计算机工程2017，Vol.43Issue(2)：16-20,5.DOI:10.3969/j.issn.1000-3428.2017.02.003
基于聚类的出租车异常轨迹检测
Clustering-based Taxi Trajectory Outlier Detection
摘要
Abstract
Taxi Global Position System(GPS) data contain macro information about the behavior of urban traffic and moving object behavior,from which valuable anomalous trajectory patterns can be mined.The location,geometry and travel time are taken as the spatial and temporal characteristics of the taxi trajectory respectively.According to the deviation of the feature,the trajectory anomalies are divided into temporal,space and spatio-temporal outliers.The trajectories of the same starting and ending points are extracted from the trajectory data,and are partitioned into segments.The similarity between trajectories is calculated and clustering based on distance and density is carried out.Frequent and the sparse trajectories are preliminary separated by the spatial characteristies.Based on kr criterion,the separation threshold of temporal anomaly is determined to realize the classification of the temporal characteristic,and finally the trajectory outlier detection of the taxi is realized.The experimental results show that the method can extract personalized route as well as abnormal parking location and traffic section from abnormal trajectories,providing reference information for intelligent transportation as well as efficient logistics planning and execution.关键词
异常轨迹检测/全球定位系统数据/轨迹聚类/时空特征/轨迹模式Key words
trajectory outlier detection/Global Position System (GPS) data/trajectory clustering/spatio-temporal characteristics/trajectory pattern分类
信息技术与安全科学引用本文复制引用
朱燕,李宏伟,樊超,许栋浩,施方林..基于聚类的出租车异常轨迹检测[J].计算机工程,2017,43(2):16-20,5.基金项目
国家自然科学基金“空间数据流的概念漂移问题研究”(41571394). （41571394）
