201 citations · 922 across the 22 of their papers we have counts for
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cs.LG2022
Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks
Zhaobin Mo, Yongjie Fu, Xuan Di
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 t…
cs.LG2022★ 2 cited
TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification
Zhaobin Mo, Yongjie Fu, Daran Xu +1
This paper proposes the TrafficFlowGAN, a physics-informed flow based generative adversarial network (GAN), for uncertainty quantification (UQ) of dynamical systems. TrafficFlowGAN…
eess.SY2022★ 7 cited
A Unified Network Equilibrium for E-Hailing Platform Operation and Customer Mode Choice
Xu Chen, Xuan Di
This paper aims to combine both economic and network user equilibrium for ride-sourcing and ride-pooling services, while endogenously optimizing the pooling sequence of two origin-…