基于空间自相关的常规城市公交车辆危险驾驶热点路段识别

Identification of Hazardous Driving Hotspots of Conventional Urban Bus Based on Spatial Autocorrelation

为了获得公交危险驾驶状态的空间分布特征,采用空间自相关性分析方法,识别危险驾驶状态的空间集聚性,确定危险驾驶状态热点路段,并对显著性影响因素进行分析。首先,采集4个季度各1周公交车辆卫星定位数据样本,修复重复数据、异常数据和缺失数据,并以公交站点为节点划分空间区段,对每个区段进行编号;接着,将速度过快、急加速、急减速和急转弯确定为危险驾驶状态,参照车辆运动学特性获得4种危险驾驶状态阈值,并计算4种危险驾驶状态的统计学指标和全局莫兰指数,结果表明,公交车辆危险驾驶状态具有空间集聚性(空间随机分布概率p<0.01,标准差得分值Z>2.58),速度过快状态(全局莫兰指数为0.731)的空间集聚性最为显著;然后,分别对4种危险驾驶状态进行局部空间自相关性分析,绘制了90%、95%和99%置信度下的莫兰散点图和Lisa集聚图,结合城市地图,获得危险驾驶状态的热点路段;最后,选取路段长度、车道数、平直度等9个指标,对比分析了OLS模型、SLE模型、SEM模型和SDM模型的拟合优度,采用SDM模型获得4种危险驾驶状态的显著性影响因素。文中研究结果可为公交车辆危险驾驶状态空间识别、精细化安全运行管理提供理论依据。

In order to obtain the spatial distributing characteristics of hazardous bus driving status,this paper identified the spatial clustering through spatial autocorrelation analysis,determined the hot spots,and analyzed the significant influencing factors.Firstly,the study collected position system data samples of the urban buses for one week in each of the four quarters and modified the duplicate,abnormal and missing data.Bus stops were used as nodes to divide spatial spots,and every spot was numbered.Over speed,urgent acceleration,urgent deceleration and sharp turn were identified as hazardous driving status.The four conditions thresholds were obtained according to the kinematic characteristics of vehicles.The study calculated statistical indicators and global Moran’s Ig of four conditions.The results show that hazardous driving status are spatially clustered(probability of a spatial random distribution p<0.01,standard deviation score Z>2.58).Over speed has most significant characteristic of spatial clustering(Ig=0.731).The study performed local spatial autocorrelation analysis for the four conditions.According to the analysis,local Moran’s I scatter plots and LISA clustering plots are plotted at 90%,95%and 99%confidence levels.The hazardous hot spots of urban buses were obtained combining with city maps.Finally,the study selected 9 factors such as road length,number of lanes and straightness to formulate models.The compare and analysis were performed to get the fitting goodness of OLS,SLE,SEM and SDM model.The SDM model was used to obtain the significant influencing factors for 4 dangerous driving states.The results can provide a theoretical basis for supervising the safety operation and identifying the hazardous driving status of urban buses in spatial perspective.