Abstract
Due to the prevalence of GPS-enabled devices and wireless communication technology, spatial trajectories have become the basis of many location based applications, e.g., Didi. However, trajectory data suffers low quality problems causing by sensor errors and artificial forgeries. Sensor errors are inevitable while forgeries are always constructed on bad purpose. For example, some Didi drivers use GPS simulators to generate forgery trajectories and make fake transactions. In this work we aim to distinguish whether a given trajectory is a GPS simulated trajectory. By formulating this task as the problem of traffic speed extracting and irregular measuring, we propose a simulated trajectory detection framework. In traffic speed extracting phase, we first divide time into time slots and then extract the regular speed of each road during each time slot. In irregular measuring phase, we propose three methods to measure the distance between the speed of the given trajectory and the real traffic speeds. For empirical study, we apply our solution to a real trajectory dataset and have found that the simulated trajectory detection framework can detect most forgery trajectories.
W. Chen—Equal Contribution.
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
Cai, Y., Ng, R.: Indexing spatio-temporal trajectories with chebyshev polynomials. In: SIGMOD, pp. 599–610 (2004)
Chakka, V., Everspaugh, A., Patel, J.: Indexing large trajectory data sets with seti. In: CIDR (2003)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 75–79 (2009)
Wenk, C., Salas, R., Pfoser, D.: Addressing the need for map-matching speed: localizing global curvematching algorithms. In: Proceedings of the 18th International Conference on Scientific and Statistical Database Management (2006)
Eskin, E., Arnold, A., Prerau, M.: A geometric framework for unsupervised anomaly detection: detecting intrusions in unlabeled data. In: Barbará, D., Jajodia, S. (eds.) Applications of Data Mining in Computer Security. Springer, New York (2002)
Alt, H., Efrat, A., Rote, G., Wenk, C.: Matching planar maps. J. Algorithms 49(2), 262–283 (2003)
Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)
Hsiao, K.-J., Xu, K.S., Calder, J., Hero III, A.O.: Multi-criteria anomaly detection using pareto depth analysis. Eprint Arxiv, vol. 25, pp. 854–862 (2011)
Yin, H., Wolfson, O.: A weight-based map matching method in moving objects databases. In: International Conference on Scientific and Statistical Database Management, pp. 437–438 (2004)
Jeung, H., Shen, H., Zhou, X.: Convoy queries in spatio-temporal databases. In: ICDE, pp. 1457–1459 (2008)
Jeung, H., Yiu, M., Zhou, X., Jensen, C., Shen, H.: Discovery of convoys in trajectory databases. PVLDB 1, 1068–1080 (2008). VLDB Endowment
Greenfeld, J.: Matching GPS observations to locations on a digital map. In: Proceedings of 81th Annual Meeting of the Transportantion Research Board (2002)
Lee, J., Han, J., Whang, K.: Trajectory clustering: a partition-and-group framework. In: SIGMOD, pp. 593–604. ACM (2007)
Li, H., Kulik, L., Ramamohanarao, K.: Robust inferences of travel paths from GPS trajectories. Int. J. Geog. Inform. Sci. 29(12), 2194–2222 (2015)
Li, Z., Ding, B., Han, J., Kays, R.: Swarm: mining relaxed temporal moving object clusters. PVLDB 3, 723–734 (2010)
Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)
Ni, J., Ravishankar, C.: Indexing spatio-temporal trajectories with efficient polynomial approximations. TKDE 19(5), 663–678 (2007)
Nordmann, L., Pham, H.: Weighted voting systems. IEEE Trans. Reliab. 48(1), 42–49 (1999)
Pink, O., Hummel, B.: A statistical approach to map matching using road network geometry, topology and vehicular motion constraints. In: International IEEE Conference on Intelligent Transportation Systems, pp. 862–867 (2008)
Pfoser, D., Jensen, C., Theodoridis, Y.: Novel approaches to the indexing of moving object trajectories. In: VLDB, pp. 395–406 (2000)
Spaccapietra, S., Parent, C., Damiani, M.L., de Macedo, J.A., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data Knowl. Eng. 65(1), 126–146 (2008)
Sricharan, K., Hero III, A.O.: Efficient anomaly detection using bipartite k-NN graphs. In: Advances in Neural Information Processing Systems, vol. 24, pp. 478–486 (2011)
Su, H., Zheng, K., Huang, J., Wang, H., Zhou, X.: Calibrating trajectory data for spatio-temporal similarity analysis. VLDB J. 24(1), 93–116 (2015)
Wang, H., Zheng, K., Xu, J., Zheng, B., Zhou, X., Sadiq, S.: SharkDB: an in-memory column-oriented trajectory storage. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1409–1418. ACM (2014)
Yan, Z., Spaccapietra, S., et al.: Towards semantic trajectory data analysis: a conceptual and computational approach. In: VLDB Ph.D Workshop (2009)
Acknowledgments
This research is supported by UESTC (Grant No: ZYGX2016KYQD135).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Su, H. et al. (2017). GPS-Simulated Trajectory Detection. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-55699-4_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55698-7
Online ISBN: 978-3-319-55699-4
eBook Packages: Computer ScienceComputer Science (R0)