Multi-hop Mobility Prediction

With the occurrence of large-scale human trajectories, which imply spatial and temporal patterns, the subject of mobility prediction has been widely studied. A number of approaches are proposed to predict the next location of a user. In this paper, we expect to lengthen the temporal dimension of prediction results beyond one hop. To predict the future locations of a user at every time unit within a specified time, we propose a Markov-based multi-hop mobility prediction (Markov–MHMP) algorithm. It is a hybrid approach that considers multiple factors including personal habit, weekday similarity, and collective behavior. On a GPS dataset, our approach performs prediction better than baseline and state-of-the-art approaches under several evaluation criteria.

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