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Embedding geographic information for anomalous trajectory detection

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Abstract

Anomalous trajectory detection is a crucial task in trajectory mining fields. Traditional anomalous trajectory detection methods mainly focus on the differences of a new trajectory and the historical trajectory with density and isolation techniques, which may suffer from the following two disadvantages. (1) They cannot capture the sequential information of the trajectory well. (2) They cannot make use of the common information of the trajectory points. To overcome the above shortcomings, we propose a novel method called A nomalous T rajectory D etection using R ecurrent N eural N etwork (ATD − RNN) which characterizes the trajectory with the learned trajectory embedding. The trajectory embedding can capture the sequential information of the trajectory and depict the internal characteristics between abnormal and normal trajectories. In order to learn the high-quality trajectory embedding, we further propose an attention mechanism to aggregate the long sequential information. Furthermore, to alleviate the data sparsity problem, we augment the datasets between a source and a destination by taking the relevant trajectories into consideration simultaneously. Extensive experiments on real-world datasets validate the effectiveness of our proposed methods.

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Notes

  1. https://github.com/LeeSongt/ATD-RNN

  2. https://www.kaggle.com/c/pkdd-15-predict-taxi-service-trajectory-i/data

References

  1. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, vol. 16, pp 265–283 (2016)

  2. Argyriou, A., Evgeniou, T., Pontil, M.: Multi-task feature learning. In: Advances in Neural Information Processing Systems, pp. 41–48 (2007)

  3. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Computer Science (2014)

  4. Bhowmick, K., Narvekar, M.: Trajectory outlier detection for traffic events: a survey. In: Intelligent Computing and Information and Communication, pp 37–46. Springer (2018)

  5. Bu, Y., Chen, L., Fu, A. W. C., 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)

  6. Cai, Y., Wang, H., Chen, X., Jiang, H.: Trajectory-based anomalous behaviour detection for intelligent traffic surveillance. IET Intelligent Transport Systems 9(8), 810–816 (2015)

    Article  Google Scholar 

  7. Cao, H., Xu, F., Sankaranarayanan, J., Li, Y., Samet, H.: Habit2vec: Trajectory semantic embedding for living pattern recognition in population. IEEE Transactions on Mobile Computing (2019)

  8. Chawla, S., Zheng, Y., Hu, J.: Inferring the root cause in road traffic anomalies. In: 2012 IEEE 12th International Conference on Data Mining, pp. 141–150 (2012)

  9. Chen, C., Zhang, D., Castro, P. S., Li, N., Sun, L., Li, S., Wang, Z.: iboat: Isolation-based online anomalous trajectory detection. IEEE Trans. Intell. Transp. Syst. 14(2), 806–818 (2013)

    Article  Google Scholar 

  10. 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)

  11. Djenouri, Y., Belhadi, A., Lin, J. C. W., Djenouri, D., Cano, A.: A survey on urban traffic anomalies detection algorithms. IEEE Access 7, 12,192–12,205 (2019)

    Article  Google Scholar 

  12. Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 1019–1027 (2016)

  13. Gao, Q., Zhou, F., Zhang, K., Trajcevski, G., Luo, X., Zhang, F.: Identifying human mobility via trajectory embeddings. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 1689–1695. AAAI Press (2017)

  14. 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)

  15. 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 (ICASSP), pp. 6645–6649. IEEE (2013)

  16. Kingma, D. P., Ba, J.: Adam: A method for stochastic optimization. arXiv:1412.6980 (2014)

  17. Knorr, E. M., Ng, R. T., Tucakov, V.: Distance-based outliers: algorithms and applications. The VLDB Journal—The International Journal on Very Large Data Bases 8(3–4), 237–253 (2000)

    Article  Google Scholar 

  18. Kong, X., Song, X., Xia, F., Guo, H., Wang, J., Tolba, A.: Lotad: Long-term traffic anomaly detection based on crowdsourced bus trajectory data. World Wide Web 21(3), 825–847 (2018)

    Article  Google Scholar 

  19. Kumar, S., Zhang, X., Leskovec, J.: Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1269–1278. ACM (2019)

  20. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)

  21. Lee, J. G., Han, J., Li, X.: Trajectory outlier detection: a partition-and-detect framework. In: IEEE 24th International Conference on Data Engineering, 2008, pp. 140–149. ICDE 2008 (2008)

  22. Li, X., Han, J., Kim, S., Gonzalez, H.: Roam: Rule-and motif-based anomaly detection in massive moving object data sets. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 273–284. SIAM (2007)

  23. Liu, W., Zheng, Y., Chawla, S., Yuan, J., Xing, X.: Discovering spatio-temporal causal interactions in traffic data streams. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1010–1018. ACM (2011)

  24. Luong, M. T., Pham, H., Manning, C. D.: Effective approaches to attention-based neural machine translation. arXiv:1508.04025 (2015)

  25. Lv, Z., Xu, J., Zhao, P., Liu, G., Zhao, L., Zhou, X.: Outlier trajectory detection: a trajectory analytics based approach. In: International Conference on Database Systems for Advanced Applications, pp. 231–246. Springer (2017)

  26. Maaten, L.v.d., Hinton, G.: Visualizing data using t-sne. J Mach Learning Res 9(Nov), 2579–2605 (2008)

    MATH  Google Scholar 

  27. Meng, F., Guan, Y., Lv, S., Wang, Z., Xia, S.: An overview on trajectory outlier detection. Artif. Intell. Rev. (10), pp. 1–20 (2018)

