Similarity of GPS Trajectories Using Dynamic Time Warping: An Application to Cruise Tourism

  • Mauro FerranteEmail author
  • Christian Bongiorno
  • Noam Shoval
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 274)


The aim of this research is to propose an analysis of the trajectories of cruise passengers at their destination using Dynamic Time Warping algorithm. Data collected by means of GPS devices relating to the behavior of cruise passengers in the port of Palermo have been analyzed in order to show similarities and differences among their spatial trajectories at destination. A cluster analysis has been performed in order to identify segments of cruise passengers, based on the similarity of their trajectories. The results have been compared in terms of several metrics derived from GPS tracking data in order to validate the proposed approach. Our findings are of interest from a methodological perspective concerning the analysis of GPS data and the management of cruise tourism destinations.


Cruise tourism Dynamic time warping GPS trajectories 


  1. 1.
    Aach, J., Church, G.M.: Aligning gene expression time series with time warping algorithms. Bioinformatics 17(6), 495–508 (2001)CrossRefGoogle Scholar
  2. 2.
    Andrienko, G., Andrienko, N., Rinzivillo, S., Nanni, M., Pedreschi, D., Giannotti, F.: Interactive visual clustering of large collections of trajectories. In: IEEE Symposium on Visual Analytics Science and Technology, 2009. VAST 2009. IEEE (2009)Google Scholar
  3. 3.
    Andriotis, K., Agiomirgianakis, G.: Cruise visitors experience in a Mediterranean port of call. Int. J. Tour. Res. 12(4), 390–404 (2010)CrossRefGoogle Scholar
  4. 4.
    Bauder, M.: Using GPS supported speed analysis to determine spatial visitor behaviour. Int. J. Tour. Res. 17(4), 337–346 (2015)CrossRefGoogle Scholar
  5. 5.
    Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. KDD Work. 10(16), 359–370 (1994)Google Scholar
  6. 6.
    Bonanno, G., Lillo, F., Mantegna, R.N.: Levels of complexity in financial markets. Phys. A Stat. Mech. Appl. 299(1), 16–27 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Brida, J.G., Fasone, V., Scuderi, R., Zapata-Aguirre, S.: Exploring the determinants of cruise passengers expenditure at ports of call in Uruguay. Tour. Econ. 20(5), 1133–1143 (2014)CrossRefGoogle Scholar
  8. 8.
    Cessford, G.R., Dingwall, P.R.: Tourism on New Zealands Sub-antarctic islands. Ann. Tour. Res. 21(2), 318–332 (1994)CrossRefGoogle Scholar
  9. 9.
    De Cantis, S., Ferrante, M., Kahani, A., Shoval, N.: Cruise passengers’ behavior at the destination: investigation using GPS technology. Tour. Manag. 52, 133–150 (2016)CrossRefGoogle Scholar
  10. 10.
    Defays, D.: An efficient algorithm for a complete link method. Comput. J. 20(4), 364–366 (1977)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Edwards, D., Griffin, T., Hayllar, B., Dickson, T.: Making Tracks and Collecting Images: New Methods for Examining Tourists’ Spatial Behaviour in Cities. In: Council for Australian University Tourism and Hospitality Education (Hrsg.), CAUTHE 2009, See Change: Tourism & Hospitality in a Dynamic World. Perth, pp. 2023–2026 (2009)Google Scholar
  12. 12.
    Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. 95(25), 14863–14868 (1998)CrossRefGoogle Scholar
  13. 13.
    Ferrante, M., De Cantis, S., Shoval, N.: A general framework for collecting and analyzing the tracking data of cruise passengers at the destination. Curr. Issues Tour. 1–26 (2016)Google Scholar
  14. 14.
    Giorgino, T.: DTW: Dynamic Time Warping algorithms. R package version 1.17.1 (2013)Google Scholar
  15. 15.
    Gong, H., Chen, C., Bialostozky, E., Lawson, C.T.: A GPS/GIS method for travel mode detection in New York City. Spec. Issue: Geoinformatics 2010 36(2), 131–139 (2012)Google Scholar
  16. 16.
    Gower, J.C., Ross, G.J.S.: Minimum spanning trees and single linkage cluster analysis. J. R. Stat. Soc. Ser. C 18(1), 54–64 (1969)MathSciNetGoogle Scholar
  17. 17.
    Guyer, C., Pollard, J.: Cruise visitor impressions of the environment of the Shannon-Erne waterways system. J. Environ. Manag. 51(2), 199–215 (1997)CrossRefGoogle Scholar
  18. 18.
