Mobile Networks and Applications

, Volume 21, Issue 2, pp 367–374 | Cite as

Multi-hop Mobility Prediction

  • Zhiyong YuEmail author
  • Zhiwen Yu
  • Yuzhong Chen


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.


Multi-hop mobility prediction Markov model GPS trajectories Crowd sensing 



This study is partially supported by the National Natural Science Foundation of China (No. 61300103).


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Fuzhou UniversityFuzhouChina
  2. 2.Northwestern Polytechnical UniversityXi’anChina

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