基于路口信息的出租车异常轨迹检测

Taxi abnormal trajectory detection based on intersection information

针对出租车的异常轨迹检测问题,根据已有的出租车GPS数据,结合城市道路路口信息,提出了一种基于路口的异常轨迹检测算法(Intersection-Based Anomalous Trajectories Detection,IBATD)。该算法将GPS数据进行地图匹配,并将匹配后的GPS轨迹以路口的形式描述,再以多叉树的方式实现轨迹聚类。通过计算待测轨迹的轨迹概率,并与给定异常阈值进行比较,将轨迹分类为正常或异常。与经典的基于Hausdorff距离的谱聚类算法相比,多叉树轨迹聚类具有更准确的轨迹模型库、更快的运算速度以及实时检测的特点。

For trajectory outlier detection problem of the taxi, on the basis of the existing taxi GPS data, and combinedwith the urban road intersection information, this paper puts forward an IBATD(Intersection-Based Anomalous TrajectoriesDetection)algorithm. The algorithm describes the GPS point in the form of intersection after map matching, and thenclusters these trajectories with multiway-tree method. By calculating the trajectory probability under the test and comparingwith the given anomaly threshold, it classifies the trajectory to be normal or abnormal. Compared with the classic spectralclustering algorithm based on Hausdorff distance, the multiway-tree clustering method has more accurate trajectorymodel library, faster operation speed, and can make real-time detection.