  28. Meng, F., Yuan, G., Lv, S., Wang, Z., Xia, S.: An overview on trajectory outlier detection. Artif. Intell. Rev. 52(4), 2437–2456 (2019)

    Article  Google Scholar 

  29. Mnih, A., Kavukcuoglu, K.: Learning word embeddings efficiently with noise-contrastive estimation. In: Advances in Neural Information Processing Systems, pp. 2265–2273 (2013)

  30. Munoz-Organero, M., Ruiz-Blaquez, R., Sánchez-Fernández, L.: Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on gps traces while driving. Comput. Environ. Urban. Syst. 68, 1–8 (2018)

    Article  Google Scholar 

  31. Nguyen, H., Liu, W., Chen, F.: Discovering congestion propagation patterns in spatio-temporal traffic data. IEEE Transactions on Big Data 3(2), 169–180 (2016)

    Article  Google Scholar 

  32. Pandhre, S., Mittal, H., Gupta, M., Balasubramanian, V. N.: Stwalk: learning trajectory representations in temporal graphs. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, pp. 210–219. ACM (2018)

  33. Pang, L. X., Chawla, S., Liu, W., Zheng, Y.: On mining anomalous patterns in road traffic streams. In: International Conference on Advanced Data Mining and Applications, pp. 237–251. Springer (2011)

  34. Pfahringer, B., Bensusan, H., Giraud-Carrier, C. G.: Meta-learning by landmarking various learning algorithms. In: ICML, pp. 743–750 (2000)

  35. Rocktäschel, T., Grefenstette, E., Hermann, K. M., Kočiskỳ, T., Blunsom, P.: Reasoning about entailment with neural attention. arXiv:1509.06664 (2015)

  36. Rush, A. M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. arXiv:1509.00685 (2015)

  37. Sharma, S., Kiros, R., Salakhutdinov, R.: Action recognition using visual attention. Computer Science (2017)

  38. Shi, Y., Deng, M., Yang, X., Gong, J.: Detecting anomalies in spatio-temporal flow data by constructing dynamic neighbourhoods. Comput. Environ. Urban. Syst. 67, 80–96 (2018)

    Article  Google Scholar 

  39. Sillito, R. R., Fisher, R. B.: Semi-supervised learning for anomalous trajectory detection. In: BMVC, vol. 1, pp. 035–1 (2008)

  40. Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, pp. 4077–4087 (2017)

  41. Srivatsa, M., Ganti, R., Wang, J., Kolar, V.: Map matching:facts and myths. In: ACM Sigspatial International Conference on Advances in Geographic Information Systems (2013)

  42. Tang, J., Ngan, H. Y.: Traffic outlier detection by density-based bounded local outlier factors. Information Technology in Industry 4(1), 6 (2016)

    Google Scholar 

  43. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: International Conference on World Wide Web (2015)

  44. Torrey, L., Shavlik, J.: Transfer learning. In: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, pp. 242–264. IGI Global (2010)

  45. Wang, D., Peng, C., Zhu, W.: Structural deep network embedding. In: ACM Sigkdd International Conference on Knowledge Discovery & Data Mining (2016)

  46. Wu, F., Fu, K., Wang, Y., Xiao, Z., Fu, X.: A spatial-temporal-semantic neural network algorithm for location prediction on moving objects. Algorithms 10(2), 37 (2017)

    Article  MathSciNet  Google Scholar 

  47. 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, pp. 837–846. ACM (2017)

  48. Ying, J. J. C., Lee, W. C., Weng, T. C., Tseng, V. S.: Semantic trajectory mining for location prediction. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 34–43. ACM (2011)

  49. 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, pp. 422–431. ACM (2014)

  50. Zhang, D., Li, N., Zhou, Z. H., Chen, C., Sun, L., Li, S.: ibat: detecting anomalous taxi trajectories from gps traces. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 99–108. ACM (2011)

  51. Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

  52. Zhao, W. X., Zhou, N., Sun, A., Wen, J. R., Han, J., Chang, E. Y.: A time-aware trajectory embedding model for next-location recommendation. Knowl. Inf. Syst., pp. 1–21 (2017)

  53. Zheng, Y.: Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST) 6(3), 29 (2015)

    Google Scholar 

  54. Zhou, F., Gao, Q., Trajcevski, G., Zhang, K., Zhong, T., Zhang, F.: Trajectory-user linking via variational autoencoder. In: IJCAI, pp. 3212–3218 (2018)

  55. Zhu, J., Jiang, W., Liu, A., Liu, G., Zhao, L.: Time-dependent popular routes based trajectory outlier detection. In: International Conference on Web Information Systems Engineering, pp. 16–30. Springer (2015)

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (No. 61772082, 61702296, 61375058), and the Beijing Municipal Natural Science Foundation (4182043).

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Haidian District, Beijing, 100876, China

    Ding Xiao, Ruijia Wang, Xiaotian Han & Chuan Shi

  2. Beijing University of Posts and Telecommunications, JD Intelligent Cities Business Unit, JD Digits, Beijing, China

    Li Song

  3. Big Data Center, PICC Property and Casualty Company Limited, Beijing, China

    Yanan Cai

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  1. Ding Xiao
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  2. Li Song
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  4. Xiaotian Han
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Correspondence to Chuan Shi.

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Xiao, D., Song, L., Wang, R. et al. Embedding geographic information for anomalous trajectory detection. World Wide Web 23, 2789–2809 (2020). https://doi.org/10.1007/s11280-020-00812-z

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