This study proposes a traffic flow prediction method based on graph attention network to address the needs of urban traffic congestion and planning. Our method first constructs a city traffic network graph, with road segments, intersections, and other traffic elements as nodes, considering traffic correlations and spatial structure to establish relationships between nodes. Then, utilizing the graph attention mechanism, it effectively captures the transmission of traffic information between nodes and the degree of correlation, thereby more accurately predicting future traffic flow. Experimental results demonstrate that our method achieved significant performance improvements in traffic prediction on real traffic datasets, proving its effectiveness and feasibility in traffic flow prediction tasks. This study provides new insights and methods for urban traffic management and planning.


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    Title :

    Traffic flow prediction based on graph attention network


    Contributors:
    Na, Jing (editor) / He, Shuping (editor) / Zhu, Wenyan (author) / Kong, Hoiio (author) / Cai, Wenzheng (author) / Zhu, Wenhao (author)

    Conference:

    International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024) ; 2024 ; Yinchuan, China


    Published in:

    Proc. SPIE ; 13259


    Publication date :

    2024-09-04





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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