Apprendre de la crise sanitaire : Existe-t-il des conditions favorables au bien-être des télétravailleurs à domicile ?

Nande, Florence; Weber, Marie-Laure; Bouchet, Stéphanie; Loup, Pierre

doi:10.3917/grh.044.0013

Cited by 3 publications

(1 citation statement)

References 76 publications

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“…We should note that Q and O are strongly linked through traffic processes, which defines the socalled 'fundamental diagrams' [33] with the disappearance of all COVID-19 mobility constraints, but there is an intriguing pattern of persisting lower Q and O values during morning (07:00 to 10:00) and evening (17:00 to 22:00) rush hours than in 2019. This reduction in morning traffic compared to pre-crisis conditions may be attributed to the widespread adoption of remote working [45].…”

Section: Traffic Flux and Vehicles Occupancy Relationshipsmentioning

confidence: 99%

Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity

Bonnemaizon,

Ciais,

Zhou

et al. 2024

Preprint

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Road transportation accounts for up to 35% of carbon dioxide and 49% of nitrogen oxides emissions in the Paris region. In an effort to mitigate those emissions, local authorities have implemented a climate plan that includes measures such as converting car lanes into cycle paths and enforcing strict low emission zones. Moreover, the COVID-19 crisis has had a notable impact on citizens' behavior and traffic patterns. There was a sharp decline and subsequent recovery in traffic during spring 2020, followed by an extended period of restrictions in late 2020 and 2021, leading to decreased traffic levels. However, estimates of city traffic patterns are often incomplete and of coarse spatio-temporal resolution, even where extensive networks of sensors exist. Here, we use a machine learning approach to assess data from 2086 magnetic road sensors in the city of Paris, resulting in a comprehensive dataset of hourly traffic flow and road occupancy covering 6846 road segments from 2018 to 2022. Our model captures flow and occupancy on an hourly and road segment basis with a Symmetric Mean Absolute Percentage Error of 37% and 54% respectively, allowing us to create a new map of hourly transportation patterns in Paris.

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Section: Traffic Flux and Vehicles Occupancy Relationshipsmentioning

confidence: 99%

Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity

Bonnemaizon,

Ciais,

Zhou

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity

Bonnemaizon,

Ciais,

Zhou

et al. 2024

Environ. Res.: Infrastruct. Sustain.

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Road transportation accounts for up to 35% of carbon dioxide and 49% of nitrogen oxides emissions in the Paris region. However, estimates of city traffic patterns are often incomplete and of coarse spatio-temporal resolution, even where extensive networks of sensors exist. This study uses a machine learning approach to analyze data from 2086 magnetic road sensors across Paris, generating a detailed dataset of hourly traffic flow and road occupancy covering 6846 road segments from 2018 to 2022. Our model captures flow and occupancy with a Symmetric Mean Absolute Percentage Error of 37% and 54% respectively, providing high-resolution insights into traffic patterns. These insights allow for the creation of a comprehensive map of hourly transportation patterns in Paris, offering a robust framework for assessing traffic variables for each significant road link in the city. The model's ability to incorporate an emission factor based on the mean speed of the vehicle fleet, derived from flow and occupancy data, holds promise for developing a detailed CO2 and pollutant inventory. This methodology is not limited to Paris; it can be applied to other urban centers with similar data availability, highlighting its potential as a versatile tool for sustainable urban monitoring.

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10 ans de la revue @grh : une cartographie des publications en ressources humaines

Perez,

Peterson

2023

@Grh

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À l’occasion des 10 ans de la revue @grh , cet article vise à présenter une cartographie des publications de la revue depuis sa création en 2011 à l’occasion du XXII e congrès de l’AGRH. La sélection de 193 articles dans les 44 numéros de la revue entre 2011 et 2022 nous permet de mettre en évidence les principales thématiques abordées, les méthodologies utilisées et les auteurs contributeurs. Notre étude repose sur l’élaboration d’une représentation approfondie des articles à travers l’étude des mots-clés, des résumés, des thèmes et des auteurs à l’aide d’outils graphiques avec analyse des données assistée par ordinateur. Les résultats mettent en évidence la prédominance des thèmes largement abordés dans la revue et les méthodologies les plus fréquemment utilisées. Cette cartographie permet d’identifier les thèmes fréquemment étudiés et offre des perspectives de recherche pour les sujets et méthodes émergents.

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Apprendre de la crise sanitaire : Existe-t-il des conditions favorables au bien-être des télétravailleurs à domicile ?

Cited by 3 publications

References 76 publications

Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity

Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity

Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity

10 ans de la revue @grh : une cartographie des publications en ressources humaines

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