iBAT: detecting anomalous taxi trajectories from GPS traces

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 sourcedestination cell-pair and represent each taxi trajectory as a sequence of symbols. Secondly, we propose an IsolationBased Anomalous Trajectory (iBAT) detection method and verify with large scale taxi data that iBAT achieves remarkable performance (AUC>0.99, 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. Author Keywords Anomalous trajector...

• Type: Journal
• Year: 2011
• Where: HUC
• Authors: Daqing Zhang, Nan Li, Zhi-Hua Zhou, Chao Chen, Lin Sun, Shijian Li
• Keywords: Anomaly Detection, Digital Traces, HUC 2011, Human Behaviors, Human Computer Interaction