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

Sonia Ben Mokhtar; Antoine Boutet; Louafi Bouzouina; Patrick Bonnel; Olivier Brette; Lionel Brunie; Mathieu Cunche; Stephane D 'Alu; Vincent Primault; Patrice Raveneau; Herve Rivano; Razvan Stanica

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

Sonia Ben Mokhtar ¹ Antoine Boutet ¹ Louafi Bouzouina ² Patrick Bonnel ² Olivier Brette ³ Lionel Brunie ¹ Mathieu Cunche ^{4, 5} Stephane D 'Alu ^{4, 6} Vincent Primault ¹ Patrice Raveneau ⁷ Herve Rivano ⁶ Razvan Stanica ^{6, 8}

1 DRIM - Distribution, Recherche d'Information et Mobilité

LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

2 LAET - Laboratoire Aménagement Économie Transports

3 TRIANGLE - Triangle : action, discours, pensée politique et économique

4 CITI - CITI Centre of Innovation in Telecommunications and Integration of services

5 PRIVATICS - Privacy Models, Architectures and Tools for the Information Society

Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services

6 AGORA - ALGorithmes et Optimisation pour Réseaux Autonomes

Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services

7 L3I - Laboratoire Informatique, Image et Interaction

8 INSA Lyon - Institut National des Sciences Appliquées de Lyon

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.

Document type :

Conference papers

Domain :

Computer Science [cs] / Networking and Internet Architecture [cs.NI]

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https://hal.inria.fr/hal-01578557
Contributor : Antoine Boutet <>
Submitted on : Tuesday, August 29, 2017 - 1:44:14 PM
Last modification on : Wednesday, November 20, 2019 - 2:47:00 AM

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