no code implementations • 3 Mar 2023 • Xuan Di, Rongye Shi, Zhaobin Mo, Yongjie Fu
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNN), has been booming in science and engineering fields.
no code implementations • 19 Jun 2022 • 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).
1 code implementation • 19 Jun 2022 • Zhaobin Mo, Yongjie Fu, Daran Xu, Xuan Di
TrafficFlowGAN adopts a normalizing flow model as the generator to explicitly estimate the data likelihood.
no code implementations • 21 Apr 2021 • Mobin Zhao, Wangzhi Li, Yongjie Fu, Kangrui Ruan, Xuan Di
A case study is performed on a 2-by-2 road network located in State College, Pennsylvania, USA, to further demonstrate the effectiveness of the proposed algorithm under real-world scenarios.
no code implementations • 22 Nov 2020 • Zhenyu Shou, Xu Chen, Yongjie Fu, Xuan Di
We show that the routing behavior of intelligent agents is shown to converge to the classical notion of predictive dynamic user equilibrium (DUE) when traffic environments are simulated using dynamic loading models (DNL).