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TRANSIT: Fine-Grained Human Mobility Trajectory Inference at Scale with Mobile Network Signaling Data

Abstract : 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 proceessing NSD to accurately distinguish mobility phases from stationary activities for individual mobile devices, and reconstruct, at scale, fine-grained human mobility trajectories, by exploiting 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.
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Contributor : Razvan Stanica Connect in order to contact the contributor
Submitted on : Monday, July 26, 2021 - 12:11:13 PM
Last modification on : Friday, July 8, 2022 - 10:09:03 AM
Long-term archiving on: : Wednesday, October 27, 2021 - 6:16:57 PM


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Loïc Bonnetain, Angelo Furno, Nour-Eddin El Faouzi, Marco Fiore, Razvan Stanica, et al.. TRANSIT: Fine-Grained Human Mobility Trajectory Inference at Scale with Mobile Network Signaling Data. Transportation research. Part C, Emerging technologies, Elsevier, 2021, 130, pp.1-34. ⟨10.1016/j.trc.2021.103257⟩. ⟨hal-03299297⟩



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