Name
Abstract | ||
|---|---|---|
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 43 800 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) $\geq$ 0.99. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TITS.2013.2238531 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
anomalous trajectory detection,online,isolation,global positioning system (gps) traces,trajectory,methodology,indexes,algorithms,global positioning system,accuracy | Commit,Simulation,Taxis,Road traffic,Global position system,Global Positioning System,Engineering,TRIPS architecture,Trajectory | Journal |
Volume | Issue | ISSN |
14 | 2 | 1524-9050 |
Citations | PageRank | References |
45 | 1.44 | 30 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Chen | 1 | 2032 | 185.26 |
| Daqing Zhang | 2 | 3619 | 217.31 |
| Pablo Samuel Castro | 3 | 295 | 16.21 |
| Nan Li | 4 | 353 | 15.23 |
| Lin Sun | 5 | 234 | 10.32 |
| Shijian Li | 6 | 1155 | 69.34 |
| Zonghui Wang | 7 | 207 | 17.16 |