Estimating Real Human Trajectories through Mobile Phone Data

Nowadays, the huge worldwide mobile-phone penetration is increasingly turning the mobile network into a gigantic ubiquitous sensing platform, enabling large-scale analysis and applications. In recent years, mobile data-based research reaches important conclusions about various aspects of human mobility patterns and trajectories. But how accurately do these conclusions reflect the reality? In order to evaluate the difference between the reality and the approximation methods, we study in this paper the error between real human trajectory and the one obtained through mobile phone data using different interpolation methods (linear, cubic, nearest and spline interpolations) while taking into account some mobility parameters. From extensive evaluations based on real cellular network activity data of the Boston metropolitan area, we show that the linear interpolation offers the best estimation for sedentary people and the cubic one for commuters. Moreover, the nearest interpolation appears as the best one for “ordinary people” doing regular stops and standard displacements. Another important experimental finding described in this paper is that trajectory estimation methods show different error regimes whether used within or outside the “territory” of the user defined by the radius of gyration.