Research on Traffic Risk Prediction Model Based on Multimodal Spatiotemporal Fusion Deep Learning

Abstract

Aiming at the problem that single-modal features are difficult to depict the complex spatiotemporal evolution law in urban traffic risk prediction, a multimodal spatiotemporal deep learning model fusing Multi-view Graph Convolutional Network (GCN), Transformer and Temporal Convolutional Network (TCN) is proposed to realize binary traffic risk prediction at the 10×10 grid level. Based on the traffic dataset of Area C in New York City (NYC), the study integrates grid time series, static features and multi-type adjacency matrix data. Strategies such as outlier processing, dynamic threshold binarization and feature normalization are adopted to solve the problems of data quality and imbalance between positive and negative samples. Meanwhile, gradient clipping, weighted loss and early stopping strategies are combined to ensure the stability of model training. Experimental results show that the model achieves an F1 score of 0.745, an AUC-ROC of 0.798 and an AUC-PR of 0.756 on the test set. It can effectively capture the spatial correlation and temporal dependence features of traffic risks, maintain good prediction performance in the unbalanced data scenario, and meet the actual business needs of urban traffic risk prediction.