KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2016-06-02
Print Publication Date2016
Permanent link to this recordhttp://hdl.handle.net/10754/611754
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AbstractTaxies equipped with GPS devices are considered as 24-hour moving sensors widely distributed in urban road networks. Plenty of accurate and realtime trajectories of taxi are recorded by GPS devices and are commonly studied for understanding traffic dynamics. This paper focuses on anomaly detection in traffic volume, especially the non-recurrent traffic anomaly caused by unexpected or transient incidents, such as traffic accidents, celebrations and disasters. It is important to detect such sharp changes of traffic status for sensing abnormal events and planning their impact on the smooth volume of traffic. Unlike existing anomaly detection approaches that mainly monitor the derivation of current traffic status from history in the past, the proposed method in this paper evaluates the abnormal score of traffic on one road by comparing its current traffic volume with not only its historical data but also its neighbors. We define the neighbors as the roads that are close in sense of both geo-location and traffic patterns, which are extracted by matrix factorization. The evaluation results on trajectories data of 12,286 taxies over four weeks in Beijing show that our approach outperforms other baseline methods with higher precision and recall.
CitationLiu, X., Liu, X., Wang, Y., Pu, J., & Zhang, X. (2016). Detecting Anomaly in Traffic Flow from Road Similarity Analysis. Lecture Notes in Computer Science, 92–104. doi:10.1007/978-3-319-39958-4_8
SponsorsThis work was supported by the National Department Public Benefit Research Foundation of China (No.201510209).