冰雪路面下高速公路事故及严重程度影响因素分析

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...

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

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.