Aggregate Human Mobility Modeling Using Principal Component Analysis
Accurate modeling of aggregate human mobility benefits many aspects of cellular mobile networks. Compared with traditional approaches, the cellular networks provide information for aggregate human mobility in urban space with large spatial extent and continuous temporal coverage, due to the high penetration of cell phones. In this paper, a model by utilizing Principal Component Analysis (PCA) is proposed to explore the space-time structure of aggregate human mobility. The original data were collected by cellular networks in a southern city of China, recording population distribution by dividing the city into thousands of pixels. By applying PCA to original data, the low intrinsic dimensionality is revealed.