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

Qiujian Lv, Yuanyuan Qiao, Nirwan Ansari, Jun Liu, Jie Yang

Research output: Contribution to journalArticlepeer-review

110 Scopus citations


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 spatiooral 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 spatiooral 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 spatiooral 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, we 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.

Original languageEnglish (US)
Article number7572081
Pages (from-to)5204-5216
Number of pages13
JournalIEEE Transactions on Vehicular Technology
Issue number6
StatePublished - Jun 2017

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Automotive Engineering


  • Big data
  • cellular data network
  • hidden Markov model (HMM)
  • next-place prediction
  • spatiooral mobility prediction


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