Driving risk prediction method for elderly drivers considering data imbalance and feature extraction

With the aging of society, the increase in the number of elderly drivers poses a potential hazard to road traffic safety. Therefore, accurately predicting the severity of possible traffic accidents of elderly drivers is crucial to ensure the safety of drivers and passengers. In this paper, a hybrid model based on CTGAN-ResNet-XGBoost network is proposed for classifying the severity of accidents of elderly drivers. The model was trained and tested using traffic accident data of the United States from 2018—2022. The hybrid model first generates a small amount of categorical data via the CTGAN network to address the dataset’s category imbalance. Then, the balanced dataset is transformed into feature images using the DeepInsight method and feature extraction is performed using the ResNet network to improve the feature recognition ability of the classification model. Finally, the XGBoost model is used to classify the severity of the accident and SHAP method is used to analyze the main features affecting the accident. The superior performance of the hybrid model is verified through experimental comparative analysis. The experimental results show that the hybrid model has a significant advantage in the prediction of driving risk for elderly drivers, and the causes of accidents for elderly drivers are different from younger drivers, and the characteristics of speed, seatbelt use, and driver’s age are the main factors affecting the severity of accidents. The results of this study improve the accuracy and reliability of traffic accident severity prediction and provide more scientific support for traffic safety management.