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Real Time Anomalous Trajectory Detection and Analysis

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Abstract

GPS-equipped taxis can be considered 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 present a novel GPS-based taxi system which can detect ongoing anomalous passenger delivery behaviors leveraging our proposed iBOAT method. To achieve real time monitoring, we reduce the response time of iBOAT by more than five times with an inverted index mechanism adopted. We evaluate the effectiveness of the system with large scale real life taxi GPS records while serving 200,000 taxis. With this system, we obtain about 0.44 million anomalous trajectories out of 7.35 million taxi delivery trips, which correspond to 7600 taxis’ GPS records in one month time in the city of Hangzhou, China. Through further analysis of these anomalous trajectories, we observe that: (1) Over 60 % of the anomalous trajectories are “detours” that travel longer distances and time than normal trajectories; (2) The average trip length of drivers with high-detour tendency is 20 % longer than that of normal drivers; (3) The length of anomalous sub-trajectories is usually less than a third of the entire trip, and they tend to begin in the first two thirds of the journey; (4) Although longer distance results in a greater taxi fare, a higher tendency to take anomalous detours does not result in higher monthly revenue; and (5) Taxis with a higher income usually spend less time finding new passengers and deliver them in faster speed.

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Authors and Affiliations

  1. RST Department, Institut Mines-TELECOM/TELECOM SudParis, Evry, 91011, France

    Lin Sun, Daqing Zhang, Chao Chen & Pablo Samuel Castro

  2. College of Computer Science, Zhejiang University, Hangzhou, 310027, China

    Shijian Li & Zonghui Wang

Authors
  1. Lin Sun
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  2. Daqing Zhang
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  3. Chao Chen
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  4. Pablo Samuel Castro
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  5. Shijian Li
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  6. Zonghui Wang
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Correspondence to Daqing Zhang.

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Sun, L., Zhang, D., Chen, C. et al. Real Time Anomalous Trajectory Detection and Analysis. Mobile Netw Appl 18, 341–356 (2013). https://doi.org/10.1007/s11036-012-0417-8

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