PRIVA'MOV: Analysing Human Mobility Through Multi-Sensor Datasets

Abstract : The wide adoption of mobile devices has created unprecedented opportunities to collect mobility traces and make them available for the research community to conduct interdisciplinary research. However, mobility traces available in the public domain are usually restricted to traces resulting from a single sensor (e.g., either GPS, GSM or WiFi). In this paper, we present the PRIVA'MOV dataset, a novel dataset collected in the city of Lyon, France on which user mobility has been collected using multiple sensors. More precisely, this dataset contains mobility traces of about 100 persons including university students, staff and their family members over 15 months collected through the GPS, WiFi, GSM, and accelerometer sensors. We provide in this paper both a quantitative and a preliminary qualitative analysis of this dataset. Specifically, we report the number of visited points of interests, GSM antennas and WiFi hotspots and their distribution across the various users. We finally analyse the uniqueness of human mobility by considering the various sensors.
Type de document :
Communication dans un congrès
NetMob 2017, Apr 2017, Milan, Italy. 〈〉
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Contributeur : Antoine Boutet <>
Soumis le : mardi 29 août 2017 - 13:44:14
Dernière modification le : mercredi 31 octobre 2018 - 12:24:26


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  • HAL Id : hal-01578557, version 1


Sonia Ben Mokhtar, Antoine Boutet, Louafi Bouzouina, Patrick Bonnel, Olivier Brette, et al.. PRIVA'MOV: Analysing Human Mobility Through Multi-Sensor Datasets. NetMob 2017, Apr 2017, Milan, Italy. 〈〉. 〈hal-01578557〉



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