User mobility modeling based on mobile traffic data collected in real cellular networks

Abstract

Nowadays, the development of mobile communication technology results in a huge amount of mobile traffic data. The Call Detail Records (CDRs) contain considerable users' traffic-related information, e.g., the user ID, service begin time, service duration and the communication cell which the users connect. Combining with the position information of base stations, CDRs substantially reflect the users' activity trajectories. In order to efficiently analyze the massive traffic data from the view of user mobility, several technical challenges have to be tackled including data collection, trajectory construction, data noise removing, data storage and analyzing methods. This paper introduces a mobility modeling method for wireless big data. The mobility modeling is based on real traffic data collected from 4G cellular networks including 3 different cities in a western province of China. Our experiments discover the user's mobility feature, changing of city hotspots and the mobility patterns. By considering location data trends across all users, it becomes possible to understand mobility on many important applications such as traffic prediction, radio resource optimization and allocation, mobile computing and urban planning.