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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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https://hal.archives-ouvertes.fr/hal-03202124
Contributor : Paola Goatin Connect in order to contact the contributor
Submitted on : Wednesday, November 17, 2021 - 9:30:21 PM
Last modification on : Friday, January 21, 2022 - 10:32:20 AM

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UCQTFM.pdf
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  • HAL Id : hal-03202124, version 2

Citation

Alexandra Würth, Mickaël Binois, Paola Goatin, Simone Göttlich. Data driven uncertainty quantification in macroscopic traffic flow models. 2021. ⟨hal-03202124v2⟩

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