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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 otherwords, the mapping from traffic density to velocity. To quantify uncertaintyfor the TSE problem is to characterize the robustness of predicted trafficstates. Since its inception, generative adversarial networks (GAN) have becomea popular probabilistic machine learning framework. In this paper, we willinform the GAN based predictions using stochastic traffic flow models anddevelop a GAN based PIDL framework for TSE, named ``PhysGAN-TSE". By conductingexperiments on a real-world dataset, the Next Generation SIMulation (NGSIM)dataset, this method is shown to be more robust for uncertainty quantificationthan the pure GAN model or pure traffic flow models. Two physics models, theLighthill-Whitham-Richards (LWR) and the Aw-Rascle-Zhang (ARZ) models, arecompared as the physics components for the PhysGAN, and results show that theARZ-based PhysGAN achieves a better performance than the LWR-based one.

Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks