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Found 5 papers, 1 papers with code
Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
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.
Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks
This paper aims to quantify uncertainty in traffic state estimation (TSE) using the generative adversarial network based physics-informed deep learning (PIDL).
TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification
TrafficFlowGAN adopts a normalizing flow model as the generator to explicitly estimate the data likelihood.
CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles
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.
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment
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).
