A spatiotemporal neural network model for city traffic prediction

Ying Zhang; Fan Zhang

doi:10.1117/12.3012080

21 December 2023 A spatiotemporal neural network model for city traffic prediction

Ying Zhang, Fan Zhang

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129703R (2023) https://doi.org/10.1117/12.3012080
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

The problem of traffic congestion is a significant challenge faced by smart city development. The causes of traffic congestion are complex and diverse, involving both routine factors and random factors such as unexpected events. These factors present challenges for accurate prediction of urban traffic flow. In this paper, we propose a global factor-aware spatiotemporal neural network model called GFA-STNet which addresses the cyclic, spatiotemporal, and random characteristics of urban traffic flow. We utilize deep learning to capture the spatiotemporal correlations of urban flow, employ residual networks to capture the temporal features of nearby time, periodic time, and trend time in the variation of traffic flow over time. Graph convolutional networks are used to extract the adjacency relationships between regions and combine them with external factors to extract the global spatiotemporal relationships of traffic flow, which are used to predict the traffic flow of each region. Experiments are conducted on real-world datasets, and the results show that the proposed model improves the accuracy of predictions, compared to classical traffic prediction models.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ying Zhang and Fan Zhang "A spatiotemporal neural network model for city traffic prediction", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129703R (21 December 2023); https://doi.org/10.1117/12.3012080

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