Predicting Mobile Phone User Locations by Exploiting Collective Behavioral Patterns

Abstract

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.

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