The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 417–421, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-417-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 417–421, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-417-2017

  27 Sep 2017

27 Sep 2017

A NEW MODEL FOR FUZZY PERSONALIZED ROUTE PLANNING USING FUZZY LINGUISTIC PREFERENCE RELATION

S. Nadi1 and A. H. Houshyaripour2 S. Nadi and A. H. Houshyaripour
  • 1Department of Geomatics Engineering, Faculty of Civil and Transportation Engineering, University of Isfahan, Isfahan, Iran
  • 2Remote Sensing, Department of Geomatics Engineering, Faculty of Civil and Transportation Engineering, University of Isfahan, Iran

Keywords: Fuzzy Route Planning, Personalized, Uncertainty, Fuzzy AHP, Fuzzy Comparison

Abstract. This paper proposes a new model for personalized route planning under uncertain condition. Personalized routing, involves different sources of uncertainty. These uncertainties can be raised from user’s ambiguity about their preferences, imprecise criteria values and modelling process. The proposed model uses Fuzzy Linguistic Preference Relation Analytical Hierarchical Process (FLPRAHP) to analyse user’s preferences under uncertainty. Routing is a multi-criteria task especially in transportation networks, where the users wish to optimize their routes based on different criteria. However, due to the lake of knowledge about the preferences of different users and uncertainties available in the criteria values, we propose a new personalized fuzzy routing method based on the fuzzy ranking using center of gravity. The model employed FLPRAHP method to aggregate uncertain criteria values regarding uncertain user’s preferences while improve consistency with least possible comparisons. An illustrative example presents the effectiveness and capability of the proposed model to calculate best personalize route under fuzziness and uncertainty.