A New Framework for Traffic Anomaly Detection

Abstract

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2014 SIAM International Conference on Data Mining (SDM)A New Framework for Traffic Anomaly DetectionJinsong Lan, Cheng Long, Raymond Chi-Wing Wong, Youyang Chen, Yanjie Fu, Danhuai Guo, Shuguang Liu, Yong Ge, Yuanchun Zhou, and Jianhui LiJinsong Lan, Cheng Long, Raymond Chi-Wing Wong, Youyang Chen, Yanjie Fu, Danhuai Guo, Shuguang Liu, Yong Ge, Yuanchun Zhou, and Jianhui Lipp.875 - 883Chapter DOI:https://doi.org/10.1137/1.9781611973440.100PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Trajectory data is becoming more and more popular nowadays and extensive studies have been conducted on trajectory data. One important research direction about trajectory data is the anomaly detection which is to find all anomalies based on trajectory patterns in a road network. In this paper, we introduce a road segment-based anomaly detection problem, which is to detect the abnormal road segments each of which has its “real” traffic deviating from its “expected” traffic and to infer the major causes of anomalies on the road network. First, a deviation-based method is proposed to quantify the anomaly of reach road segment. Second, based on the observation that one anomaly from a road segment can trigger other anomalies from the road segments nearby, a diffusion-based method based on a heat diffusion model is proposed to infer the major causes of anomalies on the whole road network. To validate our methods, we conduct intensive experiments on a large real-world GPS dataset of about 23,000 taxis in Shenzhen, China to demonstrate the performance of our algorithms. Previous chapter Next chapter RelatedDetails Published:2014eISBN:978-1-61197-344-0 https://doi.org/10.1137/1.9781611973440Book Series Name:ProceedingsBook Code:PRDT14Book Pages:1-1086