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Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification.

Ming Yan1,2, Shuijing Li2, Chien Aun Chan3,4

  • 1State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a weighted Markov model that classifies mobile users by behavior. This approach improves the accuracy of predicting individual user mobility patterns using mobile communication data.

Keywords:
mobile communicationmobile usermobility predictionuser classificationweighted Markov model

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Area of Science:

  • Data Science
  • Network Science
  • Epidemiology

Background:

  • Mobile communication data offers insights into epidemic spread and traffic.
  • Current mobility prediction models aggregate user data, overlooking individual behavior variations.

Purpose of the Study:

  • To enhance mobile user mobility prediction accuracy.
  • To address the limitations of aggregated data in existing prediction algorithms.

Main Methods:

  • Proposed a weighted Markov prediction model incorporating mobile user classification.
  • Extracted user trajectory information and measured complexity using mobile trajectory entropy.
  • Classified users based on behavior patterns using machine learning algorithms.
  • Optimized model parameters (step threshold, weighting coefficients) per user classification.

Main Results:

  • User classification and optimized model parameters significantly improved prediction performance.
  • The weighted Markov model demonstrated enhanced accuracy compared to aggregated approaches.

Conclusions:

  • Mobile user classification is crucial for accurate mobility prediction.
  • The proposed weighted Markov model offers a more personalized and effective approach to mobility prediction using mobile data.