MAT-WGCN: Traffic Speed Prediction Using Multi-Head Attention Mechanism and Weighted Adjacency Matrix

Abstract

Traffic prediction is important in applications such as traffic management, route planning, and traffic flow optimization. Traffic speed prediction is an important part of traffic forecasting, which has always been a challenging problem due to the complexity and dynamics of traffic systems. In order to predict traffic speed more accurately, we propose a traffic speed prediction model based on a multi-head attention mechanism and weighted adjacency matrix: MAT-WGCN. MAT-WGCN first uses GCN to extract the road spatial features in the weighted adjacency matrix, and it uses GRU to extract the correlation between speed and time from the original features. Then, the spatial features extracted by GCN and the temporal features extracted by GRU are fused, and a multi-head attention mechanism is introduced to integrate spatiotemporal features, collect and summarize spatiotemporal road information, and realize traffic speed prediction. In this study, the prediction performance of MAT-WGCN was tested on two real datasets, EXPY-TKY and METR-LA, and compared with the performance of traditional methods such as HA and SVR that do not combine spatial features, as well as T-GCN, A3T-GCN, and newer methods such as GCN and NA-DGRU that combine spatial features. The experimental results demonstrate that MAT-WGCN can capture the temporal and spatial characteristics of road conditions, thus enabling accurate traffic speed predictions. Furthermore, the incorporation of a multi-head attention mechanism significantly enhances the robustness of our model.