TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data

Call detail records (CDR) collected by mobile phone network providers have been largely used to model and analyze human-centric mobility. Despite their potential, they are limited in terms of both spatial and temporal accuracy thus being unable to capture detailed human mobility information. Network Signaling Data (NSD) represent a much richer source of spatio-temporal information currently collected by network providers, but mostly unexploited for fine-grained reconstruction of human-centric trajectories. In this paper, we present TRANSIT, TRAjectory inference from Network SIgnaling daTa, a novel framework 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 TRANSIT (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 m spatial accuracy in the estimation of the trajectories. We also leverage TRANSIT to process a unique large-scale NSD dataset of more than 10 millions of individuals and perform an exploratory analysis of city-wide transport mode shares, recurrent commuting paths, urban attractivity and analysis of mobility flows. •Our study addresses locational uncertainty and temporal sparsity using mobile phone network signaling data.•We provide the TRANSIT (TRAjectory inference from Network Signaling daTa) framework for processing network signaling data, able to accurately tell apart movement intervals from stationary activity periods for each mobile device and infer fine-grained human mobility trajectories during the associated movement intervals.•We validate TRANSIT with ground truth GPS data and show that our framework exceeds the performance of the existing methods.•We implement and discuss large-scale human-mobility applications of TRANSIT.