A Geometric Deep Learning Approach to Traffic Flow Shockwave Prediction on Freeways Using Vehicle Trajectory Data and HD Map

Accurately predicting the occurrence, amplitude, and propagation speed of traffic flow shockwaves are essential for dynamic traffic control to mitigate traffic congestion. However, traditional approaches primarily rely on macroscopic traffic data from loop detectors, which often fail to capture the fine-grained interactions among vehicles and roadway features, leading to limited prediction accuracy. To address this gap, we propose a novel vehicle trajectory prediction method based on geometric deep learning (GNN-T), integrating a graph neural network with an attention mechanism. The graph neural network captures vehicle and lane features, and the attention mechanism models interactions between vehicles and between vehicles and lanes, significantly enhancing prediction performance. The evaluation results show that GNN-T outperforms conventional car-following models and other baseline methods, achieving improvements in trajectory prediction accuracy by 7.1% to 9.5%. Furthermore, a traffic flow shockwave prediction model, based on wavelet transform and predicted trajectories, is developed to predict shockwave occurrence, timing, propagation speed, and amplitude. Compared to the Intelligent Driver Model and traditional traffic flow detection methods, our approach demonstrates superior performance, with accuracy rates in shockwave prediction up to 93.5% in validation datasets. These results indicate the potential of the proposed model for practical applications in traffic flow management.