Abstract
Cooperative Intelligent Transport Network is one of the most challenging issue in networking and computer science. In this area, huge amount of data are exchanged. Smart analysis of this collected data could be achieved for many purposes: traffic prediction, driver profile detection, anomaly detection, etc. Anomaly detection is an important issue for road operators. An anomaly on roads could be caused by various reasons: potholes, obstacles, weather conditions, etc. An early detection of such anomalies will reduce incident risks such as traffic jams, accidents. The aim of this paper is to collect message exchanges between vehicles and analyze trajectories. This analysis becomes difficult since a privacy principle is applied in the case of C-ITS. Indeed, each message sent is generated with an identifier of the sender. This identifier is kept only over a specified time interval thus one vehicle will have multiple identifiers. We first have to solve Trajectory-User Linking problem by chaining anonymous trajectories to potential vehicles by considering similarity in movement patterns. After that we apply various methods to check variations of trajectories from normal ones. When we observe some differences, we can raise an alarm about a potential anomaly. In order to check the validity of this work, we generated a large amount of messages exchanges by many vehicles using the Omnet simulator together with the Artery, Sumo plug-in. We applied various variations on some obtained trajectories. Finally, we ran our detection algorithm on the obtained trajectories using different parameters (angles, speed, acceleration) and obtained very interesting results in terms of detection rate.
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
Similar content being viewed by others
References
Shekhar, S., Xiong, H., Zhou, X. (eds.): Encyclopedia of GIS. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-17885-1
Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. 6(3), 1–41 (2015). https://doi.org/10.1145/2743025
Valdés, F., Güting, R.H.: A framework for efficient multi-attribute movement data analysis. VLDB J. 28(4), 427–449 (2018). https://doi.org/10.1007/s00778-018-0525-6
Alesiani, F., Moreira-Matias, L., Faizrahnemoon, M.: On learning from inaccurate and incomplete traffic flow data. IEEE Trans. Intell. Transport. Syst. 19(11), 3698–3708 (2018). https://doi.org/10.1109/TITS.2018.2857622
Wu, T., Qin, J., Wan, Y.: TOST: a topological semantic model for GPS trajectories inside road networks. IJGI 8(9), 410 (2019). https://doi.org/10.3390/ijgi8090410
Cao, Y., et al.: Effective spatio-temporal semantic trajectory generation for similar pattern group identification. Int. J. Mach. Learn. Cybern. 11(2), 287–300 (2019). https://doi.org/10.1007/s13042-019-00973-y
Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: Semantic trajectories: mobility data computation and annotation. ACM Trans. Intell. Syst. Technol. 4(3), 1 (2013). https://doi.org/10.1145/2483669.2483682
Nishad, A., Abraham, S.: SemTraClus: an algorithm for clustering and prioritizing semantic regions of spatio-temporal trajectories. Int. J. Comput. Appl. 1–10 (2019). https://doi.org/10.1080/1206212X.2019.1655853
Gao, Q., Zhou, F., Zhang, K., Trajcevski, G., Luo, X., Zhang, F.: Identifying human mobility via trajectory embeddings. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia, pp. 1689–1695 (August 2017). https://doi.org/10.24963/ijcai.2017/234
Zhou, F., Gao, Q., Trajcevski, G., Zhang, K., Zhong, T., Zhang, F.: Trajectory-user linking via variational autoencoder. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp. 3212–3218 (July 2018). https://doi.org/10.24963/ijcai.2018/446
Feng, J., et al.: DPLink: user identity linkage via deep neural network from heterogeneous mobility data. In: The World Wide Web Conference on - WWW 2019, San Francisco, CA, USA, pp. 459–469 (2019). https://doi.org/10.1145/3308558.3313424
