Spatial relevancy algorithm for context-aware systems SRACS in urban traffic networks using dynamic range neighbor query and directed interval algebra

  • Authors:

  • Najmeh Neysani Samany;Mahmoud Reza Delavar;Nicholas Chrisman;Mohammad Reza Malek

  • Affiliations:

  • GIS Division, Dept. of Surveying and Geomatics Eng., College of Eng., University of Tehran, Tehran, Iran, Tel: 00982188400424 and 00989126860309;Centre of Excellence in Geomatic Eng. in Disaster Management, Dept. of Serveying and Geomatic Eng., College of Eng., University of Tehran, Tehran, Iran;Department of Geomatic Science, Laval University, Québec, Canada;Dept. of GIS, Faculty of Geodesy and Geomatic Eng., K.N. Toosi Univ. of Technology, Tehran, Iran

  • Venue:
  • Journal of Ambient Intelligence and Smart Environments
  • Year:
  • 2013

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Abstract

Spatial relevancy is one of the primary types of relevancies that determine whether a context is spatially related to the user or not. This paper specifically addresses the use of spatial relationships for detecting spatially relevant contexts. A key aspect is the consideration of all types of spatial relationships metric, directional and topologic. The proposed approach is restricted to the urban network and assumes that in such an environment, the user relates to contexts via linear spatial intervals. The main contribution of this work is that the proposed model is sensitive to the velocity and direction of the user and applies Directed Interval Algebra DIA and the Dynamic Range Neighbour Query DRNQ to introduce spatially relevant contexts according to their arrangement in space. The Spatial Relevancy Algorithm for Context-aware Systems SRACS helps the tourist to find his/her preferred areas that are spatially relevant. The experimental results in a scenario of tourist navigation are evaluated with respect to the accuracy of the model, performance time and satisfaction of users in 25 iterations of the algorithm on 25 routes in Tehran. The evaluation process demonstrated the efficiency of the model in real-world applications.