    Hallo, J.C., Manning, R.E., Valliere, W., Budruk, M.: A case study comparison of visitor self-reported and GPS recorded travel routes. In: Proceedings of the 2004 Northeastern Recreation Research Symposium, GTR-NE-326, Newton Square, PA: Forest Service, pp. 172–177 (2004)Google Scholar
  19. 19.
    Jaakson, R.: Beyond the tourist bubble? cruiseship passengers in port. Ann. Tour. Res. 31(1), 44–60 (2004)CrossRefGoogle Scholar
  20. 20.
    Johnson, D., Trivedi, M.M.: Driving style recognition using a smartphone as a sensor platform. In: Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1609–1615 (2011)Google Scholar
  21. 21.
    Johnson, S.C.: Hierarchical clustering schemes. Psychometrika 2, 241–254 (1967)zbMATHCrossRefGoogle Scholar
  22. 22.
    Kovács-Vajna, Z.M.: A fingerprint verification system based on triangular matching and dynamic time warping. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1266–1276 (2000)CrossRefGoogle Scholar
  23. 23.
    McKercher, B., Zoltan, J.: Tourists flows and spatial behavior. In: Lew, A.A., Hall, M.C., Williams, A.M. (eds.) The Wiley Blackwell Companion to Tourism, pp. 33–44. Wiley, Malden (2014)CrossRefGoogle Scholar
  24. 24.
    Mori, A., Uchida, S., Kurazume, R., Taniguchi, R., Hasegawa, T., Sakoe, H.: Early recognition and prediction of gestures. In: Proceeding of the 18th International Conference on Pattern Recognition 2006, vol. 3, pp. 560–563 (2006)Google Scholar
  25. 25.
    Munich, M.E., Perona, P.: Continuous dynamic time warping for translation-invariant curve alignment with applications to signature verification. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999. IEEE vol. 1, pp. 108–115 (1999)Google Scholar
  26. 26.
    Myers, C., Rabiner, L.R., Rosenberg, A.E.: Performance tradeoffs in dynamic time warping algorithms for isolated word recognition. IEEE Trans. Acoust. Speech Signal Process. 28(6), 623–635 (1980)zbMATHCrossRefGoogle Scholar
  27. 27.
    Puczkó, L., Bárd, E., Füzi, J.: Methodological triangulation: the study of visitor behaviour at the Hungarian open air museum. In: Richards, G., Munsters, W. (eds.) Cultural Tourism Research Methods, pp. 61–74. CABI, Wallingford (2010)CrossRefGoogle Scholar
  28. 28.
    Rabiner, L.R., Juang, B.-H.: Fundamentals of Speech Recognition. Tsinghua University Press, Beijing (1999)Google Scholar
  29. 29.
    Rhee, I., Shin, M., Hong, S., Lee, K., Kim, S.J., Chong, S.: On the levy-walk nature of human mobility. IEEE/ACM Trans. Netw. (TON) 19(3), 630–643 (2011)CrossRefGoogle Scholar
  30. 30.
    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)zbMATHCrossRefGoogle Scholar
  31. 31.
    Shoval, N.: Tracking technologies and urban analysis. Cities 25(1), 21–28 (2008)CrossRefGoogle Scholar
  32. 32.
    Shoval, N., Isaacson, M.: Tracking tourists in the digital age. Ann. Tour. Res. 34(1), 141–159 (2007)CrossRefGoogle Scholar
  33. 33.
    Sokal, R., Michener, C.: A statistical method for evaluating systematic relationships. Univ. Kans. Sci. Bull. 38, 1409–1438 (1958)Google Scholar
  34. 34.
    Tsui, S.Y.A., Shalaby, A.: An enhanced system for link and mode identification for GPS-based personal travel survey. Transp. Res. Rec. 1972, 38–45 (2006)CrossRefGoogle Scholar
  35. 35.
    Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: Proceedings of the 18th International Conference on Data Engineering. IEEE, pp. 673–684 (2002)Google Scholar
  36. 36.
    Wang, H., Su, H., Zheng, K., Sadiq, S., Zhou, X.: An effectiveness study on trajectory similarity measures. In: Proceedings of the Twenty-Fourth Australasian Database Conference, vol. 137, pp. 13–22 (2013)Google Scholar
  37. 37.
    Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mauro Ferrante
    • 1
    Email author
  • Christian Bongiorno
    • 2
  • Noam Shoval
    • 3
    • 4
  1. 1.Dipartimento Culture e SocietàUniversità degli Studi di PalermoPalermoItaly
  2. 2.Dipartimento Fisica e ChimicaUniversità degli Studi di PalermoPalermoItaly
  3. 3.The Department of GeographyThe Hebrew University of JerusalemJerusalemIsrael
  4. 4.The University Center for Urban and Social Research, The University of PittsburghPittsburghUSA

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