Computing Semantic Trajectories: Methods and Used Techniques

  • Thouraya SakouhiEmail author
  • Jalel Akaichi
  • Usman Ahmed
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 76)


The widespread use of mobile devices generates huge amount of location data. The generated data is useful for many applications, including location-based services such as outdoor sports forums, routine prediction, location-based activity recognition and location-based social networking. Sharing individuals’ trajectories and annotating them with activities, for example a tourist transportation mode during his trip, helps bringing more semantics to the GPS data. Indeed, this provides a better understanding of the user trajectories, and then more interesting location-based services. To address this issue, diverse range of novel techniques in the literature are explored to enrich this data with semantic information, notably, machine learning and statistical algorithms. In this work, we focused, at a first level, on exploring and classifying the literature works related to semantic trajectory computation. Secondly, we capitalized and discussed the benefits and limitations of each approach.


Mobility data Trajectory Semantic modeling Ontology Machine learning Data mining Activity recognition 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Institut Supérieur de GestionUniversité de TunisTunisTunisia
  2. 2.College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia

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