基于线形与交通状态的山区高速公路追尾事故预测

为了进行山区高速公路追尾事故预测并识别追尾事故突出诱导因素，在对两车追尾事故进行类别划分并确定出典型两车追尾事故的基础上，分析了典型两车追尾事故的事故率与线形指标、车速差、大型车混入率、交通量等单一因素间的相关关系。鉴于单一因素与追尾事故率间的关系不能准确描述追尾事故发生规律的缺陷，建立了线形与交通状态组合条件下的追尾事故次数负二项分布预测模型，并给出了模型变量弹性系数计算方法，用以确定追尾事故的突出诱导因素。研究结果表明：基于线形与交通状态的追尾事故负二项分布预测模型能够对追尾事故进行准确预测，利用弹性系数计算方法确定出车速差、年平均日交通量（AADT）以及竖曲线半径为典型两车追尾事故的突出诱导因素。

Prediction of Rear-end Collision on Mountainous Expressway Based on Geometric Alignment and Traffic Conditions

In order to predict the rear-end collision(REC) and identify its main induction factors on mountainous expressway,typical two-vehicle REC was defined based on its classification,and the relationship between REC rate and the single geometric alignment indexes,speed difference of vehicles,traffic composition and traffic volume was analyzed respectively.As the relationship between single element and REC rate could not fully describe the occurrence of REC,negative binomial(NB) prediction model based on the geometric alignment and traffic condition was developed,and the elasticity coefficient calculation method used to confirm the prominent induction factors was proposed.The results show that the NB prediction model can predict REC accurately.Speed difference,AADT and vertical curve radius are confirmed to be prominent factors for typical two-vehicle REC by the elasticity coefficient calculation method.