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Taxi Driving Anomalous Route Detection Using GPS Sampling Data

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Intelligent Computing, Networked Control, and Their Engineering Applications (ICSEE 2017, LSMS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 762))

Abstract

This paper proposed a method of detecting taxi driving anomalous route using the GPS sampling data. After analyzing the characteristics of sampling data, such as discrete and uneven, and taking into account that the traditional anomaly detection methods are hard to be applied directly in this field as well as high computation complexity, the mapping trajectory is defined and the new anomaly detection method is proposed based on the grid concept, which doesn’t require measure the distance or density during anomaly detection procedure and thus alleviates the computing resource requirements. To validate the proposed method, the real-life GPS sampling dataset is used and the experimental results confirm that our proposed method is effective.

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References

  1. Angiulli, F., Fassetti, F.: Dolphin: an efficient algorithm for mining distance-based outliers in very large datasets. ACM Trans. Knowl. Discov. Data (TKDD) 3, 1–57 (2009)

    Article  Google Scholar 

  2. Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: ACM SIGMOD Record, pp. 93–104. ACM (2000)

    Google Scholar 

  3. 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. LNICSSITE, vol. 104, pp. 63–74. Springer, Heidelberg (2012). doi:10.1007/978-3-642-30973-1_6

    Chapter  Google Scholar 

  4. Daneshpazhouh, A., Sami, A.: Entropy-based outlier detection using semi-supervised approach with few positive examples. Pattern Recogn. Lett. 49, 77–84 (2014)

    Article  Google Scholar 

  5. Ding, Z., Fei, M., Du, D., Yang, F.: Streaming data anomaly detection method based on hyper-grid structure and online ensemble learning. Soft Comput. 1–13 (2016)

    Google Scholar 

  6. Ge, Y., Xiong, H., Liu, C., Zhou, Z.-H.: A taxi driving fraud detection system. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 181–190. IEEE, Vancouver (2011)

    Google Scholar 

  7. Huang, H., Yoo, S., Qin, H., Yu, D.T.: Physics-based anomaly detection defined on manifold space. ACM Trans. Knowl. Discov. Data 9, 1–39 (2014)

    Article  Google Scholar 

  8. Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data 6, 1–39 (2012)

    Article  Google Scholar 

  9. Liu, S.Y., Ni, L.M., Krishnan, R.: Fraud detection from taxis’ driving behaviors. IEEE Trans. Veh. Technol. 63, 464–472 (2014)

    Article  Google Scholar 

  10. Moshtaghi, M., Havens, T.C., Bezdek, J.C., Park, L., Leckie, C., Rajasegarar, S., Keller, J.M., Palaniswami, M.: Clustering ellipses for anomaly detection. Pattern Recogn. 44, 55–69 (2011)

    Article  MATH  Google Scholar 

  11. Noto, K., Brodley, C., Slonim, D.: FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection. Data Min. Knowl. Discov. 25, 109–133 (2012)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  13. Zhang, L., Hu, Z.M., Yang, G.: Trajectory outlier detection based on multi-factors. IEICE Trans. Inf. Syst. E97D, 2170–2173 (2014)

    Google Scholar 

  14. Zhang, Y., Meratnia, N., Havinga, P.: Outlier detection techniques for wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 12, 159–170 (2010)

    Article  Google Scholar 

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Acknowledgments

This work is supported by the Open Project of Top Key Discipline of Computer Software and Theory in Zhejiang Provincial (ZC323014100, ZC323016038), the Doctoral Research Start-up Fund of Zhejiang Normal University(ZC304016020), Project of Science and Technology Department of Zhejiang Province (2015C33085), besides, we also thank the anonymous reviewers for their constructive suggestions to improve the quality of the paper.

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Authors and Affiliations

  1. College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China

    Zhiguo Ding

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  1. Zhiguo Ding
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Correspondence to Zhiguo Ding .

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Editors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, China

    Dong Yue

  2. Shanghai University , Shanghai, China

    Chen Peng

  3. Shanghai University , Shanghai, China

    Dajun Du

  4. Nanjing University of Posts and Telecommunications, Nanjing, China

    Tengfei Zhang

  5. Shanghai University , Shanghai, China

    Min Zheng

  6. Swinburne University of Technology, Melbourne, Victoria, Australia

    Qinglong Han

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© 2017 Springer Nature Singapore Pte Ltd.

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Ding, Z. (2017). Taxi Driving Anomalous Route Detection Using GPS Sampling Data. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_31

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  • DOI: https://doi.org/10.1007/978-981-10-6373-2_31

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6372-5

  • Online ISBN: 978-981-10-6373-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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