Effective and efficient trajectory outlier detection based on time-dependent popular route

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• 作者：Jie Zhu ; Wei Jiang ; An Liu ; Guanfeng Liu ; Lei Zhao
• 关键词：Outlier detection ; Time ; dependent popular route ; Trajectory pattern mining
• 刊名：World Wide Web
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：20
• 期：1
• 页码：111-134
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Information Systems Applications (incl.Internet); Database Management; Operating Systems;
• 出版者：Springer US
• ISSN：1573-1413
• 卷排序：20

文摘

With the rapid proliferation of GPS-equipped devices, a myriad of trajectory data representing the mobility of various moving objects in two-dimensional space have been generated. This paper aims to detect the anomalous trajectories with the help of the historical trajectory dataset and the popular routes. In this paper, both of spatial and temporal abnormalities are taken into consideration simultaneously to improve the accuracy of the detection. Previous work has developed a novel time-dependent popular routes based algorithm named TPRO. TPRO focuses on finding out all outliers in the historical trajectory dataset. But in most cases, people do not care about which trajectory in the dataset is abnormal. They only yearn for the detection result of a new trajectory that is not included in the dataset. So this paper develops the the upgrade version of TPRO, named TPRRO. TPRRO is a real-time outlier detection algorithm and it contains the off-line preprocess step and the on-line detection step. In the off-line preprocess step, TTI (short for time-dependent transfer index) and hot TTG (short for time-dependent transfer graph) cache are constructed according to the historical trajectory dataset. Then in the on-line detection step, TTI and hot TTG cache are used to speed up the detection progress. The experiment result shows that TPRRO has a better efficiency than TPRO in detecting outliers.