城市主干道安全建模与影响因素分析  

Modeling Safety of Urban Arterials and Identification of Impact Factors

作　　者：樊天翔 王雪松[1] 

机构地区：[1]同济大学道路与交通工程教育部重点实验室,上海201804

出　　处：《交通信息与安全》2016年第1期64-70,共7页Journal of Transport Information and Safety

基　　金：国家自然科学基金项目(51138003);上海市科学技术委员会项目(15DZ1204800)资助

摘　　要：主干道是城市路网的骨架,其复杂的道路交通环境造成事故频发。通过安全建模分析,识别事故的主要影响因素,有利于提出改善措施。基于上海市18条主干道176个路段,选取道路几何特征、用地性质、路段流量和路段平均车速作为影响因素。利用浮动车数据(floating car data,FCD)计算路段平均车速,克服了定点测速装置断面选取的问题。考虑到同一主干道路段之间的空间相关性,建立分层泊松对数正态模型。采用全贝叶斯方法进行参数估计,并比较不同信息先验对结果的影响。事故特征在高峰时段和平峰时段存在差异,因而分时段研究。结果表明:样本数据呈现分层结构,与极大似然先验(maximum likelihood estimation,MLE)模型相比,分层模型的方差信息标准(deviance information criterion,DIC)值更小。极大似然先验可以提高参数估计可靠性。与无信息先验模型相比,极大似然先验模型估计参数的标准差更小。在主干道层面,路段平均长度越长,事故频率上升。在路段层面,道路几何特征和用地性质对道路安全存在显著影响。事故频率随路段流量增加而上升,尤其在高峰时段,单位流量引起的事故增量更加显著。路段平均车速在高峰时段与事故频率具有正相关关系。Arterial roads are the framework of urban road network,where the crash occurs frequently due to complex traffic environment.It is necessary to conduct corresponding safety analysis,in order to propose constructive countermeasures.Geometric features,land use,traffic volume and average speed are gathered at a total of 176 road segments from 18 arterial roads in Shanghai.Average segment speed is calculated from floating car data（FCD）,which solved the problems related to speed data collection with sensors installed on fixed locations.Considering correlations among segments along an arterial roads,a set of Bayesian hierarchical Poisson log-normal models are developed.The Full Bayesian Method is used for parameter estimation and different prior distributions are tested.Crash features vary depending on time,so the models for peak and off-peak hours are developed separately.Results indicate that hierarchical models improve the goodness-of-fit of the data because deviance information criterion（DIC）values of hierarchical models are significantly less than maximum likelihood estimation（MLE）prior models.The reliability of parameter estimation can be improved by MLE prior.The standard deviations of parameters of the MLE prior models are less than those of non-informative models.Along arterial roads,the longer the segment length,the more crashes.At the segment level,geometric features and land use are substantially associated with crash frequencies.Higher traffic volume is associated with increased crash frequencies especially during peak hours.Average segment speed contributes to increasing crash occurrence during peak hours.

关 键 词：交通安全 城市主干道 安全模型 影响因素 贝叶斯分层模型 极大似然先验 浮动车数据 

分 类 号：U491.31[交通运输工程—交通运输规划与管理]