DETERMINING TRAJECTORIES FROM MOBILE TELEPHONE DATA

Network Signaling Data (NSD) are composed of control-plane events in the mobile network architecture generated by all interactions with mobile devices that are needed for the operation and management of the telecommunication system. NSD represent a very rich source of spatio-temporal information currently collected by network providers, but mostly unexploited for fine-grained reconstruction of human-centric trajectories. In this patent, we propose a method capable of processing NSD to accurately distinguish mobility phases from stationary activities for individual mobile devices, and reconstruct, at scale, fine-grained human mobility trajectories, by exploiting, with a DBSCAN-based clustering approach, the inherent recurrence of human mobility and the higher sampling rate of NSD. The validation on a ground-truth dataset of GPS trajectories showcases the superior performance of our proposed method (80% precision and 96% recall) with respect to state-of-the-art solutions in the identification of movement periods, as well as an average 190-meter spatial accuracy in the estimation of the trajectories. The method is highly scalable and can be used to process massive NSD dataset of tens of millions of trajectories and perform an exploratory analysis of, e.g., city-wide transport mode shares, recurrent commuting paths, urban attractivity and analysis of mobility flows.