Vicenzi, F., Petry, L.M.: Exploring frequency-based approaches for efficient trajectory classification. In: Proceedings of the 35th Annual ACM Symposium on Applied Computing - SAC 2020, March 30-April 3, pp. 624–631 (2020). https://doi.org/10.1145/3341105.3374045
Yu, Q., Luo, Y., Chen, C., Chen, S.: Trajectory similarity clustering based on multi-feature distance measurement. Appl. Intell. 49(6), 2315–2338 (2019). https://doi.org/10.1007/s10489-018-1385-x
Sabarish, B.A., Karthi, R., Gireeshkumar, T.: Clustering of trajectory data using hierarchical approaches. In: Hemanth, D.J., Smys, S. (eds.) Computational Vision and Bio Inspired Computing. LNCVB, vol. 28, pp. 215–226. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-71767-8_18
Ferrero, C.A., Alvares, L.O., Zalewski, W., Bogorny, V.: MOVELETS: exploring relevant subtrajectories for robust trajectory classification. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing-SAC 2018, Pau, France, pp. 849–856 (2018). https://doi.org/10.1145/3167132.3167225
Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: Proceedings 18th International Conference on Data Engineering, San Jose, CA, USA, pp. 673–684 (2002). https://doi.org/10.1109/ICDE.2002.994784
Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data - SIGMOD 2005, Baltimore, Maryland, p. 491 (2005). https://doi.org/10.1145/1066157.1066213
Kang, H.-Y., Kim, J.-S., Li, K.-J.: Similarity measures for trajectory of moving objects in cellular space. In: Proceedings of the 2009 ACM symposium on Applied Computing - SAC 2009, Honolulu, Hawaii, p. 1325 (2009). https://doi.org/10.1145/1529282.1529580
Ying, J.J.-C., Lu, E.H.-C., Lee, W.-C., Weng, T.-C., Tseng, V.S.: Mining user similarity from semantic trajectories. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks - LBSN 2010, San Jose, California, p. 19 (2010). https://doi.org/10.1145/1867699.1867703
Furtado, A.S., Kopanaki, D., Alvares, L.O., Bogorny, V.: Multidimensional similarity measuring for semantic trajectories: multidimensional similarity Measuring for Semantic Trajectories. Trans. in GIS 20(2), 280–298 (2016). https://doi.org/10.1111/tgis.12156
Lehmann, A.L., Alvares, L.O., Bogorny, V.: SMSM: a similarity measure for trajectory stops and moves. Int. J. Geogr. Inf. Sci. 33(9), 1847–1872 (2019). https://doi.org/10.1080/13658816.2019.1605074
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1–58 (2009)
Wang, X., Fagette, A., Sartelet, P., Sun, L.: A probabilistic tensor factorization approach to detect anomalies in spatiotemporal traffic activities. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 1658–1663. IEEE (October 2019)
Wang, H., Bah, M.J., Hammad, M.: Progress in outlier detection techniques: a survey. IEEE Access 7, 107964–108000 (2019)
Petit, J., Schaub, F., Feiri, M., Kargl, F.: Pseudonym schemes in vehicular networks: a survey. IEEE Commun. Surv. Tutorials 17(1), 228–255 (2015). https://doi.org/10.1109/COMST.2014.2345420
ETSI E. 302 637–2 V1. 3.1-Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Part 2: Specification of Cooperative Awareness Basic Service. ETSI (September 2014)
Page, E.S.: Continuous inspection schemes. Biometrika 41(1/2), 100–115 (1954)
Golab, L., Özsu, M.T.: Issues in data stream management. ACM Sigmod Rec. 32(2), 5–14 (2003)
Gama, J., Sebastião, R., Rodrigues, P.P.: On evaluating stream learning algorithms. Mach. Learn. 90(3), 317–346 (2012). https://doi.org/10.1007/s10994-012-5320-9
Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Moso, J.C. et al. (2020). Anomaly Detection on Roads Using C-ITS Messages. In: Krief, F., Aniss, H., Mendiboure, L., Chaumette, S., Berbineau, M. (eds) Communication Technologies for Vehicles. Nets4Cars/Nets4Trains/Nets4Aircraft 2020. Lecture Notes in Computer Science(), vol 12574. Springer, Cham. https://doi.org/10.1007/978-3-030-66030-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-66030-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66029-1
Online ISBN: 978-3-030-66030-7
eBook Packages: Computer ScienceComputer Science (R0)