Research on Accident Type Prediction for New Energy Vehicles Based on the AS-Naive Bayes Algorithm

Developing new energy vehicles (NEVs) is a key strategy for achieving low-carbon and sustainable transportation. However, as the number of NEVs increases, traffic accidents involving these vehicles have risen sharply. To explore the characteristics of NEV accident types, and assess the occurrence of different accident types, this study proposes an accident type analysis and prediction method based on a novel Naive Bayes algorithm integrating the additive smoothing and synthetic minority over-sampling technique (AS-Naive Bayes). First, typical accident data (such as scraping, collisions, run-overs, rollovers, and battery fires/explosions) are extracted from the traffic management platform. A statistical analysis is then conducted to assess the relationships between accident types and factors including road conditions, time, vehicle status, and driver behavior. Moreover, to reduce the influence of irrelevant factors, Chi-square testing and Mutual Information are used to select features strongly associated with accident types. After that, to address the challenges of limited sample size and imbalanced distribution of accident types, this study proposes an accident type prediction method based on the AS–Naive Bayes algorithm, which integrates the Synthetic Minority Over-sampling Technique (SMOTE) and additive smoothing. Finally, five-fold cross-validation results show that the proposed method achieves a prediction accuracy of 84.8%, outperforming Support Vector Machine (SVM, 74.1%) and Long Short-Term Memory (LSTM, 79.8%), and standard Naive Bayes models, demonstrating its effectiveness in accurately identifying NEV accident types.