Traffic flow prediction based on temporal multigraph convolutional neural network

Wenying Guan; Jiajia Zheng; Yetao Wang; Zhengyuan Li; Hao Liu

doi:10.1117/12.3024208

14 February 2024 Traffic flow prediction based on temporal multigraph convolutional neural network

Wenying Guan, Jiajia Zheng, Yetao Wang, Zhengyuan Li, Hao Liu

Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 130180Z (2024) https://doi.org/10.1117/12.3024208
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China

Abstract

Traffic flow prediction plays an important role in intelligent transportation systems(ITS), but is challenged by the spatiotemporal complexity of traffic flow connections. In order to integrate the traffic temporal correlation, spatial correlation and semantic correlation in road network models, we propose a deep learning framework, Temporal Multi-Graph Convolutional Neural Network(T-MGCN) for traffic flow prediction. Firstly, we identify several semantic correlations and encode the non-Euclidean spatial correlations and heterogeneous semantic correlations between roads into multiple graphs. These correlations were modeled by a multi-graph convolutional neural network. Next, a recurrent neural network model is used to learn the dynamic characteristics of the traffic flow to obtain temporal correlations; Finally, a fully connected neural network is used to fuse spatio-temporal correlations and semantic correlations

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

Citation Download Citation

Wenying Guan, Jiajia Zheng, Yetao Wang, Zhengyuan Li, and Hao Liu "Traffic flow prediction based on temporal multigraph convolutional neural network", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 130180Z (14 February 2024); https://doi.org/10.1117/12.3024208

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