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. Index Terms—Mobility patterns, interpolation methods, tra-jectory estimation, radius of gyration. I. INTRODUCTION Human mobility and behavior pattern analysis has long been a prominent research topic for social scientists, urban planners, geographers and telecommunication researchers, but the perti-nency of its results has thus far been limited by the availability of quality data and suitable data mining techniques. 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 characteristics, such as human mobility and calling patterns [1] [2], social networks [3] [4], content consumption cartography [5], urban and transport planning [6] and network design [7]. Nevertheless, in such user displacement sampling data, a high uncertainty is related to users movements, since available samples strongly depend on the user-network interaction frequency. For instance, we cannot determine the user positions between the calls with an acceptable accuracy. Some modeling techniques have been proposed in the literature to predict user movement between two places. Authors in [9] and [10] infer the top-k routes traveling a given location sequence within a specified travel time from uncertain ckeck-in data. These works permit to identify the most popular travel routes in a city but it does not allow to construct the time-senstive routes.