Bayesian Network Model Diagnosis And Reasoning Research On Traffic Accidents In Mountainous Areas

Mountainous highways are affected by special factors such as their topography,geology and climate.Once a traffic accident occurs,the loss of mountainous areas is often more severe than that of plain areas.This paper takes 2365 traffic accidents in 2016-2018 in the three sections of Huxian District,Ningshan District and Qinling District of the West Han Dynasty of Jingkun Expressway as the background.This paper takes the bibliometric method,the interpretative structural model method and the Bayesian network research as the core of the research.This paper takes Bayesian network modeling to analyze the causes and prediction analysis of mountain highway traffic accidents as research ideas.The main research work of this paper is as follows:Firstly,based on the bibliometric method,the Chinese and English literatures on the analysis of the influencing factors of mountainous highway traffic accidents within a certain period of time are searched and analyzed.Then,according to the frequency of the influencing factors appearing in the literature and the value and utilization rate of the article,the key influencing factors of the accident are identified.Finally,according to the first-class factors of people,vehicles,environment and roads,9 of the 13 secondary factors retrieved are finally selected as key influencing factors.Then,based on the interpretation structure model method,a hierarchical structure model is established for the nine key influencing factors of mountain highway traffic accidents and the causal correlation between the analysis factors.The model is divided into four layers,and the factors are affected layer by layer.Whether the four factors of bridge and tunnel section,road line shape,long longitudinal slope and average daily traffic flow are at the bottom of the model are the most fundamental causes of traffic accidents.After that,the Bayesian network principle,structure learning and parameter learning methods are explained.Based on the interpretative structural model and K2 algorithm,a hybrid Bayesian network structure model for mountainous highway traffic accidents is established.Using Netica software for parameter learning,the node conditional probability is obtained.The stability and accuracy of the Bayesian network are verified by the K-fold cross-validation method.Finally,the accident prediction analysis and the cause diagnosis of the accident are carried out on the Bayesian network of mountain highway traffic accidents.Combine the traffic accident instance to explain the maximum possibility and analyze the sensitivity of the network node.The shortcomings of the research on the subject and the prospect of innovation provide reference for the future highway traffic accidents and Bayesian network research in mountainous areas.