Contextual location prediction using spatio-temporal clustering

Abstract

Purpose

The prediction of a context, especially of a user’s location, is a fundamental task in the field of pervasive computing. Such predictions open up a new and rich field of proactive adaptation for context-aware applications. This study/paper aims to propose a methodology that predicts a user’s location on the basis of a user’s mobility history.

Design/methodology/approach

Contextual information is used to find the points of interest that a user visits frequently and to determine the sequence of these visits with the aid of spatial clustering, temporal segmentation and speed filtering.

Findings

The proposed method was tested with a real data set using several supervised classification algorithms, which yielded very interesting results.

Originality/value

The method uses contextual information (current position, day of the week, time and speed) that can be acquired easily and accurately with the help of common sensors such as GPS.

Citation

Guessoum, D., Miraoui, M. and Tadj, C. (2016), "Contextual location prediction using spatio-temporal clustering", International Journal of Pervasive Computing and Communications, Vol. 12 No. 3, pp. 290-309. https://doi.org/10.1108/IJPCC-05-2016-0027