Road Traffic Accident Risk Prediction Based on Random Forest Model

With the increase in global car ownership, the incidence of road traffic accidents is also rising. For the prediction and research of road traffic accidents risk, this paper considers weather factors and roadside facility factors, processes missing values in the samples, balances the sampling using the SMOTE method, and determines the optimal parameters. A roadside traffic accident risk prediction model based on Random Forest is established, mainly to predict the severity of traffic accidents and the duration of the impact of accidents. It is found that in weather factors, atmospheric pressure is the dominant factor for predicting accident severity, while precipitation is the dominant factor for predicting duration. Among the roadside facility factors, the prediction of accident severity and the duration of impact are both highly correlated with the presence of intersections and junctions nearby. Finally, this paper compares the fitting effect of Random Forest algorithm, Neural Network algorithm, Adaboost algorithm, Support Vector Machine algorithm, and XGboost algorithm on the model. The R2value of the Random Forest model is 0.833, which is the highest among all models. Therefore, the Random Forest model has the best fitting effect on the research problem.