Data driven uncertainty quantification in macroscopic traffic flow models
Résumé
We propose a Bayesian approach for parameter uncertainty quantification in macroscopic traffic flow models from cross-sectional data. We validate the results comparing the error metrics of both first and second order models. While involving more parameters to be calibrated, second order models globally show better performances in reconstructing traffic quantities of interest.
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