As a Bayesian approach to fitting motorway traffic flow models remains rare in the literature, we explore empirically the sampling challenges this approach offers which have to do with the strong correlations and multi-modality 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 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. To sample from this challenging posterior distribution we use a state-of-the-art gradient-free function space sampler augmented with parallel tempering.

翻译：由于在文献中仍很少采用贝叶斯方法来安装高速公路交通流量模型,我们从经验上探讨这一方法所提供的抽样挑战,这些挑战与后方分布的紧密关联性和多模式有关,特别是,我们提供了一个统一的统计模型,用高速公路数据来估计边界条件和路边交通流量模型这一众所周知的公路交通流量模型的基本图表参数,从而使我们能够提供一种交通流量密度估计方法,该方法比交通流量文献中发现的两个方法要高。为了从这一具有挑战性的后方分布中抽样,我们使用一个先进的无梯度功能空间取样器,该取样器将配以平行的缓冲。