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CORRIGENDUM: Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity (2024 Environ. Res.: Infrastruct. Sustain. 4 035010)

Year: 2026

Type: erratum

Abstract: CORRIGENDUM: Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity (2024 Environ. Res.: Infrastruct. Sustain. 4 035010), Bonnemaizon, Xavier, Ciais, Philippe, Zhou, Chuanlong, Arous, Simon Ben, Davis, Steven J, Megel, Nicolas

Source: Environmental Research Infrastructure and Sustainability

DOI: 10.1088/2634-4505/ae34e5

Authors: Xavier Bonnemaizon, Philippe Ciais, Chuanlong Zhou, Simon Ben Arous, Steven J Davis, Nicolas Megel

Institutions: Centre National de la Recherche Scientifique, Université de Versailles Saint-Quentin-en-Yvelines, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Université Paris-Saclay, Laboratoire des Sciences du Climat et de l'Environnement, University of California, Irvine

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,515

OpenAlex ID: W7124448345

Cites: 1

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