MIAC: A mobility intention auto-completion model for location prediction

Abstract

Location prediction is essential to many commercial applications and enables appealing experience for business and governments. Many research work show that human mobility is highly predictable. However, existing work on location prediction reported limited improvements in using generalized spatio-temporal features and unsatisfactory prediction accuracy for complex human mobility. To address these challenges, in this paper we propose a Mobility Intention and Auto-Completion (MIAC) model. We extract those mobility patterns that generalize common spatio-temporal features of all users, and use the mobility intentions as the hidden states from mobility dataset. A new predicting algorithm based on auto-completion is then proposed. The experimental results on real-world datasets demonstrate that the proposed MIAC model can properly capture the regularity of a user's mobility by simultaneously considering the spatial and temporal features. The comparison results also indicate that MIAC model significantly outperforms state-of-the-art location prediction methods, and also can predicts long range locations.