Yan Ming, Li Shuijing, Chan Chien Aun, Shen Yinghua, Yu Ying
State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China.
School of Information and Communications Engineering, Communication University of China, Beijing 100024, China.
Sensors (Basel). 2021 Mar 3;21(5):1740. doi: 10.3390/s21051740.
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.
移动运营商收集的大量移动通信数据可以提供有关疫情传播或交通模式的重要见解。通过分析历史数据并提取用户位置信息，可以使用各种方法来预测移动用户的移动性。然而，现有的预测算法主要基于所有用户在聚合层面的历史数据，忽略了个体行为模式的异质性。为了提高预测准确性，本文提出了一种基于移动用户分类的加权马尔可夫预测模型。首先通过分析真实的移动通信数据提取用户的轨迹信息，其中使用移动轨迹熵来衡量用户轨迹的复杂性。其次，基于不同的用户行为模式提出分类标准，并使用机器学习算法对所有用户进行分类。最后，根据每个用户分类的特点，优化加权马尔可夫预测模型的步长阈值和加权系数，并对每个用户分类进行移动性预测。我们的结果表明，优化后的加权系数可以提高加权马尔可夫预测模型的性能。
