A Novel Traffic Congestion Detection Model Based on Congestion Factors and Spatio-Temporal Attention Mechanisms

Timely congestion detection supports traffic monitoring and management, while spatial and temporal variations in road network conditions pose challenges to reliable detection. Most of the existing research on congestion detection focuses on the current traffic conditions of the road network and do not pay much attention to the factors with spatio-temporal characteristics, which usually change according to the situation of road network congestion. To this end, this paper proposes a novel spatio-temporal progressive convolution graph model for traffic congestion detection. The model introduces congestion factors and a spatio-temporal attention mechanism module to enhance the ability to model spatio-temporal factors and improve the effectiveness of congestion detection, thereby enabling the rapid identification of congestion areas in the road network. The experimental results on METR-LA and PeMS-Bay show five-level congestion classification accuracies of 75.3% and 78.9%, respectively, under the fixed speed-based labeling rule used in this study. The results support comparative evaluation under the fixed speed-based labeling rule used in this study; the congestion thresholds require local calibration before operational use on U.S. highways.