Data-driven uncertainty quantification in macroscopic traffic flow models
type: Item
instance of: scholarly article
title: Data-driven uncertainty quantification in macroscopic traffic flow models
zbMATH Open document ID: 1501.35460
author: Alexandra Würth
author: M. Binois
author: Paola Goatin
author: S. Göttlich
published in: Advances in Computational Mathematics
publication date: 2022-11-17T00:00:00Z
Mathematics Subject Classification ID: 35R30
Mathematics Subject Classification ID: 35L65
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 76A30
zbMATH DE Number: 7618978
zbMATH Keywords: macroscopic traffic flow models
zbMATH Keywords: Godunov scheme
zbMATH Keywords: loop detector traffic data
zbMATH Keywords: Bayesian calibration
zbMATH Keywords: parameter estimation
zbMATH Keywords: optimization
describes a project that uses: HLLE
describes a project that uses: mcmcse
MaRDI profile type: Publication
full work available at URL: s10444-022-09989-5
OpenAlex ID: W3193813997
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DOI: 10.1007/S10444-022-09989-5
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description: scientific article; zbMATH DE number 7618978