Name
Abstract | ||
|---|---|---|
GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to reveal many hidden "facts" about the city dynamics and human behaviors. In this paper, we aim to discover anomalous driving patterns from taxi's GPS traces, targeting applications like automatically detecting taxi driving frauds or road network change in modern cites. To achieve the objective, firstly we group all the taxi trajectories crossing the same source destination cell-pair and represent each taxi trajectory as a sequence of symbols. Secondly, we propose an Isolation-Based Anomalous Trajectory (iBAT) detection method and verify with large scale taxi data that iBAT achieves remarkable performance (AUC0.99, over 90% detection rate at false alarm rate of less than 2%). Finally, we demonstrate the potential of iBAT in enabling innovative applications by using it for taxi driving fraud detection and road network change detection. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2030112.2030127 | UbiComp |
Keywords | Field | DocType |
road network change,detection method,false alarm rate,anomalous driving pattern,detection rate,large scale taxi data,taxi trajectory,anomalous taxi trajectory,gps-equipped taxi,gps trace,road network change detection,fraud detection,change detection,human behavior,anomaly detection | Change detection,Computer science,Simulation,Taxis,Real-time computing,Human–computer interaction,Global Positioning System,Human behavior,Constant false alarm rate,Trajectory | Conference |
Citations | PageRank | References |
97 | 3.86 | 27 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daqing Zhang | 1 | 3619 | 217.31 |
| Nan Li | 2 | 353 | 15.23 |
| Zhi-Hua Zhou | 3 | 13480 | 569.92 |
| Chao Chen | 4 | 2032 | 185.26 |
| Lin Sun | 5 | 234 | 10.32 |
| Shijian Li | 6 | 1155 | 69.34 |