基于贝叶斯网络的公路运输风险评估  

Highway Transport Risk Assessment Based on Bayesian Network

作　　者：张永辉[1] 刘子云 杨靖芳 毕郁 ZHANG Yonghui;LIU Ziyun;YANG Jingfang;BI Yu(College of Automotive&Traffic Engineering,Nanjing Forestry University,Nanjing 210042,China)

机构地区：[1]南京林业大学汽车与交通工程学院,江苏南京210042

出　　处：《物流技术》2022年第12期27-31,共5页Logistics Technology

基　　金：江苏省交通运输科技项目(2021G08);江苏省自然科学基金青年项目(BK20200798);南京林业大学大学生创新训练计划项目(2021NFUSPITP0452)。

摘　　要：为定量分析公路运输安全风险的影响因素,对比公路客运、公路货运事故的主要致因,采用贝叶斯网络(BN)模型,运用Netica软件构建了各成因的耦合作用模型,对公路运输事故致因进行了深入分析。通过建立以客运事故严重程度(A)和货运事故严重程度(T)为目标节点,人、车、环境、管理4个因素为中间节点,13个子因素为基本节点的贝叶斯网络模型,进行了双向推理,确定了事故主要致因。结果表明:双因素耦合风险是公路运输事故的主因,其中公路客运人-管理耦合造成重大伤害概率高达93%,公路货运车-管理耦合造成重大伤害概率高达75.2%;管理是客运与货运事故的主要致因,人的不安全状态是客运事故的第二致因,车的不安全状态是货运事故的第二致因。In this paper, in order to quantitatively analyze the influencing factors of highway transport safety risks, we compared the main causes of accidents in highway passenger transport and highway freight transport, used the Bayesian network(BN) model and software Netica to construct the coupling model of the causes, and analyzed in-depth the cause of the accidents. By establishing the Bayesian network model with passenger transport accident severity(A) and freight transport accident severity(T) as the target nodes, the four factors of people,vehicle, environment, and management as the intermediate nodes, and 13 sub-factors as the basic nodes, we conducted two-way reasoning to determine the main causes of the accidents. The result showed that twin-coupled risks are the main cause of highway transport accidents,among which the people-management coupled risk was 93% likely to cause severe injury/damage, and the vehicle-management coupled risk was 75.2% likely;the management risk was the primary cause of passenger transport accidents and freight transport accidents, while the unsafe state of people was the second cause of passenger transport accidents and the unsafe state of vehicles the second cause of freight transport accidents.

关 键 词：公路运输 风险评估 风险耦合 公路客运 公路货运 贝叶斯网络 

分 类 号：F224.0[经济管理—国民经济] F540.8