Estimating human trajectories and hotspots through mobile phone data

Abstract

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. Recently, mobile data-based research reached important conclusions about various aspects of human mobility patterns. But how accurately do these conclusions reflect the reality? To evaluate the difference between reality and 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 interpolations) taking into consideration mobility parameters. Moreover, we evaluate the error between real and estimated load using the proposed interpolation methods. From extensive evaluations based on real cellular network activity data of the state of Massachusetts, we show that, with respect to human trajectories, the linear interpolation offers the best estimation for sedentary people while the cubic one for commuters. Another important experimental finding 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. Regarding the load estimation error, we show that by using linear and cubic interpolation methods, we can find the positions of the most crowded regions ("hotspots") with a median error lower than 7%.

Discovering urban activity patterns in cell phone data

“…In recent years this has inspired intensive research, such as the analysis of human mobility behavior, e.g. (Gonzalez et al 2008;Schneider et al 2013;Ratti et al 2006;Ratti et al 2007;Sevtsuk and Ratti 2010;Calabrese et al 2013;Hoteit et al 2014), origin-destination flows, e.g. (Tettamanti and Varga 2014;Wang et al 2013;Caceres et al 2012;Calabrese et al 2011;Friedrich et al 2010), and road usage patterns (Wang et al 2012).…”

Mobile data traffic offloading over Passpoint hotspots

“…It is worth mentioning that since we are working on a cell-based data set with 6-minute interval sessions and in order to capture user's position at each instant of time, we use the following strategy: if the user remains in the same cell in two consecutive sessions, he is considered as a non-moving user (its position is chosen randomly in the cell), however if the user changes its cell from one session to another, he is considered as moving along a linear trajectory from its position in the first cell to its position in the second cell. The latter property was indeed established based on an in-depth analysis about human trajectories by the authors of [26], where they show that for users moving short distances, the linear trajectory is the best estimation of their real trajectories. This property applies strongly in our model, as the region of study is relatively small.…”

Data from mobile phone operators: A tool for smarter cities?

“…Since 2006, a number of studies has been initiated to analyze human mobility patterns (e.g. Eagle and Pentland, 2006;Mateos and Fisher, 2006;Shoval, 2007;Gonzalez et al, 2008;Park et al, 2010;Song et al, 2010aSong et al, , 2010bCsáji et al, 2012;Hoteit et al, 2014). For example, Gonzalez et al (2008) conclude that human trajectories show a high degree of temporal and spatial regularity, and follow simple reproducible patterns.…”