Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces

  • Chao Chen
  • Daqing Zhang
  • Pablo Samuel Castro
  • Nan Li
  • Lin Sun
  • Shijian Li
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 104)

Abstract

Trajectories obtained from GPS-enabled taxis grant us an opportunity to not only 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 against historically “normal” routes. We propose a real-time method, iBOAT, that is able to detect anomalous trajectories “on-the-fly”, as well as identify which parts of the trajectory are responsible for its anomalousness. We evaluate our method on a large dataset of taxi GPS logs and verify that it has excellent accuracy (AUC ≥ 0.99) and overcomes many of the shortcomings of other state-of-the-art methods.

Keywords

Road Segment Area Under Curve Anomaly Detection Taxi Driver Anomaly Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Chao Chen
    • 1
  • Daqing Zhang
    • 1
  • Pablo Samuel Castro
    • 1
  • Nan Li
    • 2
    • 3
  • Lin Sun
    • 1
  • Shijian Li
    • 4
  1. 1.Institut TELECOMTELECOM SudParis, CNRS SAMOVARFrance
  2. 2.National Key Laboratory for Novel Software TechnologyNanjing UniversityChina
  3. 3.School of Mathematical SciencesSoochow UniversityChina
  4. 4.Department of Computer ScienceZhejiang UniversityChina

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