Advertisement

Spatial-Temporal Recurrent Neural Network for Anomalous Trajectories Detection

  • Yunyao Cheng
  • Bin WuEmail author
  • Li Song
  • Chuan Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)

Abstract

Aiming to improve the quality of taxi service and protect the interests in passengers, anomalous trajectory detection attracts increasing attention. Most of the existing methods concentrate on the coordinate information about trajectories and learn the similarities between anomalous trajectories from a large number of coordinate sequences. These methods ignore the relationship of spatial-temporal and ignore the particularity of the whole trajectory. Through data analysis, we find that there are significant differences between normal trajectories and anomalous trajectories in terms of spatial-temporal characteristic. Meanwhile Recurrent Neural Network can use trajectory embedding to capture the sequential information on the trajectory. Consequently, we propose an efficient method named Spatial-Temporal Recurrent Neural Network (ST-RNN) using coordinate sequence and spatial-temporal sequence. ST-RNN combines the advantages of the Recurrent Neural Network (RNN) in learning sequence information and adds attention mechanism to the RNN to improve the performance of the model. The application of Spatial-Temporal Laws in anomalous trajectory detection also achieves a positive influence. Several experiments on a real-world dataset demonstrate that the proposed ST-RNN achieves state-of-the-art performance in most cases.

Keywords

Anomaly detection Recurrent Neural Network Spatial-temporal sequence 

Notes

Acknowledgement

This work is supported by the National Key Research and Development Program of China (2018YFC0831500).

References

  1. 1.
    Al-Dohuki, S.: SemanticTraj: a new approach to interacting with massive taxi trajectories. IEEE Trans. Vis. Comput. Graph. 23(1), 11–20 (2016)CrossRefGoogle Scholar
  2. 2.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
  3. 3.
    Bu, Y., Chen, L., Wai-Chee Fu, A., Liu, D.: Efficient anomaly monitoring over moving object trajectory streams. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 159–168. ACM (2009)Google Scholar
  4. 4.
    Chen, C., Zhang, D., Samuel Castro, P., Li, N., Sun, L., Li, S.: Real-time detection of anomalous taxi trajectories from GPS traces. In: Puiatti, A., Gu, T. (eds.) MobiQuitous 2011. LNICST, vol. 104, pp. 63–74. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-30973-1_6CrossRefGoogle Scholar
  5. 5.
    Chen, C., et al.: iBOAT: isolation-based online anomalous trajectory detection. IEEE Trans. Intell. Transp. Syst. 14(2), 806–818 (2013)CrossRefGoogle Scholar
  6. 6.
    Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference On Knowledge Discovery And Data Mining, pp. 785–794. ACM (2016)Google Scholar
  7. 7.
    Cheng, B., et al.: STL: online detection of taxi trajectory anomaly based on spatial-temporal laws. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11447, pp. 764–779. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-18579-4_45CrossRefGoogle Scholar
  8. 8.
    Ge, Y., Xiong, H., Liu, C., Zhou, Z.-H.: A taxi driving fraud detection system. In: 2011 IEEE 11th International Conference on Data Mining, pp. 181–190. IEEE (2011)Google Scholar
  9. 9.
    Ge, Y., Xiong, H., Zhou, Z.-H., Ozdemir, H., Yu, J., Lee, K.C.: Top-eye: top-k evolving trajectory outlier detection. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1733–1736. ACM (2010)Google Scholar
  10. 10.
    Graves, A., Mohamed, A.-R., Hinton, G.: Speech recognition with deep recurrent neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649. IEEE (2013)Google Scholar
  11. 11.
    Hallac, D., Bhooshan, S., Chen, M., Abida, K., Leskovec, J., et al.: Drive2vec: multiscale state-space embedding of vehicular sensor data. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3233–3238. IEEE (2018)Google Scholar
  12. 12.
    Lee, J.-G., Han, J., Li, X.: Trajectory outlier detection: a partition-and-detect framework. In: 2008 IEEE 24th International Conference on Data Engineering. IEEE, April 2008Google 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: Proceedings of 7th SIAM International Conference on Data Mining (2007)Google Scholar
  14. 14.
    Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1412–1421, Lisbon, Portugal. Association for Computational Linguistics, September 2015Google Scholar
  15. 15.
    Sillito, R.R., Fisher, R.B.: Semi-supervised learning for anomalous trajectory detection. In: BMVC (2008)Google Scholar
  16. 16.
    Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment TreeBank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013)Google Scholar
  17. 17.
    Song, L., Wang, R., Xiao, D., Han, X., Cai, Y., Shi, C.: Anomalous trajectory detection using recurrent neural network. In: Gan, G., Li, B., Li, X., Wang, S. (eds.) ADMA 2018. LNCS (LNAI), vol. 11323, pp. 263–277. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-05090-0_23CrossRefGoogle Scholar
  18. 18.
    Vazirgiannis, M., Wolfson, O.: A spatiotemporal model and language for moving objects on road networks. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 20–35. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-47724-1_2CrossRefzbMATHGoogle Scholar
  19. 19.
    Wu, H., Sun, W., Zheng, B.: A fast trajectory outlier detection approach via driving behavior modeling. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, pp. 837–846. ACM, New York (2017)Google Scholar
  20. 20.
    Yu, Y., Cao, L., Rundensteiner, E.A., Wang, Q.: Detecting moving object outliers in massive-scale trajectory streams. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 422–431. ACM, New York (2014)Google Scholar
  21. 21.
    Zhang, D., et al.: iBAT: detecting anomalous taxi trajectories from GPS traces. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 99–108. ACM (2011)Google Scholar
  22. 22.
    Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 29 (2015)Google Scholar
  23. 23.
    Zhu, J., Jiang, W., Liu, A., Liu, G., Zhao, L.: Time-dependent popular routes based trajectory outlier detection. WISE 2015. LNCS, vol. 9418, pp. 16–30. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-26190-4_2CrossRefGoogle Scholar
  24. 24.
    Zobel, J., Moffat, A.: Inverted files for text search engines. ACM Comput. Surv. 38(2), 6 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Beijing Key Laboratory of Intelligent Telecommunications Software and MultimediaBeijingChina

Personalised recommendations