Regularity-based wireless subscriber population estimation

Fine-grained dynamic population estimation is in an increasingly high demand as it has numerous applications in wireless network engineering, urban planning, location-based services and mobile applications, and advertisement. In this paper, we introduce a framework that dynamically estimates the wireless subscriber population of an arbitrary fine-grained area based on the current cellular phone usage. This framework takes advantage of strong regularities, low variance, and low information entropy in human mobility and phone usage patterns; thus simplifying the estimation for wireless carriers and other big entities while maintaining a high accuracy. We implemented our `regularity-based´ framework using empirical data. Comparison with experimentally collected data shows a significant improvement in the accuracy of population estimation compared to population count based on cellular phone usage.