Analysis and Prediction of Traffic Accidents Based on Interpretable Spatial Machine Learning: A Case Study in California

Traffic accidents are one of the leading causes of death and disability, as well as a significant source of economic losses for society. However, the nonlinear and heterogeneous relationships between environmental factors and traffic accidents are complex and difficult to comprehend. This study constructs an explainable spatial machine learning framework using a geographically weighted support vector machine (GW‐SVM) model to address issues of nonlinearity, spatial heterogeneity, and interpretability. Based on a large‐scale traffic accident dataset and multisource big data, this study provides both global and local explanations for the nonlinear relationships in California, USA. The study finds that (1) humidity plays a more important role in the relationship between environmental factors and traffic accident severity; (2) all environmental variables, including both natural and socioeconomic variables, exhibit nonlinear and threshold effects on traffic accidents; and (3) compared to the existing models, the GW‐SVM model performs better in predicting the severity of traffic accidents on urban roads. The results of this study are significant for reducing traffic accident risks.