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

Sensors

Chien Aun Chan¹  Ying Yu²  Shuijing Li²  Yinghua Shen²  Ming Yan³ 

[1] Insta-Wireless, Notting Hill, VIC 3168, Australia;School of Information and Communications Engineering, Communication University of China, Beijing 100024, China;State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China;

关键词: mobility prediction;    weighted Markov model;    mobile user;    user classification;    mobile communication;   

DOI  :  10.3390/s21051740

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown