Spatiotemporal Graph Attention Networks for Urban Traffic Flow Prediction

• Published in: IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops (Print) pp. 340 - 345
• Main Authors: Zhao, Yuanpeng, Xu, Yepeng, He, Xitao, Zhang, Dengyin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12.09.2022
• Subjects: attention mechanism; Feature extraction; graph convolution; Logic gates; Predictive models; Regulation; Roads; spatiotemporal data; Spatiotemporal phenomena; Time series analysis; Traffic prediction
• ISSN: 2166-9589
• DOI: 10.1109/PIMRC54779.2022.9977794

Short-term traffic flow forecasting is a challenging subject, and it is of great significance for travel route planning, traffic regulation and other directions. Traffic flow is affected by the topological structure of the urban road network and the dynamic changes of time series, and has both temporal and spatial characteristics. However, how to extract the correlation between spatiotemporal features is still a challenging task. In response to these problems, this paper proposes a new deep learning model (GAGRU), which models the traffic road network through a graph attention network (GAT), extracts the spatial dependencies in the traffic flow and uses a gated recurrent unit (GRU) to focus on the Characteristics of traffic over time. In addition, we fuse the traffic flow features of multiple sequences to consider the periodic characteristics of traffic flow. In this paper, the model is experimentally validated using real-world datasets, and the final experimental results show that the prediction accuracy of the model is superior to other baseline methods.