Urban Traffic Accident Severity Prediction and Influencing Factors Analysis Based on Machine Learning

Urban traffic accident will cause national economic and property losses, people’s lives are threatened, and traffic accident is a hot issue concerned by all countries. Therefore, it is important for traffic management departments to study the main factors affecting urban traffic accidents and to accurately predict the severity of traffic accidents. Using the data set of Seattle City from 2004 to 2020 released by SDOT, In this paper, the traditional machine learning model, artificial neural network MLP model, Random Forest (RF) model and Gradient Boosting model were compared to predict the severity of traffic accidents. Among them, RF and Gradient Boosting models were better, with an accuracy of $75.9 \%$. By output important factors affecting the severity of traffic accidents and partial dependence plots, the model was more explanatory. Based on location, time, number of people, number of vehicles, collision type, weather, road conditions and other factors, traffic management departments can accurately predict the severity of traffic accidents and reduce property losses and casualties.