Analysis of Factors Causing Urban Road Traffic Accidents Based on XGboost Algorithm

Abstract

To further strengthen road traffic safety management and improve the accuracy of early warning systems for road traffic safety, a model for factors causing urban road traffic accidents based on the XGBoost algorithm is proposed. Firstly, SMOTE is used to process the unbalanced data, including supplementing the missing values, deleting the duplicate values, and visualizing the data. Then the prediction model is built by the XGBoost algorithm. By comparing and analyzing the results with the LR model, linear SVM model, DT model and Lightgbm model, the average accuracy rate of the XGBoost model reaches 0.979. Based on the XGBoost algorithm, the analysis of the factors causing urban road traffic accidents has better prediction performance, which can provide a reliable reference for preventing traffic accidents.