Big Data Driven Hidden Markov Model Based Individual Mobility Prediction at Points of Interest

With the emergence of smartphones and location-based services, user mobility prediction has become a critical enabler for a wide range of applications, like location-based advertising, early warning systems, and citywide traffic planning. A number of techniques have been proposed to either conduct spatio-temporal mobility prediction or forecast the next-place. However, both produce diverse prediction performance for different users and display poor performance for some users. This paper focuses on investigating the effect of living habits on the models of spatio-temporal prediction and next-place prediction, and selects one from these two models for an individual to achieve effective mobility prediction at users’ points of interest. Based on the hidden Markov model (HMM), a spatio-temporal predictor and a next-place predictor are proposed. Living habits are analyzed in terms of entropy, upon which users are clustered into distinct groups. With large-scale factual mobile data captured from a big city, the authors compare the proposed HMM-based predictors with existing state-of-the-art predictors and apply them to different user groups. The results demonstrate the robust performance of the two proposed mobility predictors, which outperform the state of the art for various user groups.

• Record URL:
  https://doi.org/10.1109/TVT.2016.2611654
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2017, IEEE.
• Authors:
  • Lv, Qiujian
  • Qiao, Yuanyuan
  • Ansari, Nirwan
  • Liu, Jun
  • 0000-0002-6336-4931
  • Yang, Jie
• Publication Date: 2017-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 5204-5216
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 66
  • Issue Number: 6
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Entropy (Communications); Global Positioning System; Markov processes; Mathematical prediction; Mobile communication systems; Smartphones; Telecommunications; Trajectory; Urban areas; Wireless communication systems
• Uncontrolled Terms: Big data
• Subject Areas: Data and Information Technology; Transportation (General);

Filing Info

• Accession Number: 01645127
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 29 2017 11:58AM