Abstract
Trajectories obtained from GPS-enabled taxis grant us an opportunity to not only extract meaningful statistics, dynamics and behaviors about certain urban road users, but also to monitor adverse and/or malicious events. In this paper we focus on the problem of detecting anomalous routes by comparing against historically “normal” routes. We propose a real-time method, iBOAT, that is able to detect anomalous trajectories “on-the-fly”, as well as identify which parts of the trajectory are responsible for its anomalousness. We evaluate our method on a large dataset of taxi GPS logs and verify that it has excellent accuracy (AUC ≥ 0.99) and overcomes many of the shortcomings of other state-of-the-art methods.
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Chen, C., Zhang, D., Samuel Castro, P., Li, N., Sun, L., Li, S. (2012). Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces. In: Puiatti, A., Gu, T. (eds) Mobile and Ubiquitous Systems: Computing, Networking, and Services. MobiQuitous 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30973-1_6
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DOI: https://doi.org/10.1007/978-3-642-30973-1_6
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