User mobility from the view of cellular data networks

Understanding the user mobility is essential to resource optimization and algorithm evaluation in mobile networks, such as network planning, content distribution, and evaluation of hand-over mechanisms. Existing human mobility models focus on extracting mobility patterns from Call Detail Records (CDRs) or WiFi traces. While the former only captures movements during phone calls, the latter does not provide direct answers to mobility of cellular network users in a large scale. In this paper, we take the first step to investigate if the mobility properties derived from cellular data traffic is different from the previous findings using other data source, especially the commonly used CDR based approach. We present a comprehensive characterization of the mobility patterns from the cellular data networks´ perspective, using a set of systematic methods. We find that the data network records can provide finer granularity of location and movement information. Three different temporal movement patterns are identified. Furthermore, we propose a new method for predicting future application usage given the mobility patterns and show promising results.