By Topic

Predicting Mobile Phone User Locations by Exploiting Collective Behavioral Patterns

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Haoyi Xiong ; Inst. Mines-Telecom, Telecom SudParis, Evry, France ; Daqing Zhang ; Daqiang Zhang ; Gauthier, V.

Location prediction based on cellular network traces has recently spurred lots of interest. However, predicting one's location remains a very challenging task due to the randomness of the human mobility patterns. Our preliminary study included in this paper shows that there is a strong correlation and association among the certain group of users' locations. Through association pattern mining on Reality Mining dataset which involves 32,579 cell tower locations and 350,000 hours of continuous activity information, we observe the highly confident association rules exist among the locations of users, and then we further verify that the associations are indeed caused by the collective behaviors of the mobile phone users. Based on this finding we introduce the collective behavioral patterns (CBP), and then propose CBP-based predictor- a novel prediction schema that aims to forecasting one's locations in next 6 hours based on the locations of other users. Furthermore, we integrate the state-of-the-art i.e., Markov-based predictor with our CBP-based schema to build a hybrid predictor. We evaluate the CBP-based schema and compare the hybrid predictor with the Markov-based predictor through intensive experiments. Experimental results show that CBP-based predictor achieves good precision and the hybrid predictor produces higher prediction accuracy than the state-of-the-art scheme at cell tower level in the forthcoming one to six hours. Finally it is verified that collective behavioral patterns can be used to predict user locations as well as to improve the performance of existing predictors.

Published in:

Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing (UIC/ATC), 2012 9th International Conference on

Date of Conference:

4-7 Sept. 2012