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Multi-hop Mobility Prediction

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Abstract

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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Acknowledgments

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

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Authors and Affiliations

  1. Fuzhou University, Fuzhou, China

    Zhiyong Yu & Yuzhong Chen

  2. Northwestern Polytechnical University, Xi’an, China

    Zhiwen Yu

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  1. Zhiyong Yu
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  2. Zhiwen Yu
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  3. Yuzhong Chen
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Correspondence to Zhiyong Yu.

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Yu, Z., Yu, Z. & Chen, Y. Multi-hop Mobility Prediction. Mobile Netw Appl 21, 367–374 (2016). https://doi.org/10.1007/s11036-015-0668-2

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