A Deep Learning Approach on Traffic States Prediction of Freeway Weaving Sections Under Adverse Weather Conditions

Freeway weaving sections’ states under adverse weather exhibit characteristics of randomness, vulnerability, and abruption. A deep learning-based model is proposed for traffic state identification and prediction, which can be used to formulate proactive management strategies. According to traffic characteristics under adverse weather, a hybrid model combining Random Forest and an improved k-prototypes algorithm is established to redefine traffic states. Traffic state prediction is accomplished using the Weather Spatiotemporal Graph Convolution Network (WSTGCN) model. WSTGCN decomposes flows into spatiotemporal correlation and temporal variation features, which are learned using spectral graph convolutional networks (GCNs). A Time Squeeze-and-Excitation Network (TSENet) is constructed to extract the influence of weather by incorporating the weather feature matrix. The traffic states are then predicted using Gated Recurrent Unit (GRU). The proposed models were tested using data under rain, fog, and strong wind conditions from 201 weaving sections on China’s G5 and G55 freeway, and U.S. I-5 and I-80 freeway. The results indicated that the freeway weaving sections’ states under adverse weather can be classified into seven categories. Compared with other baseline models, WSTGCN achieved a 3.8–8.0% reduction in Root Mean Square Error, a 1.0–3.2% increase in Equilibrium Coefficient, and a 1.4–3.1% improvement in Accuracy Rate.