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Journal Articles Advances in Computational Mathematics Year : 2022

Data driven uncertainty quantification in macroscopic traffic flow models

Abstract

We propose a Bayesian approach for parameter uncertainty quantification in macroscopic traffic flow models from cross-sectional data. A bias term is introduced and modeled as a Gaussian process to account for the traffic flow models limitations. We validate the results comparing the error metrics of both first and second order models, showing that second order models globally perform better in reconstructing traffic quantities of interest.
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Dates and versions

hal-03202124 , version 1 (19-04-2021)
hal-03202124 , version 2 (17-11-2021)

Identifiers

  • HAL Id : hal-03202124 , version 2

Cite

Alexandra Würth, Mickael Binois, Paola Goatin, Simone Göttlich. Data driven uncertainty quantification in macroscopic traffic flow models. Advances in Computational Mathematics, 2022, 48. ⟨hal-03202124v2⟩
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