow prediction.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Spatio-Temporal Heterogeneous Graph-Based Convolutional Networks for Traffic Flow Forecasting


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:
    Ma, Zhaobin (author) / Lv, Zhiqiang (author) / Xin, Xiaoyang (author) / Cheng, Zesheng (author) / Xia, Fengqian (author) / Li, Jianbo (author)


    Publication date :

    2023-12-12




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Spatio‐temporal adaptive graph convolutional networks for traffic flow forecasting

    Ma, Qiwei / Sun, Wei / Gao, Junbo et al. | Wiley | 2023

    Free access

    Spatio‐temporal adaptive graph convolutional networks for traffic flow forecasting

    Qiwei Ma / Wei Sun / Junbo Gao et al. | DOAJ | 2023

    Free access


    Spatio-temporal Dynamic Graph Convolutional Probability Sparse Attention Networks for Traffic Flow Forecasting

    Chen, Linlong / Chen, Linbiao / Wang, Hongyan et al. | Springer Verlag | 2025


    Traffic Flow Forecasting of Graph Convolutional Network Based on Spatio-Temporal Attention Mechanism

    Zhang, Hong / Chen, Linlong / Cao, Jie et al. | Springer Verlag | 2023