Physics-informed deep learning (PIDL) offers a powerful new approach to traffic state estimation (TSE), combining the strengths of physics-based models with the flexibility of deep learning. This hybrid approach leverages the rich theoretical foundation of transportation modeling while harnessing the power of data to improve accuracy and robustness, particularly in scenarios with limited data.. The interplay between these components allows to overcome limitations inherent in purely physics-based or data-driven approaches. Conversely, data-driven methods like neural networks can learn intricate patterns but require vast amounts of data for effective training. This coffeesode deep dives into this topic & provides a holistic view of this interplay

Reference-
Di, X.; Shi, R.; Mo, Z.; Fu, Y. Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook. Algorithms 2023, 16, 305. https://doi.org/10.3390/a16060305

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