Modeling the traffic accident probability of Urban roads based on an improved Bayesian network

Urban road traffic accidents are severely threatening the safety of human life and property. In this study, the Random Forest (RF) algorithm was used to identify the significant risk factors of road section accidents and intersection accidents, and a probability prediction model of urban road traffic accidents based on the improved Bayesian network (IBN) was constructed. Next, a method is proposed to identify the accident-prone points on urban roads. The study results showed that: (1) There are significant differences in the factors influencing the probability of accidents at road sections and intersections. (2) When different combinations of influencing factors change, the probability of accidents at road sections and intersections also changes. Finally, based on the data from road sections and intersections in Tianjin, accident probability thresholds of 10.28% and 6.69% respectively have been determined, which can accurately identify the accident-prone points on urban roads.