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

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2022-02-22 DOI:10.1017/dce.2022.3
Jeremie Coullon, Y. Pokern
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引用次数: 1

Abstract

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.
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交通流建模中双曲贝叶斯反问题的马尔可夫链蒙特卡罗
摘要由于拟合高速公路交通流模型的贝叶斯方法在文献中仍然很少见,我们从经验上探讨了这种方法所带来的采样挑战,这与后验分布的强相关性和多模态性有关。特别是,我们提供了一个统一的统计模型,以使用高速公路数据来估计Lighthill、Whitham和Richards(LWR)的高速公路交通流模型中的边界条件和基本图参数。这使我们能够提供一种交通流密度估计方法,该方法被证明优于交通流文献中的两种方法。为了从这种具有挑战性的后验分布中采样,我们使用了一种最先进的无梯度函数空间采样器,并添加了平行回火。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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