B.B. Gong

Pages: 133-144

Abstract
Traditional road safety prediction methods have low recall rate and poor prediction accuracy. This paper proposes a road traffic safety risk prediction method based on improved random forest. First, collect road traffic data, such as static data, traffic dynamic data, other traffic related data and accident data. Then, the abnormal road traffic data are identified based on chaos method, and the abnormal data are repaired with grey GM (1, n) model. Finally, the random forest algorithm is improved by optimizing the similarity measurement method and the optimal addition principle, and the improved random forest algorithm is used to predict the road traffic safety risk. The results show that the maximum recall rate of this method is 95%, and the prediction accuracy of road traffic safety risk is 93%.
Keywords: improving random forest; road traffic safety; risk projection; grey gm(1;n) model; similarity measure