Low Complexity Spatial Similarity Measure of GPS Trajectories

Radu Mariescu-Istodor, Andrei Tabarcea, Rahim Saeidi, Pasi Fränti


We attack the problem of trajectory similarity by approximating the trajectories using a geographical grid based on the MGRS 2D coordinate system. We propose a spatial similarity measure which is computationally feasible for big data collections. The proposed measure is based on cell matching with a similarity metric drawn from Jaccard index. We equip the proposed method with interpolation and dilation to overcome the problems missing data and different sampling frequencies when comparing two trajectories. The proposed measure is implemented online in the framework of Mopsia.


  1. Agrawal, R., Faloutsos, C., and Swami, A. (1993). Efficient similarity search in sequence databases. Springer.
  2. Berndt, D. J. and Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In KDD workshop, volume 10, pages 359-370. Seattle, WA.
  3. Chan, K.-P. and Fu, A. W.-C. (1999). Efficient time series matching by wavelets. In Data Engineering, 1999. Proceedings., 15th International Conference on, pages 126-133. IEEE.
  4. Chen, L., O zsu, M. T., and Oria, V. (2005). Robust and fast similarity search for moving object trajectories. In Proceedings of the 2005 ACM SIGMOD international conference on Management of data, pages 491-502. ACM.
  5. Chen, M., Xu, M., and Fränti, P. (2012a). Compression of gps trajectories. In Data Compression Conference (DCC), 2012, pages 62-71. IEEE.
  6. Chen, M., Xu, M., and Fränti, P. (2012b). A fast O(N) multiresolution polygonal approximation algorithm for GPS trajectory simplification. IEEE Transactions on Image Processing, pages 2770-2785.
  7. Fränti, P., Chen, J., and Tabarcea, A. (2011). Four aspects of relevance in location-based media: content, time, location and network. In Web Information Systems and Technologies (WEBIST'11), International Conference on, pages 413-417.
  8. Fränti, P., Tabarcea, A., Kuittinen, J., and Hautamäki, V. (2010). Location-based search engine for multimedia phones. In Multimedia and Expo (ICME), 2010 IEEE International Conference on, pages 558-563. IEEE.
  9. Frentzos, E., Gratsias, K., Pelekis, N., and Theodoridis, Y. (2007a). Algorithms for nearest neighbor search on moving object trajectories. Geoinformatica, 11(2):159-193.
  10. Frentzos, E., Gratsias, K., and Theodoridis, Y. (2007b). Index-based most similar trajectory search. In Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on, pages 816-825. IEEE.
  11. Güting, R. H., Behr, T., and Xu, J. (2010). Efficient knearest neighbor search on moving object trajectories. The VLDB Journal, 19(5):687-714.
  12. Hamilton, J. D. (1994). Time series analysis, volume 2. Cambridge Univ Press.
  13. Hu, N. and Steenkiste, P. (2006). Quantifying internet endto-end route similarity. In Passive and Active Measurement Conference, volume 2006, pages 101-110.
  14. Lange, D. and Naumann, F. (2011). Efficient similarity search: arbitrary similarity measures, arbitrary composition. In Proceedings of the 20th ACM international conference on Information and knowledge management, pages 1679-1688. ACM.
  15. Mariescu-Istodor, R. (2013). Detecting user actions in MOPSI. Master's thesis, University of Eastern Finland.
  16. Ni, J. and Ravishankar, C. V. (2007). Indexing spatiotemporal trajectories with efficient polynomial approximations. Knowledge and Data Engineering, IEEE Transactions on, 19(5):663-678.
  17. Pelekis, N., Kopanakis, I., Kotsifakos, E. E., Frentzos, E., and Theodoridis, Y. (2011). Clustering uncertain trajectories. Knowledge and Information Systems, 28(1):117-147.
  18. Tabarcea, A., Wan, Z., Waga, K., and Fränti, P. (2013). O-mopsi: Mobile orienteering game using geotagged photos. pages 300-303.
  19. Vlachos, M., Gunopulos, D., and Kollios, G. (2002a). Robust similarity measures for mobile object trajectories. In Database and Expert Systems Applications, 2002. Proceedings. 13th International Workshop on, pages 721-726. IEEE.
  20. Vlachos, M., Kollios, G., and Gunopulos, D. (2002b). Discovering similar multidimensional trajectories. In Data Engineering, 2002. Proceedings. 18th International Conference on, pages 673-684. IEEE.
  21. Waga, K., Tabarcea, A., Chen, M., and Fränti, P. (2012). Detecting movement type by route segmentation and classification. In Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), 2012 8th International Conference on, pages 508-513. IEEE.
  22. Waga, K., Tabarcea, A., and Fränti, P. (2011). Context aware recommendation of location-based data. In System Theory, Control, and Computing (ICSTCC), 2011 15th International Conference on, pages 1-6. IEEE.
  23. Waga, K., Tabarcea, A., Mariescu-Istodor, R., and Fränti, P. (2013). Real time access to multiple GPS tracks. pages 293-299.
  24. Worsley, K. J. and Friston, K. J. (1995). Analysis of fMRI time-series revisitedagain. Neuroimage, 2(3):173- 181.
  25. Yanagisawa, Y., Akahani, J.-i., and Satoh, T. (2003). Shapebased similarity query for trajectory of mobile objects. In Mobile data management, pages 63-77. Springer.

Paper Citation

in Harvard Style

Mariescu-Istodor R., Tabarcea A., Saeidi R. and Fränti P. (2014). Low Complexity Spatial Similarity Measure of GPS Trajectories . In Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-023-9, pages 62-69. DOI: 10.5220/0004940500620069

in Bibtex Style

author={Radu Mariescu-Istodor and Andrei Tabarcea and Rahim Saeidi and Pasi Fränti},
title={Low Complexity Spatial Similarity Measure of GPS Trajectories},
booktitle={Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},

in EndNote Style

JO - Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Low Complexity Spatial Similarity Measure of GPS Trajectories
SN - 978-989-758-023-9
AU - Mariescu-Istodor R.
AU - Tabarcea A.
AU - Saeidi R.
AU - Fränti P.
PY - 2014
SP - 62
EP - 69
DO - 10.5220/0004940500620069