Expressway traffic flow prediction based on wavelet noise reduction and time graph convolution

Jiaxing Liang; Yuanli Gu

doi:10.1117/12.3015782

20 February 2024 Expressway traffic flow prediction based on wavelet noise reduction and time graph convolution

Jiaxing Liang, Yuanli Gu

Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 130643O (2024) https://doi.org/10.1117/12.3015782
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China

Abstract

Accurate traffic flow prediction is crucial for making informed decisions regarding travel route selection and mitigating traffic congestion. This paper introduces a WGCG model that addresses the impact of traffic flow noise by combining wavelet transform with GCN and GRU models. The Sym6 wavelet basis is utilized to decompose traffic flow into two layers, effectively reducing noise. The road network's topological structure features are described using an undirected graph and adjacency matrix, with GCN extracting spatial rules and GRU mining hidden time correlation information from historical traffic flow data. The WGCG model integrates these modules to capture the dynamic patterns of traffic flow comprehensively. The model's performance is evaluated on a real dataset from Beijing's Second Ring Road, comparing prediction results with baseline models like HA, SVR, GRU, and GCN. Experimental findings indicate that the WGCG model achieves a significant increase in prediction accuracy, reducing errors by 27.9%, 22.3%, and 21.7% respectively, compared to the second-best model SVR. Ablation experiments further demonstrate that the WGCG model outperforms combined models utilizing only specific modules, confirming its feasibility and superiority.

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

Citation Download Citation

Jiaxing Liang and Yuanli Gu "Expressway traffic flow prediction based on wavelet noise reduction and time graph convolution", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 130643O (20 February 2024); https://doi.org/10.1117/12.3015782

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KEYWORDS

Data modeling

Wavelets

Denoising

Performance modeling

Education and training

Roads

Matrices

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