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Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction
Physics-informed neural networks (PINNs) are a newly emerging research frontier in machine learning, which incorporate certain physical laws that govern a given data set, e. g., those described by partial differential equations (PDEs), into the training of the neural network (NN) based on such a data set.
Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications
The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model.
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