Traffic flow prediction based on graph attention network

Wenyan Zhu; Hoiio Kong; Wenzheng Cai; Wenhao Zhu

doi:10.1117/12.3039334

4 September 2024 Traffic flow prediction based on graph attention network

Wenyan Zhu, Hoiio Kong, Wenzheng Cai, Wenhao Zhu

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132592D (2024) https://doi.org/10.1117/12.3039334
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenyan Zhu, Hoiio Kong, Wenzheng Cai, and Wenhao Zhu "Traffic flow prediction based on graph attention network", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132592D (4 September 2024); https://doi.org/10.1117/12.3039334

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