摘要
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 roadway-related and traffic-related 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.
作者
温惠英
孙佳人
曾强
张璇
WEN Huiying;SUN Jiaren;ZENG Qiang;ZHANG Xuan(School of Civil Engineering and Transportation,South China University of Technology,Guangzhou 510641,Guangdong,China)
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2018年第6期1-7,共7页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(51578247)
中国博士后科学基金资助项目(2017M610529)
广东省自然科学基金资助项目(2017A030310161)~~
关键词
高速公路
交通安全
交通构成
空间关联
条件自回归模型
freeway
traffic safety
traffic composition
spatial correlation
conditional autoregressive model
