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

Enrico Bertino; Régis Duvigneau; Paola Goatin; Enrico Bertino; Régis Duvigneau; Paola Goatin

doi:10.3934/mbe.2020078

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 2: 1511-1533. doi: 10.3934/mbe.2020078

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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

Received: 31 July 2019 Accepted: 24 November 2019 Published: 02 December 2019

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.
- 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[J]. Mathematical Biosciences and Engineering, 2020, 17(2): 1511-1533. doi: 10.3934/mbe.2020078

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Abstract

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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