Using mobile phone data analysis for the estimation of daily urban dynamics

Danya Bachir; Vincent Gauthier; Mounim El Yacoubi; Ghazaleh Khodabandelou

doi:10.1109/ITSC.2017.8317956

Using mobile phone data analysis for the estimation of daily urban dynamics

Danya Bachir
, Vincent Gauthier
, Mounim El Yacoubi
, Ghazaleh Khodabandelou

Université Paris-Saclay

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The estimation of population dynamics has become a crucial public transport planning issue. The scope of this paper is the estimation of time variant population densities at fine-grained level using geolocalized mobile phone (MP) data. After preprocessing anonymized aggregated MP data of the complete Greater Paris area, we apply spatial mapping methods to project the MPs locations from network cells to census blocks. Prior to the calibration of MP densities with national census population (static model), we estimate blocks land-use to filter out noisy areas. Our loglinear regression model achieves high performance regarding several metrics, and our hybrid mapping method grants competitive performance with respect to the state of the art. Following our static parameters interpretation, we provide a novel relation for daily population dynamics. We validate this dynamic model with sport events attendances.

Original language	English
Title of host publication	2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	626-632
Number of pages	7
ISBN (Electronic)	9781538615256
DOIs	https://doi.org/10.1109/ITSC.2017.8317956
Publication status	Published - 2 Jul 2017
Externally published	Yes
Event	20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017 - Yokohama, Kanagawa, Japan Duration: 16 Oct 2017 → 19 Oct 2017

Publication series

Name	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume	2018-March
ISSN (Electronic)	2153-0017

Conference

Conference	20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
Country/Territory	Japan
City	Yokohama, Kanagawa
Period	16/10/17 → 19/10/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 15 Life on Land

Keywords

Big Data
Data Analysis
Machine Learning
Mobile Phone Data
Population Monitoring
Urban Dynamics

Access to Document

10.1109/ITSC.2017.8317956

Cite this

Bachir, D., Gauthier, V., Yacoubi, M. E., & Khodabandelou, G. (2017). Using mobile phone data analysis for the estimation of daily urban dynamics. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017 (pp. 626-632). Article 8317956 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Vol. 2018-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2017.8317956

Bachir, Danya ; Gauthier, Vincent ; Yacoubi, Mounim El et al. / Using mobile phone data analysis for the estimation of daily urban dynamics. 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 626-632 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC).

@inproceedings{705699b474064879bbeb8c0af1bccfea,

title = "Using mobile phone data analysis for the estimation of daily urban dynamics",

abstract = "The estimation of population dynamics has become a crucial public transport planning issue. The scope of this paper is the estimation of time variant population densities at fine-grained level using geolocalized mobile phone (MP) data. After preprocessing anonymized aggregated MP data of the complete Greater Paris area, we apply spatial mapping methods to project the MPs locations from network cells to census blocks. Prior to the calibration of MP densities with national census population (static model), we estimate blocks land-use to filter out noisy areas. Our loglinear regression model achieves high performance regarding several metrics, and our hybrid mapping method grants competitive performance with respect to the state of the art. Following our static parameters interpretation, we provide a novel relation for daily population dynamics. We validate this dynamic model with sport events attendances.",

keywords = "Big Data, Data Analysis, Machine Learning, Mobile Phone Data, Population Monitoring, Urban Dynamics",

author = "Danya Bachir and Vincent Gauthier and Yacoubi, \{Mounim El\} and Ghazaleh Khodabandelou",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017 ; Conference date: 16-10-2017 Through 19-10-2017",

year = "2017",

month = jul,

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doi = "10.1109/ITSC.2017.8317956",

language = "English",

series = "IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Bachir, D, Gauthier, V , Yacoubi, ME & Khodabandelou, G 2017, Using mobile phone data analysis for the estimation of daily urban dynamics. in 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017., 8317956, IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, vol. 2018-March, Institute of Electrical and Electronics Engineers Inc., pp. 626-632, 20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017, Yokohama, Kanagawa, Japan, 16/10/17. https://doi.org/10.1109/ITSC.2017.8317956

Using mobile phone data analysis for the estimation of daily urban dynamics. / Bachir, Danya; Gauthier, Vincent ; Yacoubi, Mounim El et al.
2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 626-632 8317956 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Vol. 2018-March).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Using mobile phone data analysis for the estimation of daily urban dynamics

AU - Bachir, Danya

AU - Gauthier, Vincent

AU - Yacoubi, Mounim El

AU - Khodabandelou, Ghazaleh

PY - 2017/7/2

Y1 - 2017/7/2

N2 - The estimation of population dynamics has become a crucial public transport planning issue. The scope of this paper is the estimation of time variant population densities at fine-grained level using geolocalized mobile phone (MP) data. After preprocessing anonymized aggregated MP data of the complete Greater Paris area, we apply spatial mapping methods to project the MPs locations from network cells to census blocks. Prior to the calibration of MP densities with national census population (static model), we estimate blocks land-use to filter out noisy areas. Our loglinear regression model achieves high performance regarding several metrics, and our hybrid mapping method grants competitive performance with respect to the state of the art. Following our static parameters interpretation, we provide a novel relation for daily population dynamics. We validate this dynamic model with sport events attendances.

AB - The estimation of population dynamics has become a crucial public transport planning issue. The scope of this paper is the estimation of time variant population densities at fine-grained level using geolocalized mobile phone (MP) data. After preprocessing anonymized aggregated MP data of the complete Greater Paris area, we apply spatial mapping methods to project the MPs locations from network cells to census blocks. Prior to the calibration of MP densities with national census population (static model), we estimate blocks land-use to filter out noisy areas. Our loglinear regression model achieves high performance regarding several metrics, and our hybrid mapping method grants competitive performance with respect to the state of the art. Following our static parameters interpretation, we provide a novel relation for daily population dynamics. We validate this dynamic model with sport events attendances.

KW - Big Data

KW - Data Analysis

KW - Machine Learning

KW - Mobile Phone Data

KW - Population Monitoring

KW - Urban Dynamics

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U2 - 10.1109/ITSC.2017.8317956

DO - 10.1109/ITSC.2017.8317956

M3 - Conference contribution

AN - SCOPUS:85046259178

T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

SP - 626

EP - 632

BT - 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017

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ER -

Bachir D, Gauthier V , Yacoubi ME, Khodabandelou G. Using mobile phone data analysis for the estimation of daily urban dynamics. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 626-632. 8317956. (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC). doi: 10.1109/ITSC.2017.8317956

Using mobile phone data analysis for the estimation of daily urban dynamics

Abstract

Publication series

Conference

UN SDGs

Keywords

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