Context-aware real-time population estimation for metropolis

Abstract

Achieving accurate, real-time, and spatially fine-grained population estimation for a metropolitan city is extremely valuable for a variety of applications. Previous solutions look at data generated by human activities, such as night time lights and phone calls, for population estimation. However, these mechanisms cannot achieve both real-time and fine-grained population estimation because the data sampling rate is low and spatial granularity chosen is improper. We address these two problems by leveraging a key insight --- people frequently use data plan on cellphones and leave mobility signatures on cellular networks. Therefore, we are able to exploit these cellular signatures for real-time population estimation. Extracting population information from cellular data records is not easy because the number of users recorded by a cellular tower is not equal to the population covered by the tower, and mobile users' behavior is spatially and temporally different, where static estimating model does not work. We exploit context-aware city segmentation and dynamic population estimation model to address these challenges. We show that the population estimation error is reduced by 22.5% on a cellular dataset that includes 1 million users.