A Physics-Informed Deep Learning Paradigm for Car-Following Models

This research introduces a novel physics-informed deep learning paradigm designed for developing car-following models. The approach aims to integrate fundamental physical principles directly into deep neural networks to enhance the accuracy and interpretability of traffic simulations.