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An Online GPS Trajectory Data Compression Method Based on Motion State Change

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11061))

Abstract

Aiming to the problem of insufficient consideration to the cumulative error and offset which online Global Positioning System (GPS) trajectory data compression based on motion state change and the key point insufficient evaluation of online GPS trajectory data compression based on the offset calculation, an online compression of GPS trajectory data based on motion state change named Synchronous Euclidean Distance (SED) Limited Thresholds Algorithm (SLTA) was proposed. This algorithm used steering angle value and speed change value to evaluate information of trajectory point. At the same time, SLTA introduced the SED to limit offset of trajectory point. So SLTA could reach better information retention. The experiment results show that the trajectory compression ratio can reach about 50%. Compared with Thresholds Algorithm (TA), the average SED error of SLTA can be negligible. For other trajectory data compression algorithms, SLTA’s average angel error is minimum. SLTA can effectively do online GPS trajectory data compression.

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References

  1. Chen, M., Xu, M., Franti, P.: Compression of GPS trajectories. In: Proceedings of the 2012 Data Compression Conference (DCC), pp. 62–71. IEEE, Piscataway (2012)

    Google Scholar 

  2. Chen, M., Xu, M., Franti, P.: Compression of GPS trajectories using optimized approximation. In: Proceedings of the 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3180–3183. IEEE, Piscataway (2012)

    Google Scholar 

  3. Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: Int. J. Geograph. Inf. Geovis. 10(2), 112–122 (1973)

    Article  Google Scholar 

  4. Hershberger, J., Snoeyink, J.: Speeding up the Douglas-Peucker Line-Simplification Algorithm, pp. 134–143. University of British Columbia, Department of Computer Science (1992)

    Google Scholar 

  5. Hershberger, J., Snoeyink, J.: An O(nlogn) implementation of the Douglas-Peucker algorithm for line simplification. In: Proceedings of the Tenth Annual Symposium on Computational Geometry, pp. 383–384. ACM, New York (1994)

    Google Scholar 

  6. Agarwal, P.K., Har-Peled, S., Mustafa, N.H., et al.: Near-linear time approximation algorithms for curve simplification. Algorithmica 42(3–4), 203–219 (2005)

    Article  MathSciNet  Google Scholar 

  7. Ma, J., Xu, S., Pu, Y., et al.: A real-time parallel implementation of Douglas-Peucker polyline simplification algorithm on shared memory multi-core processor computers. In: Proceedings of the 2010 International Conference on Computer Application and System Modeling (ICCASM), pp. V4-647–V4-652. IEEE, Piscataway (2010)

    Google Scholar 

  8. Keogh, E., Chu, S., Hart, D., et al.: An online algorithm for segmenting time series. In: ICDM 2001: Proceedings IEEE International Conference on Data Mining, pp. 289–296. IEEE, Piscataway (2001)

    Google Scholar 

  9. Muckell, J., Hwang, J.H., Patil, V., et al.: SQUISH: an online approach for GPS trajectory compression. In: Proceedings of the 2nd International Conference on Computing for Geospatial Research and Applications, p. 13. ACM, New York (2011)

    Google Scholar 

  10. Muckell, J., Olsen Jr., P.W., Hwang, J.H., et al.: Compression of trajectory data: a comprehensive evaluation and new approach. GeoInformatica 18(3), 435–460 (2014)

    Article  Google Scholar 

  11. Meratnia, N., de By, R.A.: Spatiotemporal compression techniques for moving point objects. In: Bertino, E., et al. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 765–782. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24741-8_44

    Chapter  Google Scholar 

  12. Microsoft Research. Geolife GPS Trajectories data sample. [EB/OL], 09 Aug 2012. http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/

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Correspondence to Shuang Liu .

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Wang, H., Liu, S., Qian, C. (2018). An Online GPS Trajectory Data Compression Method Based on Motion State Change. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_22

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  • DOI: https://doi.org/10.1007/978-3-319-99365-2_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99364-5

  • Online ISBN: 978-3-319-99365-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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