基于T-S模糊故障树和贝叶斯网络的重特大交通事故成因分析

重特大交通事故是最严重的交通事故类型,为了识别此类事故的主要致因,融合T-S模糊故障树和贝叶斯网络对其进行深入分析.建立了以重特大交通事故为顶事件,人、车、路、环境4个因素为中间事件,24个子因素为基本事件的T-S模糊故障树,将其转化为贝叶斯网络,进而双向推理基本事件的重要度和后验概率,确定主要致因.结果表明:融合T-...

A Cause Analysis of Extraordinarily Severe Traffic Crashes Based on T-S Fuzzy Fault Tree and Bayesian Network

The T-S fuzzy fault tree and Bayesian network are integrated for an in-depth analysis to identify the main causes of extraordinarily severe traffic crashes. A T-S fuzzy fault tree is established, with the extraordinarily severe traffic crash taken as the top event, the human, vehicle, road, and environmental factors taken as the intermediate events, and 24 sub-factors taken as the basic events. The fuzzy fault tree is transformed into a Bayesian network, and the importance and posterior probability of the basic events can be inferred biaxially to determine the main causes. The results show that the method of fusing T-S fuzzy fault tree and Bayesian network can improve the accuracy and reliability of the analysis results of the causes of extraordinarily severe traffic crashes through forward and reverse reasoning and can determine improper operation, speeding, imperfect protection facilities, and bending. Slope combination, slippery road surface, and failure to drive following regulations are the six major causes of extraordinarily severe traffic crashes. The six major causes are analyzed, revealing that improper operation and speeding are more critical for extraordinarily severe traffic crashes.