A novel teacher-student ensemble of physics-informed deep learning models improves traffic state estimation under varying speed limit conditions by using a classifier to select appropriate physics-constrained models.
A data fusion approach for real-time traffic state estimation in urban signalized links
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Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario
A novel teacher-student ensemble of physics-informed deep learning models improves traffic state estimation under varying speed limit conditions by using a classifier to select appropriate physics-constrained models.