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Trajectory outlier detection approach based on common slices sub-sequence

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Abstract

Trajectory outlier detection is one of the most popular trajectory data mining topics. It helps researchers obtain a lot of valuable information that can be used as important guidance in monitoring and forecasting. Existing methods have difficulty in detecting the outlying trajectories with continuous multi-segment exception. To address the problem, in this paper, we propose a novel trajectory outlier detection algorithm based on common slices sub-sequence (TODCSS). For each trajectory, the direction-code sequence is firstly calculated based on the direction of each trajectory segment. Secondly, the corresponding sequence consisting of trajectory slices is obtained by inflection point segmentation. And then, the common slices sub-sequences between two trajectories are found to measure their distance. Finally, the slice outliers and trajectory outliers are detected based on the new CSS distance calculation. Both the intuitive visualization presentation and the experimental results on real Atlantic hurricane dataset, real-life mobility trajectory dataset of taxis in San Francisco and synthetic labeled dataset show that the proposed TODCSS algorithm effectively detects slice and trajectory outliers, and improves accuracy and stability in trajectory outlier detection.

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Notes

  1. http://avires.dimi.uniud.it/papers/trclust/create_ts2.m.

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Acknowledgements

The authors would like to thank the reviewers for their useful comments and suggestions for this paper. This work was supported by the National Natural Science Foundation of China (61702010, 61672039), the Key Program for University Top Talents of Anhui Province (gxbjZD2016011), the Natural Science Foundation of Anhui Province (1508085QF134), the University Natural Science Research Program of Anhui Province (KJ2017A327), and the Science and Technology Project of Wuhu City (2016cxy04).

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Anhui Normal University, Wuhu, 241003, China

    Qingying Yu, Yonglong Luo, Chuanming Chen & Xiaohan Wang

  2. Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, 241003, China

    Qingying Yu, Yonglong Luo, Chuanming Chen & Xiaohan Wang

Authors
  1. Qingying Yu
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  2. Yonglong Luo
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  3. Chuanming Chen
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  4. Xiaohan Wang
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Correspondence to Yonglong Luo.

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Yu, Q., Luo, Y., Chen, C. et al. Trajectory outlier detection approach based on common slices sub-sequence. Appl Intell 48, 2661–2680 (2018). https://doi.org/10.1007/s10489-017-1104-z

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