摘要
提出了基于空间自相关鉴别城市道路事故多发点的方法,并利用5年内交通事故数据进行验证.首先,以非参数核密度估计模型描述交通事故点的空间二维分布,得到事故分布密度的最优窗宽为115.2m,借此划分路段的空间单元.然后,基于地理信息系统"面弧"城市道路网络拓扑模型构建空间统计单元,并集计事故数和事故严重度指数属性.最后,通过全局Moran指数检验,事故数据呈现聚集的空间分布模式,局部G统计量鉴别出属性值高低的聚类,并生成事故多发点分布图.研究结果表明,与事故频率建模的负二项模型相比,事故位置与属性数据一体化下的空间数据分析不但能够鉴别路段(交叉口)的事故多发点,而且能够对其属性值进行空间关联排序,交通管理部门可利用空间分析可视化结果定位事故多发点和进一步开展交通安全决策研究.
An approach based on spatial autocorrelation analysis was presented to identify hot spots on urban road. The application of this methodology was illustrated by five- year＇s traffic crash data. Firstly, a non-parametric kernel density estimate sketched a model of two dimensional planar accident point spatial distribution. An estimation of the optimal bandwidth for accident distribution density was 115.2 m and subsequently was selected to create a spatial statistical unit on road segment. Then, the spatial statistical units were derived from a GIS-based region-arc topology model as a representation of the urban road network, and the attribute values of crash counts and severity indices were aggregated. Finally, the Globe Moran index was employed to examine spatial distribution clustered pattern of crash data. The local G-statistic was used to identify the clustering of low and high index values and to generate a crash hot spots map. Results of the research indicate that compared with the negative binomial model in modeling the crash frequency, the spatial analysis under integration of accident attribute data with position can not only identify hot spots on road segments （intersections） but also rank hot spots with spatial correlation according to the attribute values. Traffic management department can use the spatial analysis visualized results to locate hot spots and develop further traffic safety decisionmaking research.
出处
《同济大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第5期664-669,共6页
Journal of Tongji University:Natural Science
基金
"十一五"国家科技支撑计划(2007BAK35B03)
国家自然科学基金(51078270)
乌鲁木齐市科学技术计划(H101326001)
关键词
交通安全
空间自相关
事故多发点鉴别
核密度
估计
负二项模型
traffic safety
spatial autocorrelation
hot spots identification
kernel density estimation
negative binomial model
