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

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Theoretical and Applied Statistics (SIS 2015)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 274))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Dipartimento Culture e Società, Università degli Studi di Palermo, Palermo, Italy

    Mauro Ferrante

  2. Dipartimento Fisica e Chimica, Università degli Studi di Palermo, Palermo, Italy

    Christian Bongiorno

  3. The Department of Geography, The Hebrew University of Jerusalem, Jerusalem, Israel

    Noam Shoval

  4. The University Center for Urban and Social Research, The University of Pittsburgh, Pittsburgh, PA, USA

    Noam Shoval

Authors
  1. Mauro Ferrante
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  2. Christian Bongiorno
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  3. Noam Shoval
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Corresponding author

Correspondence to Mauro Ferrante .

Editor information

Editors and Affiliations

  1. Department of Economics, University of Foggia, Foggia, Italy

    Corrado Crocetta

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Ferrante, M., Bongiorno, C., Shoval, N. (2019). Similarity of GPS Trajectories Using Dynamic Time Warping: An Application to Cruise Tourism. In: Crocetta, C. (eds) Theoretical and Applied Statistics. SIS 2015. Springer Proceedings in Mathematics & Statistics, vol 274. Springer, Cham. https://doi.org/10.1007/978-3-030-05420-5_10

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