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

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Knowledge Science, Engineering and Management (KSEM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10412))

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.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61472418), the Major R&D Plan (Grant No. 2016YFC1202204).

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Authors and Affiliations

  1. Beijing Key Laboratory of IOT Information Security, Institute of Information Engineering, CAS, Beijing, 100093, China

    Feng Yi, Zhi Li, Hongtao Wang, Weimin Zheng & Limin Sun

  2. School of Cyber Security, University of Chinese Academy of Sciences, 19 A Yuquan Rd, Shijingshan District, Beijing, 100049, People’s Republic of China

    Feng Yi & Hongtao Wang

Authors
  1. Feng Yi
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  2. Zhi Li
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  3. Hongtao Wang
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  4. Weimin Zheng
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  5. Limin Sun
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Corresponding author

Correspondence to Zhi Li .

Editor information

Editors and Affiliations

  1. Deakin University, Burwood, Victoria, Australia

    Gang Li

  2. University of Arizona, Tucson, Arizona, USA

    Yong Ge

  3. Southwest University, Chongqing, China

    Zili Zhang

  4. Peking University, Beijing, China

    Zhi Jin

  5. University of Technology Sydney, Sydney, New South Wales, Australia

    Michael Blumenstein

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Yi, F., Li, Z., Wang, H., Zheng, W., Sun, L. (2017). MIAC: A Mobility Intention Auto-Completion Model for Location Prediction. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_37

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  • DOI: https://doi.org/10.1007/978-3-319-63558-3_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63557-6

  • Online ISBN: 978-3-319-63558-3

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