Context-aware real-time population estimation for metropolis

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.