论文标题
用于设计和控制运输网络的基于高斯细分市场的交通流模型
A Gaussian segment-based traffic flow model for the design and control of transport networks
论文作者
论文摘要
在最近开发的细胞随机交通流模型的设置中，它表明，可以通过高斯工艺准确地近似关节每电池媒介物密度，该过程具有具有有吸引力的特征，其平均值和（空间和时间）协方差可以有效地进行评估。本文在道路交通管制和运输网络设计的背景下展示了这种方法的丰富潜力。为了牢固地提供用于使用多元高斯近似值的经验支持，我们依靠包含流量流数据的详细历史数据集。然后，在本文的其余部分中，我们提供了一系列设计和控制相关的示例问题，可以使用高斯方法进行分析。这些涵盖以下主题：（i）评估固定性能度量，（ii）路线选择，（iii）控制交通流量以及（iv）具有任意拓扑的交通网络的性能。在讨论这些示例的设置，结果和应用时，我们在确定性的对应物上强调了{随机}交通模型的适当性。
In the setting of a recently developed cellular stochastic traffic flow model, it has shown that the joint per-cell vehicle densities, as a function of time, can be accurately approximated by a Gaussian process, which has the attractive feature that its means and (spatial and temporal) covariances can be efficiently evaluated. The present article demonstrates the rich potential of this methodology in the context of road traffic control and transportation network design. To solidly provide empirical backing for the use of a multivariate Gaussian approximation, we rely on a detailed historical dataset that contains traffic flow data. Then, in the remainder of the paper, we provide a sequence of design and control related example questions that can be analyzed using the Gaussian methodology. These cover the following topics: (i) evaluation of stationary performance measures, (ii) route selection, (iii) control of traffic flows, and (iv) performance of traffic networks with arbitrary topology. In discussing the setup, results, and applications of these examples, we stress the appropriateness of our {stochastic} traffic model over a deterministic counterpart.
