Risk prediction and factor analysis of rider’s injury severity in passenger car and E-bike accidents based on interpretable machine learning

Abstract

Casualties and property losses caused by the passenger car and electric bicycle crash accidents increased year by year. Assessment of the relevant risk factors of injury severity in passenger car and electric bicycle crashes could help to mitigate crash severity. This study uses an emerging machine learning method to predict the relationship between the risk factors and bicyclist accident injury severity in passenger car-electric bicycle collision accidents. The model’s performance is compared and evaluated based on accuracy, precision, recall, F1-Score, area under curve (AUC), and receiver operating characteristic curve (ROC). An interpretable machine learning framework Shapley additive explanations (SHAP) is used to further analyze the relationship between risk factors and bicyclist injury severity. It is found that we can adopt the light gradient boosting machine (LightGBM) algorithm after hyper-parameter optimization to get the highest accuracy (94.85%), precision (95.2%), recall (94.9%), F1-Score (95%), and AUC (0.993) based on the accident data of electric bicycles and passenger cars in the China in-depth accident study dataset from 2014 to 2018. The model can be used to assess new accident cases based on the model learning rate. There are some new findings in the aspects of bicyclists’ physical factors and electric vehicle characteristics. The throwing distance of the bicyclist has a positive impact on the injury severity. The bicyclist is more likely to suffer more serious injuries in a crash accident when the bicyclist is male, or shorter. Electric bicycles have a smaller handlebar width. In general, the lower handlebar height is, or lower saddle height is, the more serious the bicyclist’s injury is. Safety training for drivers can help reduce the injury severity in crash accidents and improve traffic safety.