Attentional Markov Model for Human Mobility Prediction

Abstract

Accurate human mobility prediction is important for many applications in wireless networks, including intelligent content caching and prefetching, network optimization, etc. However, modeling mobility patterns has been a challenging problem due to the complicated human mobility patterns influenced by the long-term correlation with historical trajectories and context information, and the long time interval between consecutive mobility records. In this paper, we integrate the novel attention technique into the Markov model to predict future locations. This model allows us to consider the time context of users' mobility behavior, which overcomes the disadvantage of the traditional Markov model in modeling the periodicity of trajectories. We conducted extensive evaluations using two different mobility datasets, which involve over 20,000 users. Our evaluations show that our proposed solution outperforms the state-of-the-art algorithms by over 6.6% on average. In addition, compared with the method based on deep recurrent neural networks achieving the same performance, our proposed model runs significantly faster, i.e., reducing the computation time by over 170 times, demonstrating the effeteness of our proposed model.