MIAC: A Mobility Intention Auto-Completion Model for Location Prediction

Abstract

Location prediction is essential to many proactive applications and many research works show that human mobility is highly predictable. However, existing works are reported with limited improvements in using generalized spatio-temporal features and unsatisfactory accuracy in complex human mobility. To address these challenges, a Mobility Intention and Auto-Completion (MIAC) model is proposed. We extract mobility patterns to capture common spatio-temporal features of all users, and use mobility intentions to characterize these mobility patterns. 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 in human mobility by simultaneously considering spatial and temporal features. The comparison results also indicate that MIAC model significantly outperforms state-of-the-art location prediction methods, and can also predict long range locations.