论文标题
使用生成对抗网络量化交通状态估计的不确定性
Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks
论文作者
论文摘要
本文旨在使用基于生成对抗网络的物理信息深度学习（PIDL）来量化交通状态估计（TSE）的不确定性。焦点的不确定性来自基本图，换句话说，映射从交通密度到速度。量化TSE问题的不确定性是表征预测的交通状态的鲁棒性。自成立以来，生成的对抗网络（GAN）已成为流行的概率机器学习框架。在本文中，我们将使用随机交通流模型为基于GAN的预测提供了基于GAN的预测，并为TSE的基于GAN的PIDL框架，称为``Physgan-tse''。通过在现实世界数据集上进行实验，下一代模拟（NGSIM）数据集（NGSIM）数据集，该方法比不确定的量化模型更强大地量化了纯gan模型。比较Lighthill-Whitham-Richards（LWR）和AW-Rascle-Zhang（ARZ）模型，被比较为Physgan的物理成分，结果表明，基于ARZ的Physgan的性能比基于LWR的Physio更好。
This paper aims to quantify uncertainty in traffic state estimation (TSE) using the generative adversarial network based physics-informed deep learning (PIDL). The uncertainty of the focus arises from fundamental diagrams, in other words, the mapping from traffic density to velocity. To quantify uncertainty for the TSE problem is to characterize the robustness of predicted traffic states. Since its inception, generative adversarial networks (GAN) have become a popular probabilistic machine learning framework. In this paper, we will inform the GAN based predictions using stochastic traffic flow models and develop a GAN based PIDL framework for TSE, named ``PhysGAN-TSE". By conducting experiments on a real-world dataset, the Next Generation SIMulation (NGSIM) dataset, this method is shown to be more robust for uncertainty quantification than the pure GAN model or pure traffic flow models. Two physics models, the Lighthill-Whitham-Richards (LWR) and the Aw-Rascle-Zhang (ARZ) models, are compared as the physics components for the PhysGAN, and results show that the ARZ-based PhysGAN achieves a better performance than the LWR-based one.
