Abstract
Aiming to promote the standardization of taxi services and protect the interests of passengers, many methods have been proposed to detect taxi trajectory anomaly based on collected large-scale taxi traces. However, most existing methods usually employ a counting-based policy to differentiate normal trajectories from anomalous ones, which may give rise to high false positives. In this paper, we propose an online detection method, named Spatial-Temporal Laws (STL). The basic idea of STL is that, given the displacement from the source point to the current point of a testing trajectory, if the current point is normal, either its driving distance or driving time should lie in a normal range. STL learns the two ranges from historical trajectories by defining two spatial-temporal models: one characterizing the relationship between displacement and driving distance, and another depicting the relationship between displacement and driving time. Consequently, STL is more precise compared with the counting-based methods, greatly reducing the number of false positives. Based on large-scale real-world taxi traces, STL is evaluated through a series of experiments which demonstrate its effectiveness and performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, C., Zhang, D., Castro, P.S., 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)
Ge, Y., Xiong, H., Liu, C., Zhou, Z.: A taxi driving fraud detection system. In: ICDM, pp. 181–190 (2011)
Lee, J., Han, J., Li, X.: Trajectory outlier detection: a partition-and-detect framework. In: ICDE, pp. 140–149 (2008)
Li, X., Li, Z., Han, J., Lee, J.: Temporal outlier detection in vehicle traffic data. In: ICDE, pp. 1319–1322 (2009)
Liu, S., Ni, L.M., Krishnan, R.: Fraud detection from taxis’ driving behaviors. IEEE Trans. Veh. Technol. 63(1), 464–472 (2014)
Newson, P., Krumm, J.: Hidden Markov map matching through noise and sparseness. In: ACM-GIS, pp. 336–343 (2009)
Scholz, F.: Maximum likelihood estimation. In: Encyclopedia of Statistical Sciences (1985)
Wu, H., Sun, W., Zheng, B.: A fast trajectory outlier detection approach via driving behavior modeling. In: CIKM, pp. 837–846 (2017)
Yuan, J., et al.: T-drive: driving directions based on taxi trajectories. In: ACM-GIS, pp. 99–108 (2010)
Zhang, D., Li, N., Zhou, Z., Chen, C., Sun, L., Li, S.: iBAT: detecting anomalous taxi trajectories from GPS traces. In: UbiComp, pp. 99–108 (2011)
Zhou, Z., et al.: A method for real-time trajectory monitoring to improve taxi service using GPS big data. Inf. Manage. 53(8), 964–977 (2016)
Zhu, J., Jiang, W., Liu, A., Liu, G.: Effective and efficient trajectory outlier detection based on time-dependent popular route. World Wide Web 20(1), 111–134 (2017)
Zhu, J., Jiang, W., Liu, A., Liu, G., Zhao, L.: Time-dependent popular routes based trajectory outlier detection. In: Wang, J., et al. (eds.) WISE 2015. LNCS, vol. 9418, pp. 16–30. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26190-4_2
Acknowledgments
This work was supported by National Key R&D Program of China (2018YFB1003800), the National Science Foundation of China (61772334, 61702151, 61572324), the Joint Key Project of the National Natural Science Foundation of China (U1736207), and Shanghai Talent Development Fund, Shanghai Jiao Tong arts and science inter-project (15JCMY08).
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, B. et al. (2019). STL: Online Detection of Taxi Trajectory Anomaly Based on Spatial-Temporal Laws. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-18579-4_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18578-7
Online ISBN: 978-3-030-18579-4
eBook Packages: Computer ScienceComputer Science (R0)