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Compression of trajectory data: a comprehensive evaluation and new approach

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Abstract

GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts of spatial and temporal information that traces a moving object’s path. The exponential increase in the amount of such trajectory data has caused three major problems. First, transmission of large amounts of data is expensive and time-consuming. Second, queries on large amounts of trajectory data require computationally expensive operations to extract useful patterns and information. Third, GPS trajectories often contain large amounts of redundant data that waste storage and cause increased disk I/O time. These issues can be addressed by algorithms that reduce the size of trajectory data. A key requirement for these algorithms is to minimize the loss of information essential to location-based applications. This paper presents a new compression method called SQUISH-E (Spatial QUalIty Simplification Heuristic - Extended) that provides improved run-time performance and usability. A comprehensive comparison of SQUISH-E with other algorithms is carried out through an empirical study across three types of real-world datasets and a variety of error metrics.

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References

  1. Canalys (2007) Worldwide mobile navigation device market more than doubles. Technical report, Canalys Research Release

  2. Canalys (2009) North America overtakes EMEA as largest satellite navigation market. Technical report, Canalys Research Release

  3. Meratnia N, de By RA (2004) Spatiotemporal compression techniques for moving point objects. In: Proceedings of the 9th international conference on extending database technology (EDBT), pp 765–782

  4. Abdelguerfi M, Givaudan J, Shaw K, Ladner R (2002) The 2-3TR-tree, a trajectory-oriented index structure for fully envolving valid-time spatio-temporal datasets. In: Proceedings of the 10th SIGSPATIAL international conference on advances in geographic information systems (ACM-GIS), pp 29–34

  5. Agarwal PK, Guibas LJ, Edelsbrunner H, Erickson J, Isard M, Har-Peled S, Hershberger J, Jensen C, Kavraki L (2002) Algorithmic issues in modeling motion. ACM Comput Surv 34:550–572

    Article  Google Scholar 

  6. Zhu H, Su J, Ibarra OH (2002) Trajectory queries and octagons in moving object databases. In: Proceedings of the 11th conference on information and knowledge management (CIKM), pp 413–421

  7. Prior-Jones M (2008) Satellite communications systems buyer’s guide. British Antarctic Survey

  8. Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: Proceedings of the 13th international conference on knowledge discovery and data mining (ACM-KDD), pp 330–339

  9. Muckell J, Hwang J-H, Lawson CT, Ravi SS (2010) Algorithms for compressing GPS trajectory data: an empirical evaluation. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems (ACM-GIS), pp 402–405

  10. Muckell J, Hwang J-H, Patil V, Lawson CT, Ping F, Ravi SS (2011) SQUISH: an online approach for GPS trajectory compression. In: Proceedings of the 2nd international conference on computing for geospatial research and applications (COM.Geo), pp 13.1–13.8

  11. Potamias M, Patroumpas K, Sellis T (2006) Sampling trajectory streams with spatio-temporal criteria. In: Proceedings of the 18th international conference on scientific and statistical database management (SSDBM), pp 275–284

  12. Harper JG (1991) Traffic violation detection and deterrence: implications for automatic policing. Appl Ergon 22(3):189–197

    Google Scholar 

  13. Karpinski M, Senart A, Cahill V (2006) Sensor networks for smart roads. In: Proceedings of the 4th IEEE conference on pervasive computing and communications workshops (PerCom 2006 Workshops), pp 306–310

  14. Douglas DH, Peucker TK (1973) Algorithms for the reduction of the number of points required to represent a line or its caricature. Can Cartogr 10(2):112–122

    Article  Google Scholar 

  15. Hershberger J, Snoeyink J (1992) Speeding up the Douglas-Peucker line simplification algorithm. In: Proceedings of the 5th international symposium on spatial data handling (SDH), pp 134–143

  16. Keogh EJ, Chu S, Hart D, Pazzani MJ (2001) An online algorithm for segmenting time series. In: Proceedings of the 2001 IEEE international conference on data mining (ICDM), pp 289–296

