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Discover Physically Analyzable Governing Nonlinearities of Traffic Network Flow Dynamics

https://doi.org/10.2139/ssrn.4760715
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Data-driven analysis and forecasting of highway traffic dynamics
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Physics-informed machine learning
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k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
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Hamiltonian Systems and Transformation in Hilbert Space
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The general problem of the stability of motion
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Discovering governing equations from partial measurements with deep delay autoencoders
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Data driven exploration of traffic network system dynamics using high resolution probe data
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Physics Informed Deep Learning for Traffic State Estimation
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Estimating motorway traffic states with data fusion and physics-informed deep learning
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Physics-Informed Deep Learning for Traffic State Estimation: Illustrations With LWR and CTM Models
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Adaptive Multi-Kernel SVM With Spatial–Temporal Correlation for Short-Term Traffic Flow Prediction
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Network-Wide Traffic Flow Dynamics Prediction Leveraging Macroscopic Traffic Flow Model and Deep Neural Networks
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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges
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Traffic Flow Prediction via Spatial Temporal Graph Neural Network
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Characterizing Traffic Conditions in Urban Areas
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Dynamic User Equilibrium Departure Time and Route Choice on Idealized Traffic Arterials
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Urban Traffic Speeds—I: A Comparison of Proposed Expressions Relating Journey Speed to Distance from a Town Center
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Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control
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Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
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Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models
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Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
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An Evaluation of Two Proposals for Traffic Restraint in Central London
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