Time-Dependent Popular Routes Based Trajectory Outlier Detection

  • Jie Zhu
  • Wei Jiang
  • An Liu
  • Guanfeng Liu
  • Lei ZhaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9418)


With the rapid proliferation of the GPS-equipped devices, a myriad of trajectory data representing the mobility of the various moving objects in two-dimensional space have been generated. In this paper, we aim to detect the anomalous trajectories from the trajectory dataset and propose a novel time-dependent popular routes based algorithm. In our algorithm, spatial and temporal abnormalities are taken into consideration simultaneously to improve the accuracy of the detection. For each group of trajectories with the same source and destination, we firstly design a time-dependent transfer graph and in different time period, we can obtain the top-k most popular routes as reference routes. For a pending inspecting trajectory in this time period, we will label it as an outlier if has a great difference with the selected routes in both spatial and temporal dimension. To quantitatively measure the “difference” between a trajectory and a route, we propose a novel time-dependent distance measure which is based on Edit distance in both spatial and temporal domain. The comparative experimental results with two famous trajectory outlier detection methods TRAOD and IBAT on real dataset demonstrate the good accuracy and efficiency of the proposed algorithm.


Outlier detection Time-dependent popular route Trajectory pattern mining 


  1. 1.
    Ye, Y., Zheng, Y., Chen, Y., Feng, J., Xie, X.: Mining individual life pattern based on location history. In: IEEE MDM, pp. 1–10 (2009)Google Scholar
  2. 2.
    Zheng, Y., Xie, X., Ma, W.-Y.: Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33, 32–39 (2010)Google Scholar
  3. 3.
    Chen, Z., Shen, H.T., Zhou, X.: Discovering popular routes from trajectories. In: IEEE ICDE, pp. 900–911 (2011)Google Scholar
  4. 4.
    Wei, L.-Y., Zheng, Y., Peng, W.-C.: Constructing popular routes from uncertain trajectories. In: ACM SIGKDD, pp. 195–203 (2012)Google Scholar
  5. 5.
    Zheng, Y., Liu, L., Wang, L., Xie, X.: Learning transportation mode from raw GPS data for geographic applications on the web. In: WWW, pp. 247–256 (2008)Google Scholar
  6. 6.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2006)Google Scholar
  7. 7.
    Lee, J.-G., Han, J., Li, X.: Trajectory outlier detection: A partition-and-detect framework. In: IEEE ICDE, pp. 140–149 (2008)Google Scholar
  8. 8.
    Zhang, D., Li, N., Zhou, Z.-H., Chen, C., Sun, L., Li, S.: iBAT: detecting anomalous taxi trajectories from GPS traces. In: ACM UbiComp, pp. 99–108 (2011)Google Scholar
  9. 9.
    Luo, W., Tan, H., Chen, L., Ni, L.M.: Finding time period-based most frequent path in big trajectory data. In: ACM SIGMOD, pp. 713–724 (2013)Google Scholar
  10. 10.
    Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)CrossRefGoogle Scholar
  12. 12.
    Li, X., Li, Z., Han, J., Lee, J.-G.: Temporal outlier detection in vehicle traffic data. In IEEE ICDE, pp. 1319–1322 (2009)Google Scholar
  13. 13.
    Gupta, M., Gao, J., Aggarwal, C., Han, J.: Outlier detection for temporal data. Synth. Lect. Data Min. Knowl. Discov. 5, 1–129 (2014)CrossRefGoogle Scholar
  14. 14.
    Yuan, G., Xia, S., Zhang, L., Zhou, Y., Ji, C.: Trajectory outlier detection algorithm based on structural features. J. Comput. Inf. Syst. 7(11), 4137–4144 (2011)Google Scholar
  15. 15.
    Mohamad, I., Ali, M., Ismail, M.: Abnormal driving detection using real time global positioning system data. In: Space Science and Communication (IconSpace), pp. 1–6. IEEE (2011)Google Scholar
  16. 16.
    Sillito, R.R., Fisher, R.B.: Semi-supervised learning for anomalous trajectory detection. In: BMVC, pp. 1–10 (2008)Google Scholar
  17. 17.
    Li, X., Han, J., Kim, S., Gonzalez, H.: Roam: rule-and motif-based anomaly detection in massive moving object data sets. In: SIAM SDM, pp. 273–284 (2007)Google Scholar
  18. 18.
    Yu, Y., Cao, L., Rundensteiner, E.A., Wang, Q.: Detecting moving object outliers in massive-scale trajectory streams. In: ACM KDD, pp. 422–431 (2014)Google Scholar
  19. 19.
    Bu, Y., Chen, L., Fu, A. W.-C., Liu, D.: Efficient anomaly monitoring over moving object trajectory streams. In: ACM SIGKDD, pp. 159–168 (2009)Google Scholar
  20. 20.
    Chen, C., Zhang, D., Castro, P.S., Li, N., Sun, L., Li, S., Wang, Z.: iBOAT: Isolation-based online anomalous trajectory detection. In: IEEE TITS(2013)Google Scholar
  21. 21.
    Gonzalez, H., Han, J., Li, X., Myslinska, M., Sondag, J.P.: Adaptive fastest path computation on a road network: a traffic mining approach. In: VLDB (2007)Google Scholar
  22. 22.
    Sacharidis, D., Patroumpas, K., Terrovitis, M., Kantere, V., Potamias, M., Mouratidis, K., Sellis, T.: On-line discovery of hot motion paths. In: ACM EDBT (2008)Google Scholar
  23. 23.
    Kanoulas, E., Du, Y., Xia, T., Zhang, D.: Finding fastest paths on a road network with speed patterns. In: IEEE ICDE, pp. 10–10 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jie Zhu
    • 1
  • Wei Jiang
    • 1
  • An Liu
    • 1
    • 2
  • Guanfeng Liu
    • 1
    • 2
  • Lei Zhao
    • 1
    • 2
    Email author
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

Personalised recommendations