Global and individual mobility pattern discovery based on hotspots

Data collected from the mobile Internet have the potential knowledge to provide important human mobility patterns. Understanding human mobility patterns is important to many location-based services, and could be used to predict users' behavior. In this paper, we concentrate on the issue of discovering human mobility patterns on both global and individual levels based on hotspots. We study the human mobility trajectories during 22 days for 3474 individuals collected at the core of a metropolitan Long Term Evolution (LTE) network in China. We employ a parameter-free method to detect hotspots, and demonstrate the effectiveness of our mobility pattern discovery algorithm by using the hotspots identified on both global and individual levels. We analyze the occurrence time distribution of these patterns and find that the global mobility patterns have higher occurrence probability in the morning, which indicates that people in a city tend to share the common commuting routes. For individual mobility patterns, there exists a strong spatiotemporal correlation property, implying that the individual mobility patterns have their own typical occurrence time depending on the pattern's context.