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Detecting Anomaly in Traffic Flow from Road Similarity Analysis

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Web-Age Information Management (WAIM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9659))

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Taxies 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.

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This work was supported by the Chinese 863 Programs(2013AA01A601), Science Foundation of Shenzhen in China(JCYJ20140509150917445), the State Key Laboratory of Software Development Environment(SKLSDE-2015ZX-25) and Fundamental Research Funds for the Central Universities.

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Correspondence to Juhua Pu .

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© 2016 Springer International Publishing Switzerland

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Liu, X., Liu, X., Wang, Y., Pu, J., Zhang, X. (2016). Detecting Anomaly in Traffic Flow from Road Similarity Analysis. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham.

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  • Print ISBN: 978-3-319-39957-7

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