Uncertainty quantification in a macroscopic traffic flow model calibrated on GPS data

Uncertainty quantification in a macroscopic traffic flow model calibrated on GPS data

The objective of this paper is to analyze the inclusion of one or more random parameters into the deterministic Lighthill-Whitham-Richards traffic flow model and use a semi-intrusive approach to quantify uncertainty propagation. To verify the validity of the method, we test it against real data comi...

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Bibliographic Details
Main Authors: Enrico Bertino, Régis Duvigneau, Paola Goatin
Format: Article
Language:English
Published: AIMS Press 2020-01-01
Series:Mathematical Biosciences and Engineering
Subjects:
macroscopic traffic flow models
stochastic conservation laws
uncertainty quantification
stochastic parameters
real data
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2020078?viewType=HTML
Description
Summary:The objective of this paper is to analyze the inclusion of one or more random parameters into the deterministic Lighthill-Whitham-Richards traffic flow model and use a semi-intrusive approach to quantify uncertainty propagation. To verify the validity of the method, we test it against real data coming from vehicle embedded GPS systems, provided by Autoroutes Trafic.
ISSN:1551-0018

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