Complete Trajectory Reconstruction from Sparse Mobile Phone Data

Guangshuo Chen 1 Aline Carneiro Viana 2 Marco Fiore 3 Carlos Sarraute 4
1 INFINE-POST - INFormation NEtworks
Inria Saclay - Ile de France
2 TRiBE - inTeRnet BEyond the usual
Inria Saclay - Ile de France
3 IEIIT-CNR - Institute of Electronics, Computer and Telecommunication Engineering
CNR - Consiglio Nazionale delle Ricerche [Torino] , Polito - Politecnico di Torino [Torino]
Abstract : Mobile phone data are a popular source of positioning information in many recent studies that have largely improved our understanding of human mobility. These data consist of time-stamped and geo-referenced communication events recorded by network operators, on a per-subscriber basis. They allow for unprecedented tracking of populations of millions of individuals over long periods that span months. Nevertheless, due to the uneven processes that govern mobile communications, the sampling of user locations provided by mobile phone data tends to be sparse and irregular in time, leading to substantial gaps in the resulting trajectory information. In this paper, we illustrate the severity of the problem through an empirical study of a large-scale Call Detail Records (CDR) dataset. We then propose Context-enhanced Trajectory Reconstruction, a new technique that hinges on tensor factorization as a core method to complete individual CDR-based trajectories. The proposed solution infers missing locations with a median displacement within two network cells from the actual position of the user, on an hourly basis and even when as little as 1% of her original mobility is known. Our approach lets us revisit seminal works in the light of complete mobility data, unveiling potential biases that incomplete trajectories obtained from legacy CDR induce on key results about human mobility laws, trajectory uniqueness, and movement predictability.
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Submitted on : Friday, September 13, 2019 - 1:41:05 PM
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Guangshuo Chen, Aline Carneiro Viana, Marco Fiore, Carlos Sarraute. Complete Trajectory Reconstruction from Sparse Mobile Phone Data. EPJ Data Science, EDP Sciences, 2019. ⟨hal-02286080⟩

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