冰雪路面下高速公路事故及严重程度影响因素分析  

Analysis of traffic crashes and injury severity influence factors for ice-snow covered freeway roads

作　　者：刘佳雨 冷军强[2] 尚平 罗丽君[2] LIU Jiayu;LENG Junqiang;SHANG Ping;LUO Lijun(School of Transportation Science and Engineering,Harbin Institute of Technology,Harbin 150090,China;School of Automotive Engineering,Harbin Institute of Technology,Weihai,Weihai 264209,Shandong,China;Traffic Administration of Shaanxi Public Security Department,Xi′an 710061,China)

机构地区：[1]哈尔滨工业大学交通科学与工程学院,哈尔滨150090 [2]哈尔滨工业大学(威海)汽车工程学院,山东威海264209 [3]陕西省公安厅交通管理局,西安710061

出　　处：《哈尔滨工业大学学报》2022年第3期57-64,共8页Journal of Harbin Institute of Technology

基　　金：山东省自然科学基金(ZR2020MG020)。

摘　　要：为准确识别冰雪路面下高速公路事故致因因素及严重程度影响因素,构建了基于故障树和贝叶斯网络的综合模型。在转化后的贝叶斯网络中增设3条有向弧,并根据事故严重程度将叶节点分为3种状态,对叶节点的条件概率表进行更新,基于构建的综合模型进行贝叶斯网络逆向推理和敏感性分析。结果表明:能见度低、不良天气(雨、雪、雾)、货车、夜间无照明、驾驶经验不足、超速行驶、未保持安全距离等是诱发冰雪路面高速公路事故的高风险因素;超载、货车、违法上路等在冰雪路面条件下,更易加重事故严重程度。故障树和贝叶斯网络相结合的方法可为事故因素分析提供新视角。In order to accurately identify factors affecting the occurrence and severity of crashes on ice-snow covered freeways, an integrated model based on fault tree and Bayesian network was developed. Three directed arcs were added into the transformed Bayesian network. Leaf nodes were divided into three states according to the crash severity, and the conditional probability table of the leaf nodes was updated. Bayesian network reverse reasoning and sensitivity analysis were carried out based on the proposed integrated model. Results show that high risk factors including low visibility, adverse weather(rain, fog, snow), trucks, non-lighting at night, lack of driving experience, speeding, and insufficient headway mainly induced the occurrence of crashes on ice-snow covered freeways. Overloading, trucks, and illegal driving tended to increase the crash severity under ice-snow conditions. The integrated model of fault tree and Bayesian network is expected to provide a new perspective for the analysis of crash factors.

关 键 词：交通工程 事故致因 事故严重程度 故障树 贝叶斯网络 冰雪路面 

分 类 号：U491.3[交通运输工程—交通运输规划与管理]