  17. Trajcevski G, Cao H, Scheuermann P, Wolfson O, Vaccaro D (2006) On-line data reduction and the quality of history in moving objects databases. In: Proceedings of the 5th ACM international workshop on data engineering for wireless and mobile access (MobiDE), pp 19–26

  18. Bellman RE (1961) On the approximation of curves by line segments using dynamic programming. Commun ACM (CACM) 4(6):284

    Article  Google Scholar 

  19. Muckell J, Hwang JH, Lawson CT, Ravi SS (2010) Algorithms for compressing GPS trajectory data: an empirical evaluation. Technical Report SUNYA-CS-10-06, CS Department, University at Albany – SUNY

  20. Feldman D, Sugaya A, Rus D (2012) An effective coreset compression algorithm for large scale sensor networks. In: Proceedings of the 11th international conference on information processing in sensor networks (IPSN), pp 257–268

  21. Agarwal P, Har-Peled S, Varadarajan K (2005) Geometric approximation via coresets. Technical report, Computer Science Department, Duke University

  22. Schmid F, Richter K-F, Laube P (2009) Semantic trajectory compression. In: Proceedings of the 11th international symposium on advances in spatial and temporal databases (SSTD), pp 411–416

  23. Lin CY, Chen HC, Chen YY, Lee WC, Chen LJ (2010) Compressing trajectories using inter-frame coding. Technical Report TR-IIS-10-007, Institute of Information Science

  24. Kaul S, Gruteser M, Rai V, Kenney J (2010) On predicting and compressing vehicular GPS traces. In: Proceedings of the 2010 IEEE international conference on communications Workshops (ICC Workshops), pp 1–5

  25. Potamias M, Patroumpas K, Sellis T (2006) Amnesic online synopses for moving objects. In: Proceedings of conference on information and knowledge managment (CIKM), pp 784–785

  26. Potamias M, Patroumpas K, Sellis T (2007) Online amnesic summarization of streaming locations. In: Proceedings of the 10th international symposium on advances in spatial and temporal databases (SSTD), pp 148–166

  27. Zheng Y, Li Q, Chen Y, Xie X, Ma W-Y (2008) Understanding mobility based on GPS data. In: Proceedings of the 10th international conference on ubiquitous computing (UbiComp), pp 312–321

  28. Zheng Y, Zhang L, Xie X, Ma W-Y (2009) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international conference on world wide web (WWW), pp 791–800

  29. Lawson CT, Mallia ME (2010) Understanding commuter patterns and behavior: an analysis to recommend policies aimed at reducing vehcile use. Technical report, The New York State Energy and Research and Development Authority (NYSERDA)

  30. Lawson CT, Chen C, Gong H, Karthikeyan S, Kornhauser A (2009) GPS pilot project: phase four. Technical report, New York Metropolitan Transportation Council

  31. Robusto CC (1957) The Cosine-Haversine formula. Am Math Mon 64(1):38–40

    Google Scholar 

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Acknowledgements

This work is supported in part by the National Science Foundation under CAREER award IIS-1149372 and the Research and Innovative Technology Administration of the U.S. Department of Transportation through the Region 2 – University Transportation Research Centers Program.

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Authors and Affiliations

  1. Department of Informatics, College of Computing and Information, University at Albany – State University of New York, Albany, NY, 12222, USA

    Jonathan Muckell

  2. Department of Computer Science, College of Computing and Information, University at Albany – State University of New York, Albany, NY, 12222, USA

    Paul W. Olsen Jr., Jeong-Hyon Hwang & S. S. Ravi

  3. Department of Geography and Planning and College of Computing and Information, University at Albany – State University of New York, Albany, NY, 12222, USA

    Catherine T. Lawson

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  1. Jonathan Muckell
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  2. Paul W. Olsen Jr.
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  3. Jeong-Hyon Hwang
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  4. Catherine T. Lawson
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  5. S. S. Ravi
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Correspondence to Jonathan Muckell.

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Muckell, J., Olsen, P.W., Hwang, JH. et al. Compression of trajectory data: a comprehensive evaluation and new approach. Geoinformatica 18, 435–460 (2014). https://doi.org/10.1007/s10707-013-0184-0

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  • DOI: https://doi.org/10.1007/s10707-013-0184-0

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