为了深入分析交通构成对高速公路交通安全的影响，采集广东省开阳高速公路 2014 年的道路、交通、事故数据，依据高速公路收费标准将车辆分为5 类，建立关联路段 道路、交通属性和事故频次的贝叶斯层级模型和条件自回归模型，并利用贝叶斯方法估计 模型参数，对比模型优劣． 对比结果表明:条件自回归模型由于解释了相邻路段间的空间 相关性，其模型拟合度比贝叶斯层级模型更高． 条件自回归模型的参数估计结果显示: 一 类车( 如: 小汽车) 的比例增加 1%，高速公路事故频次将降低15． 5%; 三类车( 如: 中型 客、货车) 的比例增加1%，事故频次将降低24． 4%;另外，长度越长，日均交通量、曲率、坡 度越大的路段发生事故的频次越高

To analyze the impact of traffic composition on freeway safety deeply， the roadway， traffic and crash data on Kaiyang Freeway in Guangdong Province in 2014 were collected． The vehicles were classified into five categories according to the toll standard． A Bayesian hierarchical model and a conditional autoregressive ( CAＲ) model were developed to correlate roadwayrelated and trafficrelated attributes with crash frequency on freeway segments． Bayesian methods were used to estimate the parameters and to compare the models． The results of model comparison show that the CAＲ model，which accounts for the spatial correlation across adjacent freeway segments，outperforms the Bayesian hierarchical model． The parameter estimates in the CAＲ model suggest that there are 15. 5% and 24. 4% decreases in expected crash frequency on the freeway per 1% increase of Categories 1 ( e. g. ，automobile) and 3 ( e. g. ，medium coach and medium truck) vehicles， respectively． Moreover，crash frequency is found higher on longer freeway segments with more averaged daily traffic，bigger curvature and steeper grade．