基于负二项分布的高速公路交通事故影响因素分析

An Analysis of Factors Influencing Freeway Crashes with a Negative Binomial Model

为分析高速公路交通事故的影响因素,构建基于负二项分布的事故分析模型,探究事故数与交通特性、公路线形及路面性能间关系。鉴于传统固定参数模型难以刻画各因素对事故风险影响的异质性,引入了随机参数建模方法。结果表明:相比于固定参数负二项模型,构建的随机参数负二项模型有更好的拟合优度,且能更合理地反映各因素对事故的作用效果;将随机参数分布的均值设置为其他变量的函数形式,可进一步挖掘各因素对事故风险的交互影响;交通量、路段长度、货车比例、平曲线曲率、纵坡坡度及车辙深度均与事故数正相关,且其每增加1%,事故数分别增加0.299%,1.029%,0.093%,0.079%,0.068%和0.054%;结构强度系数与事故数负相关,其每增加1%,事故数降低0.064%;增加路缘带宽度有益于交通安全;单向3车道或4车道路段的事故数多于同等条件下的2车道路段;弯坡组合路段的事故风险明显高于单纯的平曲线路段;货车比例高的下坡路段事故风险尤其高。

In this study,a negative binomial model is developed to investigate factors influencing crashes on freeways such as traffic flow,freeway alignment and pavement conditions. Since traditional fixed-effects models are incapable of capturing the heterogeneous effects of these factors on crash risk,a random-effects modeling method is introduced. Results indicate that the proposed random-effects negative binomial model has a better goodness-of-fit compared with its fixed-effects counterpart. In addition,the model explains the impact of the related factors on road safety in a more reasonable way. The interactions of the impact factors used in the model can be further studied by setting up the mean of a random parameter to be a functional form of other variables. It is found that traffic volume,length of road section,proportion of truck traffic,curvature,longitudinal grade and rutting depth are all positively correlated with crash frequency and 1% of increase in aforementioned variables increases the expected crash risk by0.299%,1.029%,0.093%,0.079%,0.068%,and 0.054%,respectively. The pavement structural strength index is negatively correlated with crashes,and one percent of increase of the index will reduce the expected crash risk by 0.064%.Increasing the width of marginal strip is found to be beneficial to enhance safety. Three-or four-lane one-way freeway sections are found to experience more crashes than two-lane one-way freeway sections. It is also found that a segment with the combined alignment of curves and slopes is significantly more dangerous than a flat curved segment and the crash risk is considerably higher for downhill segments with a high proportion of truck flow.