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Smart city article details

Title	A Spatiotemporal Neural Network Model For City Traffic Prediction
ID_Doc	4890
Authors	Zhang Y.; Zhang F.
Year	2023
Published	Proceedings of SPIE - The International Society for Optical Engineering, 12970
DOI	http://dx.doi.org/10.1117/12.3012080
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 SPIE.
Author Keywords	Convolutional networks; Residual networks; Spatiotemporal features; Traffic flow

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