Accurate traffic flow prediction is of great practical significance to alleviate road congestion. Existing methods ignore the hidden dynamic associations between road nodes, and for the problem of difficulty capturing the dynamic spatio-temporal features of traffic flow in the prediction process, a novel model based on the interactive dynamic spatio-temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed, which consists of an interactive dynamic graph convolutional network (IDGCN) structure with a spatio-temporal convolutional block (ST-Conv block) and a probabilistic sparse self-attention mechanism block (ProbSSAtt block). Among them, the IDGCN synchronizes the dynamic spatio-temporal features captured by interaction sharing, and the ST-Conv block is combined with the ProbSSAtt block to effectively capture the long short-term temporal features of the traffic flow. In addition, to effectively find the hidden dynamic associations between road network nodes, a dynamic graph convolutional network generated by the fusion of an adaptive neighbor matrix and a learnable neighbor matrix was constructed. Experimental results demonstrate that the prediction performance of the IDG-PSAtt model outperforms the baseline model under the evaluation criteria and experimental settings given in this paper. In the PEMS-BAY dataset, the mean absolute error and root mean square error of the IDG-PSAtt to 60 min are improved by 15.49% and 12.10%, compared with the state-of-the-art model, respectively.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic Flow Prediction Based on Interactive Dynamic Spatio-Temporal Graph Convolution with a Probabilistic Sparse Attention Mechanism


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Chen, Linlong (Autor:in) / Chen, Linbiao (Autor:in) / Wang, Hongyan (Autor:in) / Zhang, Hong (Autor:in)


    Erscheinungsdatum :

    2024-02-02




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Traffic flow prediction method based on interactive dynamic graph convolution and probability sparse attention

    ZHANG HONG / CHEN LINBIAO / CHEN LINLONG et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Spatio-Temporal Graph Attention Convolution Network for Traffic Flow Forecasting

    Liu, Kun / Zhu, Yifan / Wang, Xiao et al. | Transportation Research Record | 2024


    Temporal attention aware dual-graph convolution network for air traffic flow prediction

    Cai, Kaiquan / Shen, Zhiqi / Luo, Xiaoyan et al. | Elsevier | 2022


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

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


    Traffic flow prediction method based on graph attention convolution network

    ZHENG HONG / ZHANG SIKAI / LIU JIAMOU et al. | Europäisches Patentamt | 2020

    Freier Zugriff