City Regional Traffic Flow Prediction Based on Spatiotemporal Multi-View Attention Residual Network

Authors

Jing Chen, School of Information Technology and Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
Guowei Yang, School of Information Technology and Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
Zhaochong Zhang, School of Information Technology and Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
Wei Wang, School of Information Technology and Engineering, Tianjin University of Technology and Education, Tianjin 300222, China

Abstract

Abstract: However, efficiently and comprehensively capturing the complex spatiotemporal correlations within urban traffic flow presents a key challenge. Existing research methods struggle to fully capture these spatiotemporal dependencies. To address these issues, we propose a novel end-to-end deep learning framework called the spatiotemporal multi-view attention residual network (ST-MVAR) for predicting traffic flow in urban areas. we integrate the proximity, periodicity, trend, and external factors of traffic flow as inputs to the network. This network employs skip connections to form a multi-layer nested residual network structure. Additionally, we design a Multi-View Extension module to capture spatial dependencies of traffic flow at various distances and introduce a coordinate attention network to effectively establish the spatiotemporal correlations within traffic flow. Furthermore, we use the k-means clustering method to obtain patterns for each cross-sectional time traffic flow and incorporate them as additional features to further enhance the model's predictive accuracy. Experimental results demonstrate that ST-MVAR achieves higher performance with fewer parameters, 14.2% lower RMSE compared to the best previous methods.

Recommended Citation

Chen, Jing; Yang, Guowei; Zhang, Zhaochong; and Wang, Wei (2025) "City Regional Traffic Flow Prediction Based on Spatiotemporal Multi-View Attention Residual Network," Journal of System Simulation: Vol. 37: Iss. 3, Article 5.
DOI: 10.16182/j.issn1004731x.joss.23-1313
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss3/5

First Page

607

Last Page

622

CLC

TP183

Recommended Citation

Chen Jing, Yang Guowei, Zhang Zhaochong, et al. City Regional Traffic Flow Prediction Based on Spatiotemporal Multi-view Attention Residual Network[J]. Journal of System Simulation, 2025, 37(3): 607-622.

Corresponding Author

Yang Guowei

DOI

10.16182/j.issn1004731x.joss.23-1313