基于货运车辆预警数据的高速公路危险驾驶热点路段识别

本文以货运车辆车联网数据中的车辆碰撞预警、疲劳预警和超速预警数据为研究对象,针对G4高速公路北京和河北境内道路上的三种预警热点路段进行识别。运用全局空间自相关性分析方法得出三种预警数据在道路空间中不是随机分布,而是集中在某一个或几个路段上;进一步采用局部空间相关性分析方法识别出三种不同预警类型下对应的热点路段。交通管理部门可根据识别出的预警热点路段提出针对性的管控措施,有助于缓解交通事故的发生,提高货运安全水平。

Identification of Highway Hot Spots of Dangerous Driving Behavior Based on Early Warning Data of Freight Trucks

This paper takes the early warning data of collision, fatigue driving and speeding as the research object, which was obtained from the Internet of Vehicle, and identifies the hotspot sections of three kinds of early warning data on G4 highway in Beijing and Hebei. First, using the theory of global spatial autocorrelation, it is concluded that the early warning points generated during the driving are not randomly distributed in space, but clustered on one or more sections. Then, with the help of local spatial autocorrelation analysis methods, we identify the hotspot sections of three kinds of early warning data. Based on the hotspot sections identified, the traffic management department can take effective measures to prevent traffic accidents and improve the level of freight safety.