交通流建模中双曲贝叶斯反问题的MCMC -附录

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2020-01-07 DOI:10.1017/dce.2022.9
Jeremie Coullon, Y. Pokern
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引用次数: 1

摘要

由于双曲贝叶斯反问题的研究在文献中仍然很少,我们从经验上探讨了这些问题所带来的采样挑战,这些挑战与PDE解决方案中的冲击形成有关。此外,我们提供了一个统一的统计模型,用于使用LWR中的高速公路数据、边界条件和基本图参数进行估计,LWR是一个众所周知的高速公路交通流模型。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。最后,我们强调了\emph{Population Parallel Tempering}是一种可扩展的方法,可以将采样器的混合速度提高10倍。
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MCMC for a hyperbolic Bayesian inverse problem in traffic flow modelling – ADDENDUM
As work on hyperbolic Bayesian inverse problems remains rare in the literature, we explore empirically the sampling challenges these offer which have to do with shock formation in the solution of the PDE. Furthermore, we provide a unified statistical model to estimate using motorway data both boundary conditions and fundamental diagram parameters in LWR, a well known motorway traffic flow model. 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. Finally, we highlight how \emph{Population Parallel Tempering} - a modification of Parallel Tempering - is a scalable method that can increase the mixing speed of the sampler by a factor of 10.
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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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