The urban signalized road network, characterized by its dynamic and complex nature due to frequent signal control adjustments and unpredictable demand fluctuations, presents significant challenges for predicting lane-level traffic flow. This study introduces the innovative MGCN-TAMA model, which addresses these challenges by integrating multi-graph convolutional networks with a temporal-aware multi-head attention mechanism. The proposed model employs three types of adjacency matrices-a geographical matrix, a signal matrix, and an attention matrix-to capture the complex spatial dependencies among various traffic approaches. Additionally, the model utilizes temporal-aware multi-head attention to discern the nonlinear correlations in traffic variations over time. Tested on a real-world dataset from Tongxiang City, the MGCN-TAMA model significantly outperforms traditional models. Notably, in the first 30-minute prediction interval, our model achieves the lowest Mean Absolute Error, with 2.5649 vehicles per 5-minute span. These results underscore the effectiveness of combining graph-based methods with advanced attention mechanisms to enhance the accuracy of predicting lane-level traffic volumes in urban networks.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Integrating Multi-Graph Convolutional Networks and Temporal-Aware Multi-Head Attention for Lane-Level Traffic Flow Prediction in Urban Networks


    Beteiligte:
    Sun, Fengmei (Autor:in) / Zhu, Hong (Autor:in) / Tang, Keshuang (Autor:in) / Xiong, Yingchang (Autor:in) / Tan, Chaopeng (Autor:in) / Tang, Zhixian (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    1029603 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Traffic Flow Prediction Using Novel Spatial-Temporal Multi-Head Attention Graph Convolution Networks

    Cheng, Yuan / Peng, Cheng / Wang, Ze et al. | Transportation Research Record | 2024



    Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction

    Lv, Mingqi / Hong, Zhaoxiong / Chen, Ling et al. | IEEE | 2021



    Spatio-temporal graph attention networks for traffic prediction

    Ma, Chuang / Yan, Li / Xu, Guangxia | Taylor & Francis Verlag | 2024