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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-Dohuki, S.: SemanticTraj: a new approach to interacting with massive taxi trajectories. IEEE Trans. Vis. Comput. Graph. 23(1), 11–20 (2016)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
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)
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_6
Chen, C., et al.: iBOAT: isolation-based online anomalous trajectory detection. IEEE Trans. Intell. Transp. Syst. 14(2), 806–818 (2013)
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)
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_45
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)
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)
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)
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)
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 2008
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)
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 2015
Sillito, R.R., Fisher, R.B.: Semi-supervised learning for anomalous trajectory detection. In: BMVC (2008)
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)
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_23
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_2
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)
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)
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)
Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 29 (2015)
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_2
Zobel, J., Moffat, A.: Inverted files for text search engines. ACM Comput. Surv. 38(2), 6 (2006)
Acknowledgement
This work is supported by the National Key Research and Development Program of China (2018YFC0831500).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Cheng, Y., Wu, B., Song, L., Shi, C. (2019). Spatial-Temporal Recurrent Neural Network for Anomalous Trajectories Detection. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-35231-8_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35230-1
Online ISBN: 978-3-030-35231-8
eBook Packages: Computer ScienceComputer Science (R0)