Traffic flow forecasting is an important research subject related to social livelihood and economic development. How to improve the accuracy of traffic flow forecasting has been widely concerned by people. This paper proposes a traffic flow prediction model based on Transformer. The model uses GAT, which can dynamically aggregate spatial features, to model the spatial dependence of traffic flow. The self-attention mechanism in Transformer is used to adaptively capture long-term dependencies from traffic flow data to model time dependencies of traffic flow. Spatial-temporal coding is embedded in the feature vector of input data. The traffic flow prediction model is tested on six real-world traffic flow datasets, and compared with some classic baseline models, the traffic flow prediction model proposed in this paper has achieved good prediction performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on Traffic Flow Forecasting Based on Spatial-Temporal Network


    Contributors:
    Chen, Xun (author) / Liang, Jinsu (author) / Deng, Linyi (author) / Xie, Tianyi (author)


    Publication date :

    2025-03-21


    Size :

    1383781 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Research on Traffic Flow Forecasting Based on Dynamic Spatial-Temporal Transformer

    Zhang, Hong / Wang, Hongyan / Zhang, Xijun et al. | Transportation Research Record | 2023


    Multi-Step Spatial-Temporal Fusion Network for Traffic Flow Forecasting*

    Dong, Honghui / Meng, Ziying / Wang, Yiming et al. | IEEE | 2021


    ClusterST: Clustering Spatial–Temporal Network for Traffic Forecasting

    Luo, Guiyang / Zhang, Hui / Yuan, Quan et al. | IEEE | 2023


    Spatial-Temporal Graph-Based Transformer Model for Traffic Flow Forecasting

    Wang, Qichao / He, Guojun / Lu, Peiyu et al. | IEEE | 2022


    Dynamic Spatial–Temporal Convolutional Networks for Traffic Flow Forecasting

    Zhang, Hong / Kan, Sunan / Zhang, XiJun et al. | Transportation Research Record | 2023