iBAT: Detecting anomalous taxi trajectories from GPS traces

Daqing Zhang, Nan Li, Zhi Hua Zhou, Chao Chen, Lin Sun, Shijian Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 (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.

Original languageEnglish
Title of host publicationUbiComp'11 - Proceedings of the 2011 ACM Conference on Ubiquitous Computing
Pages99-108
Number of pages10
DOIs
Publication statusPublished - 19 Oct 2011
Event13th International Conference on Ubiquitous Computing, UbiComp'11 and the Co-located Workshops - Beijing, China
Duration: 17 Sept 201121 Sept 2011

Publication series

NameUbiComp'11 - Proceedings of the 2011 ACM Conference on Ubiquitous Computing

Conference

Conference13th International Conference on Ubiquitous Computing, UbiComp'11 and the Co-located Workshops
Country/TerritoryChina
CityBeijing
Period17/09/1121/09/11

Keywords

  • anomalous trajectory detection
  • gps trace
  • isolation-based anomaly detection
  • taxi

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