论文标题
MCMC用于交通流建模中的双曲线贝叶斯逆问题
MCMC for a hyperbolic Bayesian inverse problem in traffic flow modelling
论文作者
论文摘要
在文献中，由于拟合高速公路交通流量模型的贝叶斯方法仍然很少见，因此我们从经验上探索了这种方法所提供的采样挑战，这些挑战与后验分布的强相关性和多模式有关。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和众所周知的高速公路交通流模型LWR中的高速公路条件和基本图参数进行估算。这使我们能够提供一种交通流量密度估计方法，该方法比流量流文献中发现的两种方法优越。为了从这个具有挑战性的后验分布中采样，我们使用并行回火增强的最先进的无梯度函数空间采样器。
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.
