Congestion Propagation Identification and Prediction Using Self Attention–Based Diffusion Convolutional Approach

Abstract

Traffic congestion frequently propagates among the interconnected road networks over time, driven by spatial and temporal factors. Identifying and predicting these patterns is crucial for effective public transport management and urban planning. Existing systems often fail to accurately capture the complex diffusion properties of traffic flow and spatial dependencies, resulting in less reliable predictions. This study utilizes a propagation probability matrix to identify congestion propagation patterns and finds traffic behavior over 24 h, revealing critical insights into congestion trends in a selected road network. In addition, to overcome the remaining limitations, we propose a novel self attention–based diffusion convolutional network (SADCN) that effectively predicts traffic congestion propagation. The proposed model incorporates key spatial relations, including adjacency, diffusion, and propagation probability matrices, to improve the understanding of congestion dynamics. To demonstrate the significance of SADCN, we compare its performance with several recent graph-based models, including fully connected long short-term memory (FC-LSTM), diffusion convolutional recurrent neural network (DCRNN), and attention-based spatial-temporal graph convolutional network (ASTGCN). Compared with existing models, the proposed method achieved superior results, with an accuracy of 0.976, a precision of 0.950, a recall of 0.942, and F 1 -score of 0.946 for 50 epochs. Furthermore, the model outperformed state-of-the-art methods at shorter intervals, such as 5, 10, and 20 epochs, highlighting its faster convergence and efficiency in predicting traffic congestion propagation patterns to improve public transport systems.