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Found 6 papers, 1 papers with code
Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems
In this framework, an operator is trained to map heterogeneous and sparse traffic input data to the complete macroscopic traffic state in a supervised learning setting.
Learning-based solutions to nonlinear hyperbolic PDEs: Empirical insights on generalization errors
We empirically quantify the generalization/out-of-sample error of the $\pi$-FNO solver as a function of input complexity, i. e., the distributions of initial and boundary conditions.
Learning Traffic Speed Dynamics from Visualizations
We present a deep learning method to learn the macroscopic traffic speed dynamics from these space-time visualizations, and demonstrate its application in the framework of traffic state estimation.
Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimation
The results demonstrate that anisotropic kernels significantly reduce model complexity and model over-fitting, and improve the physical correctness of the estimated speed fields.
Traffic Data Imputation using Deep Convolutional Neural Networks
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information.
Learning Traffic Flow Dynamics using Random Fields
This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories.
