On Mining Anomalous Patterns in Road Traffic Streams

Large number of taxicabs in major metropolitan cities are now equipped with a GPS device. Since taxis are on the road nearly twenty four hours a day (with drivers changing shifts), they can now act as reliable sensors to monitor the behavior of traffic. In this paper we use GPS data from taxis to monitor the emergence of unexpected behavior in the Beijing metropolitan area. We adapt likelihood ratio tests (LRT) which have previously been mostly used in epidemiological studies to describe traffic patterns. To the best of our knowledge the use of LRT in traffic domain is not only novel but results in accurate and rapid detection of anomalous behavior.

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In  International Conference on Advanced Data Mining and Applications

Publisher  IEEE

Details

TypeInproceedings

Previous Versions

Wei Liu, Yu Zheng, Sanjay Chawla, Jing Yuan, and Xing Xie. Discovering Spatio-Temporal Causal Interactions in Traffic Data Streams, Association for Computing Machinery, Inc., 24 August 2011.

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