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Article Dans Une Revue IEEE Transactions on Intelligent Transportation Systems Année : 2013

iBOAT : isolation-based online anomalous trajectory detection

Résumé

Trajectories obtained from Global Position System (GPS)-enabled taxis grant us an opportunity not only to 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 the latter against time-dependent historically "normal" routes. We propose an online method that is able to detect anomalous trajectories "on-the-fly" and to identify which parts of the trajectory are responsible for its anomalousness. Furthermore, we perform an in-depth analysis on around 43800 anomalous trajectories that are detected out from the trajectories of 7600 taxis for a month, revealing that most of the anomalous trips are the result of conscious decisions of greedy taxi drivers to commit fraud. We evaluate our proposed isolation-based online anomalous trajectory (iBOAT) through extensive experiments on large-scale taxi data, and it shows that iBOAT achieves state-of-the-art performance, with a remarkable performance of the area under a curve (AUC) >= 0.99.
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Dates et versions

hal-00831500 , version 1 (07-06-2013)

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Chao Chen, Daqing Zhang, Pablo Samuel Castro, Nan Li, Lin Sun, et al.. iBOAT : isolation-based online anomalous trajectory detection. IEEE Transactions on Intelligent Transportation Systems, 2013, 14 (2), pp.806-818. ⟨10.1109/TITS.2013.2238531⟩. ⟨hal-00831500⟩
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