A regression framework for predicting user’s next location using Call Detail Records

Abstract

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.