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. The structure of all the pixel population variations could be well captured by a small set of eigen pixel population variations, an introduced notion capturing significant temporal patterns across all the pixel population variations. According to their temporal features, eigen pixel population variations can be divided into three categories, and each pixel population variation can be decomposed into three corresponding constitutions: deterministic trends, short-lived spikes, and noise. Furthermore, there is also a relation between the variance of a pixel population variation and its dominated constitution. The most significant eigen pixel population variations are utilized in the applications of forecasting and anomaly detection.