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Transportation Mode Annotation of Tourist GPS Trajectories Under Environmental Constraints

  • Hidekazu KasaharaEmail author
  • Mikihiko Mori
  • Masayuki Mukunoki
  • Michihiko Minoh
Conference paper

Abstract

Tourist transportation usage analysis provides basic information for tourism policy making. With the technical advances of tracking devices, GPS-equipped smartphones sense the movement of tourists and generate extensive volumes of movement data detailing tourist trajectories. Many researchers study semantic annotation using machine learning. However, it is necessary for machine learning to label the data for training; this requirement is costly. It would be useful for GPS semantic annotation if labelling the substantial amounts of GPS data could be avoided. In this research, we propose a new, simple GPS semantic annotation method using environmental constraints without machine learning. We call this method Segment Expansion with Environmental Constraints (SEEC) and assume a tourist behaviour model in which tourists move by foot and public transportation in touristic destinations that include numerous locations of interest. SEEC inferred the transportation modes of the GPS trajectory data at a 90.4 % accuracy level in the experiment.

Keywords

GPS Semantic annotation Big data analytics Environmental constraint 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hidekazu Kasahara
    • 1
    Email author
  • Mikihiko Mori
    • 1
  • Masayuki Mukunoki
    • 1
  • Michihiko Minoh
    • 1
  1. 1.Academic Center for Computing and Media StudiesKyoto UniversityKyotoJapan

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