With the growth of using cell phones and the increase in the diversity of smart mobile devices, a massive volume of data is generated continuously in the process of using these devices. Among these data, Call Detail Records, CDR, is highly remarkable. Since CDR contains both temporal and spatial labels, mobility analysis of CDR is one of the favorite subjects of study among the researchers. The user next location prediction is one of the main problems in the field of human mobility analysis. In this paper, we propose a regression framework to predict next locations of users of cellular operators. We propose domain-specific data processing strategies and design a deep neural network model which is based on recurrent neurons and performs regression tasks. Using this framework on real-world data, we show that the error of the prediction decreases up to 74% in comparison to the traditional location prediction models. The results of this paper can be helpful in many applications from urban planning and digital marketing to predicting the spread of pandemics.

随着使用手机的增长以及智能移动设备多样性的增加，在使用这些设备的过程中不断产生大量数据。在这些数据中，呼叫详细记录CDR非常出色。由于CDR同时包含时间和空间标记，因此CDR的迁移率分析是研究人员中最喜欢的研究主题之一。用户下一位置预测是人类移动性分析领域中的主要问题之一。在本文中，我们提出了一种回归框架来预测蜂窝运营商用户的下一个位置。我们提出了特定领域的数据处理策略，并设计了一个基于递归神经元并执行回归任务的深度神经网络模型。使用此框架处理现实数据，我们显示，与传统位置预测模型相比，预测误差最多可降低74％。本文的结果可在从城市规划和数字营销到预测大流行病传播的许多应用中提供帮助。