ADD-PINN adaptively decomposes the spatial domain based on PINN residuals and a shock indicator to improve offline traffic state estimation under the LWR model, outperforming baselines in most sparse-sensor cases while training faster.
Transportation Research Part B: Methodological 39, 187–196
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Mathematical analysis based on the Macroscopic Fundamental Diagram proves road transportation networks are fragile, with a skewness indicator for cross-network comparison and simulations showing stochastic reinforcement.
citing papers explorer
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Adaptive Domain Decomposition Physics-Informed Neural Networks for Traffic State Estimation with Sparse Sensor Data
ADD-PINN adaptively decomposes the spatial domain based on PINN residuals and a shock indicator to improve offline traffic state estimation under the LWR model, outperforming baselines in most sparse-sensor cases while training faster.
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The fragile nature of road transportation networks
Mathematical analysis based on the Macroscopic Fundamental Diagram proves road transportation networks are fragile, with a skewness indicator for cross-network comparison and simulations showing stochastic reinforcement.