Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces

Chao Chen; Daqing Zhang; Pablo Samuel Castro; Nan Li; Lin Sun; Shijian Li

Mobile and Ubiquitous Systems: Computing, Networking, and Services. 8th International ICST Conference, MobiQuitous 2011, Copenhagen, Denmark, December 6-9, 2011, Revised Selected Papers

Research Article

Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces

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@INPROCEEDINGS{10.1007/978-3-642-30973-1_6,
    author={Chao Chen and Daqing Zhang and Pablo Samuel Castro and Nan Li and Lin Sun and Shijian Li},
    title={Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces},
    proceedings={Mobile and Ubiquitous Systems: Computing, Networking, and Services. 8th International ICST Conference, MobiQuitous 2011, Copenhagen, Denmark, December 6-9, 2011, Revised Selected Papers},
    proceedings_a={MOBIQUITOUS},
    year={2012},
    month={10},
    keywords={},
    doi={10.1007/978-3-642-30973-1_6}
}

Chao Chen
Daqing Zhang
Pablo Samuel Castro
Nan Li
Lin Sun
Shijian Li
Year: 2012
Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-642-30973-1_6

Chao Chen¹^,*, Daqing Zhang¹^,*, Pablo Samuel Castro¹^,*, Nan Li^,*, Lin Sun¹^,*, Shijian Li²^,*

1: TELECOM SudParis, CNRS SAMOVAR
2: Zhejiang University

*Contact email: chao.chen@it-sudparis.eu, daqing.zhang@it-sudparis.eu, pablo.castro@it-sudparis.eu, lin@lamda.nju.edu.cn, lin.sun@it-sudparis.eu, shijianli@zju.edu.cn

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, , 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.

Published: 2012-10-23

: http://dx.doi.org/10.1007/978-3-642-30973-1_6