Crowd location forecasting at points of interest. (2015)

• Record Type: Journal Article
• Authors: Alvarez–Lozano, Jorge; García–Macías, J. Antonio; Chávez, Edgar
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 18:Number 4(2015)
• Page Start: 191
• Page End: 204
• Publication Date: 2015
• Subjects: data mining -- data sharing -- spatio–temporal crowd locations -- crowd location forecasting -- user location predictability -- user mobility similarity -- points of interest -- mobile users -- mobility patterns -- weekly patterns -- hidden Markov model -- HMM; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

Predicting the location of a mobile user in the near future can be used for a large number of user–centred ubiquitous applications. This can be extended to crowd–centred applications if a large number of users is included. In this paper we present a spatio–temporal prediction approach to forecast user location in a medium–term period. Our approach is based on the hypothesis that users exhibit a different mobility pattern for each day of the week. Once factored out this weekly pattern, user mobility among points of interest is postulated to be markovian. We trained a hidden Markov model to forecast user mobility and evaluated our approach using a public dataset. The experimental results show that our approach is effective considering a time period of up to 7 h. We obtained an accuracy of up to 81.75% for a period of 30 min, and 66.25% considering 7 h.