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A Novel Measure for Trajectory Similarity

  • Sijie Luo
  • Fumin ZouEmail author
  • Qiqin Cai
  • Feng Guo
  • Weihui Xu
  • Yong Li
Conference paper
  • 30 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1107)

Abstract

With the rapid growth of motor vehicles and the popularity of the Global Positioning System, massive traffic trajectory data is accumulated. Traffic trajectory data contains a large amount of structured knowledge such as time, space and the relationship between various traffic elements. Comparing the trajectory of the owner’s travel, mining the travel mode of the owner has become a hot topic in academic research. At present, commonly used methods for measuring similarity include dynamic time warping (DTW), Euclidean distance (ED), longest common subsequence (LCSS), and edit distance (EDR). DTW is currently recognized as the most anti-jamming and anti-deformation similarity evaluation method, but its disadvantages that high time and space complexity are also obvious. This paper proposes a new trajectory similarity measure, without using the DTW algorithm, we also can solve the problem of inaccurate matching by noise. The matching accuracy of this method is similar to or equal to DTW, which can be applied to traffic trajectory similarity evaluation, the matching speed is about 2–5 times faster than DTW.

Keywords

Traffic trajectory data Travel mode DTW Trajectory similarity measuring 

References

  1. 1.
    Zou, F., Llao, L., Jiang, X., Lai, H.: An automatic recognition approach for traffic congestion states based on traffic video. J. Highway Transp. Res. Dev. (Engl. Edn.) 8(2), 72–80 (2014)CrossRefGoogle Scholar
  2. 2.
    Li, Y., Su, H., Demiryurek, U., et al.: PaRE: a system for personalized route guidance. In: Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp. 637–646 (2017)Google Scholar
  3. 3.
    Cai, Q., Liao, L., Zou, F., Song, S., Liu, J., Zhang, M.: Trajectory similarity measuring with grid-based DTW. In: The 2nd International Conference on Smart Vehicular Technology, Transportation, Communication and Application, pp. 63–72. Springer (2019)Google Scholar
  4. 4.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, pp. 60–65. Pearson Addison-Wesley, Boston (2006)Google Scholar
  5. 5.
    Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. Found. Data Organ. Algorithms 730, 69–84 (1993)CrossRefGoogle Scholar
  6. 6.
    Atev, S., Miller, G., Papanikolopoulos, N.P.: Clustering of vehicle trajectories. IEEE Trans. Intell. Transp. Syst. 11(3), 647–657 (2010)CrossRefGoogle Scholar
  7. 7.
    Agrawal, R., Faloutsos, C., Swami, A.N.: Efficient similarity search in sequence databases. In: FODO, pp. 69–84 (1993)Google Scholar
  8. 8.
    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, pp. 1325–1330. ACM (2009)Google Scholar
  9. 9.
    Chen, L., Ozsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: SIGMOD (2005)Google Scholar
  10. 10.
    Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust. Speech Signal Process. 23(1), 67–72 (1975)CrossRefGoogle Scholar
  11. 11.
    Silva, D.F., Giusti, R.: Speeding up similarity search under dynamic time warping by pruning unpromising alignments. Data Min. Knowl. Disc. 32, 988–1016 (2018)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Kim, S., Park, S., Chu, W.: An index-based approach for similarity search supporting time warping in large sequence databases. In: ICDE, pp. 607–61 (2001)Google Scholar
  13. 13.
    Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3), 358–386 (2005)CrossRefGoogle Scholar
  14. 14.
    Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., Keogh, E.: Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 262–270 (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Sijie Luo
    • 1
    • 2
  • Fumin Zou
    • 1
    • 2
    Email author
  • Qiqin Cai
    • 1
    • 2
  • Feng Guo
    • 1
    • 2
  • Weihui Xu
    • 1
    • 2
  • Yong Li
    • 3
  1. 1.Fujian Key Lab for Automotive Electronics and Electric DriveFujian University of TechnologyFuzhouChina
  2. 2.Fujian Provincial Big Data Research Institute of Intelligent TransportationFujian University of TechnologyFuzhouChina
  3. 3.Fujian Fortunetone Network Technology Co., Ltd.FuzhouChina

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