Discovering and modeling meta-structures in human behavior from city-scale cellular data

For a long time, researchers explore spatio-temporal properties in mobility to understand human behavior. They have discovered many statistical laws about human dynamics. Unfortunately, we still have limited knowledge about the spatio-temporal structure of individuals' movement at a large scale. In this paper, we studied the unified spatio-temporal structures (i.e., meta-structures) in human mobility. We hereby propose a meta-structure discovery algorithm by coupling both topology and spatio-temporal attributes of mobility graphs. With the construction of individual profiles from meta-structure analyses, we provided a novel mobility model from a process-driven perspective, which reduced the dependence of many existing models on the consistency between local and global mobility statistics. We gained some insights on the dominating meta-structures in human mobility by leveraging mobile data in a large city. The statistical distribution of meta-structures is found to be determined by the intrinsic heterogeneity of spatio-temporal properties in human behavior. Our model evaluation showed that a process with basic rules could demonstrate the key statistical properties in mobility meta-structures. We believe that these approaches and observations would be a good reference for management of human mobility in mobile networks and transportation systems.