Physics

Radiology

Trajectory

Markov model

Hidden Markov model

Computer network

Variable-order Markov model

Mobile telephony

Markov chain

Entropy (arrow of time)

Operating system

Distributed computing

Data mining

Astronomy

Machine learning

Mobile computing

Artificial intelligence

Weighting

Mobile device

Mobility model

Maximum-entropy Markov model

Computer science

Quantum mechanics

Mobile radio

Medicine

Sensors

The vast amounts of mobile communication data collected by mobile operators can provide important insights regarding epidemic transmission or traffic patterns. By analyzing historical data and extracting user location information, various methods can be used to predict the mobility of mobile users. However, existing prediction algorithms are mainly based on the historical data of all users at an aggregated level and ignore the heterogeneity of individual behavior patterns. To improve prediction accuracy, this paper proposes a weighted Markov prediction model based on mobile user classification. The trajectory information of a user is extracted first by analyzing real mobile communication data, where the complexity of a user’s trajectory is measured using the mobile trajectory entropy. Second, classification criteria are proposed based on different user behavior patterns, and all users are classified with machine learning algorithms. Finally, according to the characteristics of each user classification, the step threshold and the weighting coefficients of the weighted Markov prediction model are optimized, and mobility prediction is performed for each user classification. Our results show that the optimized weighting coefficients can improve the performance of the weighted Markov prediction model.

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