Prediction of Traffic Accident Severity Based on Random Forest

Abstract

This paper used the data of automobile traffic accidents from 2018 to 2020 in the Chinese National Automobile Accident In-Depth Investigation System. The prediction features of traffic accident severity are innovated. Four accident features that did not participate in the importance ranking were added: accident location, accident form, road information, and collision speed. Eight accident features (engine capacity, hour of day, age of vehicle, month of year, day of week, age band of drivers, vehicle maneuver, and speed limit) have been used in previous studies. Random forest was used to rank the importance of 12 accident features, and 7 important accident features were finally adopted. By comparing the algorithms and optimizing the results, the prediction model of traffic accident degree with higher accuracy is finally obtained.