Analysis of traffic safety risk state of highway reconstruction and expansion project based on dynamic Bayesian network

Abstract

In order to accurately analyze the causes of accidents in highway reconstruction and expansion project and grasp the law of traffic safety state change, a traffic safety situation analysis method based on dynamic Bayesian network model is proposed. Firstly, on the basis of summarizing the existing research results and expert experience, eight key influencing factors reflecting state evolution were selected, and the dynamic Bayesian network structure was established. Secondly, based on fuzzy evidence theory, an improved mixed interval evidence synthesis rule was designed to solve the negative impact of high conflict evidence on parameter learning. Finally, by using Markov theory and time factor management method, the state transition probability is determined, and the dynamic Bayesian network analysis model of traffic safety situation of highway reconstruction and expansion project is constructed. The empirical analysis is carried out by taking the traffic accident occurred during the construction of a highway reconstruction and expansion project as an example. The results show that the traffic safety situation analysis method based on dynamic Bayesian network model can accurately predict and describe the changes of safety situation with time before and after traffic safety accidents. The analysis of the sensitivity of each factor under different situation levels shows that the traffic composition (the proportion of large vehicles) is the most sensitive. Through THE DIAGNOSIS and analysis of the cause of the traffic accident, it is shown that the main reason of the accident is that the temporary traffic safety facilities are not installed in time, which leads to the great difference of vehicle speed, which is consistent with the actual situation at the scene when the accident occurs. The research results provide a scientific basis for taking the corresponding safety control measures.