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

Autor: Nour-Eddin El Faouzi, Marco Fiore, Loïc Bonnetain, Angelo Furno, Cezary Ziemlicki, Razvan Stanica, Zbigniew Smoreda
Přispěvatelé: Laboratoire d'Ingénierie Circulation Transport (LICIT UMR TE ), École Nationale des Travaux Publics de l'État (ENTPE)-Université de Lyon-Université Gustave Eiffel, Institute IMDEA Networks [Madrid], ALGorithmes et Optimisation pour Réseaux Autonomes (AGORA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), Orange Labs [Chatillon], Orange Labs, ANR-18-CE22-0008,PROMENADE,Plateforme pour la Mobilité Multimodale Résiliente par réseaux multicouches et élaboration de données massives temps-réel(2018), ANR-18-CE25-0011,CANCAN,Adaptation basée sur le contenu et le contexte dans les réseaux mobiles(2018), CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Big Data
Computer science
Big data
Inference
Transportation
02 engineering and technology
Management Science and Operations Research
Individual Trajectory
computer.software_genre
03 medical and health sciences
[SPI.GCIV.IT]Engineering Sciences [physics]/Civil Engineering/Infrastructures de transport
[INFO.INFO-MC]Computer Science [cs]/Mobile Computing
020204 information systems
0202 electrical engineering
electronic engineering
information engineering
Human-Centric Mobility
Leverage (statistics)
030304 developmental biology
Civil and Structural Engineering
0303 health sciences
business.industry
Mobile Phone Data
Urban Computing
Identification (information)
Mobile phone
Automotive Engineering
Cellular network
Global Positioning System
Data mining
business
Mobile device
computer
[PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis
Statistics and Probability [physics.data-an]
Zdroj: Transportation research. Part C, Emerging technologies
Transportation research. Part C, Emerging technologies, 2021, 130, pp.1-34. ⟨10.1016/j.trc.2021.103257⟩
Transportation research. Part C, Emerging technologies, Elsevier, 2021, 130, pp.1-34. ⟨10.1016/j.trc.2021.103257⟩
ISSN: 0968-090X
1879-2359
DOI: 10.1016/j.trc.2021.103257⟩
Popis: 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. TRUE pub
Databáze: OpenAIRE
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