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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abe, N., Zadrozny, B., Langford, J.: Outlier detection by active learning. In: Proc. KDD (2006)Google Scholar
  2. 2.
    Angiulli, F., Fassetti, F.: Dolphin: An efficient algorithm for mining distance-based outliers in very large datasets. ACM-TKDD 3(1) (2009)Google Scholar
  3. 3.
    Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: Identifying density-based local outliers. In: Proc. SIGMOD (2000)Google Scholar
  4. 4.
    Bu, Y., Chen, L., Fu, A.W.C., Liu, D.: Efficient anomaly monitoring over moving object trajectory streams. In: Proc. KDD (2009)Google Scholar
  5. 5.
    Chang, H., Tai, Y., Chen, H., Hsu, J.Y.: iTaxi: Context-aware taxi demand hotspots prediction using ontology and data mining approaches. In: Proc. of TAAI (2008)Google Scholar
  6. 6.
    Froehlich, J., Krumm, J.: Route prediction from trip observations. In: Proc. SAE (2008)Google Scholar
  7. 7.
    Ge, Y., Xiong, H., Zhou, Z.H., Ozdemir, H., Yu, J., Lee, K.C.: Top-Eye: Top-k evolving trajectory outlier detection. In: Proc. CIKM (2010)Google Scholar
  8. 8.
    He, Z., Xu, X., Deng, S.: Discovering cluster-based local outliers. Pattern Recognition Letters 24(9-10), 1641–1650 (2003)CrossRefzbMATHGoogle Scholar
  9. 9.
    Knorr, E.M., Ng, R.T., Tucakov, V.: Distance-based outliers: Algorithms and applications. VLDB Journal 8(3-4), 237–253 (2000)CrossRefGoogle Scholar
  10. 10.
    Krumm, J., Horvitz, E.: Predestination: Inferring Destinations from Partial Trajectories. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 243–260. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Lee, J., Han, J., Li, X.: Trajectory Outlier Detection: A Partition-and-Detect Framework. In: Proc. ICDE (2008)Google Scholar
  12. 12.
    Li, B., Zhang, D., Sun, L., Chen, C., Li, S., Qi, G., Yang, Q.: Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset. In: PerCom Workshops (2011)Google Scholar
  13. 13.
    Li, X., Han, J., Kim, S., Gonzalez, H.: ROAM: Rule- and motif-based anomaly detection in massive moving object data sets. In: Proc. SDM (2007)Google Scholar
  14. 14.
    Li, X., Li, Z., Han, J., Lee, J.G.: Temporal outlier detection in vehicle traffic data. In: Proc. ICDE (2009)Google Scholar
  15. 15.
    Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Learning and inferring transportation routines. Artificial Intelligence 171(5–6), 311–331 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Liao, Z., Yu, Y., Chen, B.: Anomaly detection in GPS data based on visual analytics. In: Proc. VAST (2010)Google Scholar
  17. 17.
    Lippi, M., Bertini, M., Frasconi, P.: Collective Traffic Forecasting. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part II. LNCS, vol. 6322, pp. 259–273. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Liu, F.T., Ting, K.M., Zhou, Z.: Isolation Forest. In: Proc. ICDM (2008)Google Scholar
  19. 19.
    Liu, L., Andris, C., Ratti, C.: Uncovering cabdrivers’ behavior patterns from their digital traces. Computers, Environment and Urban Systems 34, 541–548 (2010)CrossRefGoogle Scholar
  20. 20.
    Phithakkitnukoon, S., Veloso, M., Bento, C., Biderman, A., Ratti, C.: Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City. In: de Ruyter, B., Wichert, R., Keyson, D.V., Markopoulos, P., Streitz, N., Divitini, M., Georgantas, N., Mana Gomez, A. (eds.) AmI 2010. LNCS, vol. 6439, pp. 86–95. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Qi, G., Li, X., Li, S., Pan, G., Wang, Z.: Measuring Social Functions of City Regions from Large-scale Taxi Behaviors. In: PerCom Workshop (2011)Google Scholar
  22. 22.
    Sillito, R., Fisher, R.B.: Semi-supervised learning for anomalous trajectory detection. In: Proc. BMVC (2008)Google Scholar
  23. 23.
    Yuan, J., Zheng, Y.: T-Drive: Driving Directions Based on Taxi Trajectories. In: ACM SIGSPATIAL GIS (2010)Google Scholar
  24. 24.
    Zhang, D., Guo, B., Yu, Z.: The Emergence of Social and Community Intelligence. IEEE Computer 44(7), 21–28 (2011)CrossRefGoogle Scholar
  25. 25.
    Zhang, D., Li, N., Zhou, Z., Chen, C., Sun, L., Li, S.: iBAT: Detecting Anomalous Taxi Trajectories from GPS Traces. In: Proc. of UbiComp (2011)Google Scholar
  26. 26.
    Zheng, V.W., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proc. AAAI (2010)Google Scholar
  27. 27.
    Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban Computing with Taxicabs. In: Proc. of Ubicomp (2011)Google Scholar
  28. 28.
    Zheng, Y., Zhang, L., Xie, X., Ma, W.: Mining interesting locations and travel sequences from gps trajectories. In: Proc. WWW (2009)Google Scholar
  29. 29.
    Ziebart, B.D., Maas, A.L., Dey, A.K., Bagnell, J.A.: Navigate Like a Cabbie: Probabilistic Reasonoing from Observed Context-Aware Behavior. In: Proc. of Ubicomp (2008)Google Scholar

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

Personalised recommendations