Markov chain Monte Carlo for a hyperbolic Bayesian inverse problem in traffic flow modeling
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.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/26326736
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Authors:
- Coullon, J
- Pokern, Y
- Publication Date: 2022
Language
- English
Media Info
- Pagination: Article ID e4
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Serial:
- Data-Centric Engineering
- Volume: 3
- ISSN: 2632-6736
Subject/Index Terms
- TRT Terms: Freeways; Statistical analysis; Statistical sampling; Traffic density; Traffic flow
- ATRI Terms: Bayesian; Freeway; Sampling; Statistical analysis; Traffic concentration; Traffic flow
- Subject Areas: Data and Information Technology;
Filing Info
- Accession Number: 01838389
- Record Type: Publication
- Source Agency: ARRB Group Limited
- Files: ATRI
- Created Date: Mar 10 2022 8:32AM