Abstract:

With the continual rise in the number of motor vehicles in urban areas, traffic congestion has worsened, adversely affecting environmental protection and urban operational efficiency. Consequently, accurately predicting traffic congestion is of critical importance for traffic management and optimization. However, existing research still faces limitations in modeling the dynamic, time-varying characteristics of traffic flow as well as the complex interactions among road segments. To address these challenges, a Gated Spatiotemporal Convolutional Network model based on Graph Neural Networks is proposed to more effectively capture and predict traffic congestion. First, an improved K-Means clustering algorithm is employed to divide the raw data into multiple congestion-state categories, which are then incorporated as auxiliary features to enhance feature representation. Next, a Gated Temporal Convolutional Network is introduced to capture the temporal properties and dynamic dependencies in traffic data, and a Dynamic Adaptive Gated Graph Convolutional Network is constructed to achieve feature fusion and dynamic weight allocation through a signal generation module and a dual-modulation mechanism, thereby facilitating effective extraction of spatiotemporal features. Finally, residual connections are incorporated to improve training stability, and skip connections are utilized to integrate multi-level and multi-scale features. Experimental results on real-world PeMS08 and PeMS04 datasets demonstrate that the proposed model achieves superior prediction accuracy compared with other baseline methods.

Key words: traffic congestion prediction, graph neural networks, dynamic adaptive gating, clustering algorithm, gated temporal convolutional network