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

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.

[1]  Zsolt Miklós Kovács-Vajna,et al.  A Fingerprint Verification System Based on Triangular Matching and Dynamic Time Warping , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Amer Shalaby,et al.  Enhanced System for Link and Mode Identification for Personal Travel Surveys Based on Global Positioning Systems , 2006 .

[3]  Aaron E. Rosenberg,et al.  Performance tradeoffs in dynamic time warping algorithms for isolated word recognition , 1980 .

[4]  Reiner Jaakson,et al.  Beyond the tourist bubble? Cruiseship passengers in port. , 2004 .

[5]  Juan Gabriel Brida,et al.  Research Note: Exploring the Determinants of Cruise Passengers' Expenditure at Ports of Call in Uruguay , 2014 .

[6]  Robert E. Manning,et al.  A CASE STUDY COMPARISON OF VISITOR SELF-REPORTED AND GPS RECORDED TRAVEL ROUTES , 2004 .

[7]  G. R. Cessford,et al.  Tourism on New Zealand's Sub-Antarctic Islands , 1994 .

[8]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[9]  Dino Pedreschi,et al.  Interactive visual clustering of large collections of trajectories , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[10]  L. Puczkó,et al.  Methodological triangulation: the study of visitor behaviour at the Hungarian open air museum. , 2010 .

[11]  Stefano De Cantis,et al.  Cruise passengers' behavior at the destination: Investigation using GPS technology , 2016 .

[12]  John Pollard,et al.  Cruise Visitor Impressions of the Environment of the Shannon–Erne Waterways System , 1997 .

[13]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[14]  Mohan M. Trivedi,et al.  Driving style recognition using a smartphone as a sensor platform , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[15]  Injong Rhee,et al.  On the levy-walk nature of human mobility , 2011, TNET.

[16]  Bob McKercher,et al.  Tourist Flows and Spatial Behavior , 2014 .

[17]  Ryo Kurazume,et al.  Early Recognition and Prediction of Gestures , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[18]  Catherine T. Lawson,et al.  A GPS/GIS method for travel mode detection in New York City , 2012, Comput. Environ. Urban Syst..

[19]  Shazia Wasim Sadiq,et al.  An Effectiveness Study on Trajectory Similarity Measures , 2013, ADC.

[20]  Noam Shoval,et al.  Tracking tourists in the digital age , 2007 .

[21]  S. C. Johnson Hierarchical clustering schemes , 1967, Psychometrika.

[22]  Pietro Perona,et al.  Continuous dynamic time warping for translation-invariant curve alignment with applications to signature verification , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[23]  Fabrizio Lillo,et al.  Levels of complexity in financial markets , 2001 .

[24]  Robert R. Sokal,et al.  A statistical method for evaluating systematic relationships , 1958 .

[25]  Toni Giorgino,et al.  Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package , 2009 .

[26]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[27]  Michael Bauder,et al.  Using GPS supported speed analysis to determine spatial visitor behaviour. , 2015 .

[28]  Noam Shoval,et al.  Tracking technologies and urban analysis , 2008 .

[29]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[30]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[31]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[32]  J. Gower,et al.  Minimum Spanning Trees and Single Linkage Cluster Analysis , 1969 .

[33]  George M. Church,et al.  Aligning gene expression time series with time warping algorithms , 2001, Bioinform..

[34]  D. Defays,et al.  An Efficient Algorithm for a Complete Link Method , 1977, Comput. J..

[35]  George Agiomirgianakis,et al.  Cruise visitors' experience in a Mediterranean port of call , 2010 .