山区公路小半径弯道路段事故严重度影响因素及其异质性比较分析

为分析影响山区公路小半径路段典型事故的严重程度的相关因素及其异质性效应，基于某山区双车道公路1 067起交通事故数据，从驾驶员、车辆、道路和环境4个方面选取15个潜在特征变量，采用二项Logit模型和随机参数二项Logit模型，分别构建小半径弯道路段上追尾碰撞、正面碰撞和侧面碰撞3类典型事故的严重度分析模型，分析3类典型事故严重度的显著影响因素，并采用边际弹性系数量化分析影响因素的作用强度。结果表明，小半径弯道路段上不同形态事故的严重度影响因素存在明显差异:①追尾碰撞严重度的显著影响因素依次为摩托车、夜间、弯道转角、驾驶员年龄、季节，摩托车和冬季分别是服从(2.716.1.5642)和(-1.495，2.1162)正态分布的异质性影响因素，导致发生伤亡事故的概率为95.72%和23.58%；②正面碰撞严重度的显著影响因素依次为货车、摩托车、驾驶员超车、弯道转角和弯道长度，货车导致其伤亡事故概率增加108.8%，摩托车和弯道长度分别是服从(6.941，9.9012)和(-0.004，0.0032)正态分布的异质性影响因素，导致发生伤亡事故的概率为76.11%和9.18%；③侧面碰撞严重度的显著影响因素依次为摩托车、驾驶员年龄及弯道有接入口，摩托车和接入口分别是服从(5.211，5.1112)和(-1.408，2.1462)正态分布的异质性影响因素，导致发生伤亡事故的概率为88.87%和25.47%。④与传统二项Logit模型相比，追尾碰撞、正面碰撞和侧面碰撞的随机参数二项Logit模型的拟合优度分别提高了2.85%，4.15%，6.76%，且定量捕捉了异质性影响因素，更适用于事故严重度的精细化分析。 

A Comparative Analysis of Heterogeneous Effects of Various Factors on Accident Severity at Sharp Curve Sections of Mountainous Highway

To identify contributing factors and their heterogeneity effects onto accident severity at sharp curve sections of mountainous highway, fifteen potential factors are selected from the following areas including driver, vehicle, road, and environment conditions based on the data from 1 067 accidents on a two-lane highway in mountain areas. Then, a binary Logit model, and a random parameters binary Logit model are used to analyze severity of three typical types of accidents including rear-end, head-on, and side collision. Results show that there are significant differences in the effect of impact factors on crash severity of three types of accidents at sharp curve sections as follows: ①For rear-end collisions, the significant variables of crash severity are motorcycle, night, cornering, age of drivers, and different seasons. Motorcycle and winter are heterogeneous influence factors obeying a normal distribution with a mean value of 2.716 and -1.495, a variance of 1.564 and 2.116. The probability of resulting in a casualty accident is 95.72% and 23.58%, respectively. ②For head-on collisions, the significant variables of crash severity are truck, motorcycle, overtaking of drivers, curve corners, and curve lengths in turn. The probability of casualty accidents with the truck increases by 108.8%. The motorcycle and curve lengths are heterogeneous influencing factors obeying a normal distribution, with a mean value of 6.941 and -0.004, a variance of 9.901 and 0.003. Consequently, the probability of casualty accident is 76.11% and 9.18%, respectively. ③For side collisions, the significant variables of crash severity are motorcycle, age of drivers, and corner with entrance in turn. The motorcycle and corner with entrance are heterogeneous influencing factors obeying a normal distribution, with a mean value of 5.211 and -1.408, and a variance of 5.111 and 2.146. Consequently, the probability of casualty accident is 88.87% and 25.47%, respectively. ④Compared with the traditional binomial Logit models, the accuracy of the random parameter binary Logit models for predicting crash severity of the rear-end, head-on, and side collision are increased by 2.85%, 4.15%, and 6.76%, respectively. With the proposed model, the heterogeneous effects of several factors can be quantitatively captured, and therefore, it can be used for improved severity analysis of road accidents.