Dynamic Spatiotemporal Graph Wavelet Network for Traffic Flow Prediction

Real-time and high-precision traffic flow prediction plays a crucial role in transportation management, contributing to control dispatch and reducing traffic congestion. Due to the complex and dynamic characteristics of the traffic flow, traffic flow prediction remains challenging. Previous work often ignores some dynamic and momentary spatial information, and predicting the traffic flow of a target road segment in the long-term horizon is a difficult problem. To address these issues, we propose a novel deep learning framework, termed long short-term structural spatiotemporal information fusion graph wavelet network (LSSTF-GWN), to capture momentary dynamic spatiotemporal correlation and make long-term predictions. The LSSTF-GWN model integrates graph wavelets network (GWN) with temporal gated convolution networks into graph convolution network to construct a multigraph architecture to address the complex spatiotemporal correlations in traffic flow data. The GWN extracts the instantaneous and global features of the spatial information by designing different adjacency matrices. The LSSTF-GWN not only considers the fixed distance between graphs but also builds long-term dynamic graphs for the inner relationships of the nodes to reflect contextual and global information. The proposed method is evaluated using three metrics, i.e., mean absolute error, root-mean-square error, and mean absolute percentage error, on two real-world data sets from the Caltrans Performance Measurement System (PeMS). The experimental results demonstrate the superior performance of our method in long-term traffic flow prediction.