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

1 Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
2 Inria Sophia Antipolis - Méditerranée, Université Côte d’Azur, Inria, CNRS, LJAD, 2004 route des Lucioles - BP 93, 06902 Sophia Antipolis Cedex, France

Special Issues: Mathematical Modeling with Measures

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.
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Keywords macroscopic traffic flow models; stochastic conservation laws; uncertainty quantification; stochastic parameters; real data

Citation: Enrico Bertino, Régis Duvigneau, Paola Goatin. Uncertainty quantification in a macroscopic traffic flow model calibrated on GPS data. Mathematical Biosciences and Engineering, 2020, 17(2): 1511-1533. doi: 10.3934/mbe.2020078


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