Where will you go? Mobile data mining for next place prediction

Abstract

The technological advances in smartphones and their widespread use has resulted in the big volume and varied types of mobile data which we have today. Location prediction through mobile data mining leverages such big data in applications such as traffic planning, location-based advertising, intelligent resource allocation; as well as in recommender services including the popular Apple Siri or Google Now. This paper, focuses on the challenging problem of predicting the next location of a mobile user given data on his or her current location. In this work, we propose NextLocation - a personalised mobile data mining framework - that not only uses spatial and temporal data but also other contextual data such as accelerometer, bluetooth and call/sms log. In addition, the proposed framework represents a new paradigm for privacy-preserving next place prediction as the mobile phone data is not shared without user permission. Experiments have been performed using data from the Nokia Mobile Data Challenge (MDC). The results on MDC data show large variability in predictive accuracy of about 17% across users. For example, irregular users are very difficult to predict while for more regular users it is possible to achieve more than 80% accuracy. To the best of our knowledge, our approach achieves the highest predictive accuracy when compared with existing results. © 2013 Springer-Verlag GmbH.