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Markov chain Monte Carlo for a hyperbolic Bayesian inverse problem in traffic flow modeling

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Markov chain Monte Carlo for a hyperbolic Bayesian inverse problem in traffic flow modeling. / Coullon, J.; Pokern, Y.
In: Data-Centric Engineering, Vol. 3, e4, 31.03.2022.

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Coullon J, Pokern Y. Markov chain Monte Carlo for a hyperbolic Bayesian inverse problem in traffic flow modeling. Data-Centric Engineering. 2022 Mar 31;3:e4. Epub 2022 Feb 22. doi: 10.1017/dce.2022.3

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@article{0caf9443f9204ae0beea8c1e82c69fdd,
title = "Markov chain Monte Carlo for a hyperbolic Bayesian inverse problem in traffic flow modeling",
abstract = "As a Bayesian approach to fitting motorway traffic flow models remains rare in the literature, we empirically explore the sampling challenges this approach offers which have to do with the strong correlations and multimodality of the posterior distribution. In particular, we provide a unified statistical model to estimate using motorway data both boundary conditions and fundamental diagram parameters in a motorway traffic flow model due to Lighthill, Whitham, and Richards known as LWR. This allows us to provide a traffic flow density estimation method that is shown to be superior to two methods found in the traffic flow literature. To sample from this challenging posterior distribution, we use a state-of-the-art gradient-free function space sampler augmented with parallel tempering.",
keywords = "Bayesian inverse problem, MCMC, motorway traffic flow, traffic engineering, uncertainty quantification",
author = "J. Coullon and Y. Pokern",
year = "2022",
month = mar,
day = "31",
doi = "10.1017/dce.2022.3",
language = "English",
volume = "3",
journal = "Data-Centric Engineering",

}

RIS

TY - JOUR

T1 - Markov chain Monte Carlo for a hyperbolic Bayesian inverse problem in traffic flow modeling

AU - Coullon, J.

AU - Pokern, Y.

PY - 2022/3/31

Y1 - 2022/3/31

N2 - As a Bayesian approach to fitting motorway traffic flow models remains rare in the literature, we empirically explore the sampling challenges this approach offers which have to do with the strong correlations and multimodality of the posterior distribution. In particular, we provide a unified statistical model to estimate using motorway data both boundary conditions and fundamental diagram parameters in a motorway traffic flow model due to Lighthill, Whitham, and Richards known as LWR. This allows us to provide a traffic flow density estimation method that is shown to be superior to two methods found in the traffic flow literature. To sample from this challenging posterior distribution, we use a state-of-the-art gradient-free function space sampler augmented with parallel tempering.

AB - As a Bayesian approach to fitting motorway traffic flow models remains rare in the literature, we empirically explore the sampling challenges this approach offers which have to do with the strong correlations and multimodality of the posterior distribution. In particular, we provide a unified statistical model to estimate using motorway data both boundary conditions and fundamental diagram parameters in a motorway traffic flow model due to Lighthill, Whitham, and Richards known as LWR. This allows us to provide a traffic flow density estimation method that is shown to be superior to two methods found in the traffic flow literature. To sample from this challenging posterior distribution, we use a state-of-the-art gradient-free function space sampler augmented with parallel tempering.

KW - Bayesian inverse problem

KW - MCMC

KW - motorway traffic flow

KW - traffic engineering

KW - uncertainty quantification

U2 - 10.1017/dce.2022.3

DO - 10.1017/dce.2022.3

M3 - Journal article

VL - 3

JO - Data-Centric Engineering

JF - Data-Centric Engineering

M1 - e4

ER -