The full text of this article

 

Crowd location forecasting at points of interest
by Jorge Alvarez-Lozano; J. Antonio García-Macías; Edgar Chávez
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 18, No. 4, 2015

 

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.

Online publication date: Sun, 26-Apr-2015

 

is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

 
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

 
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC):
Login with your Inderscience username and password:

 

    Username:        Password:         

Forgotten your password?


 
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

 
If you still need assistance, please email subs@inderscience.com