SR-Traffic: Discovering Macroscopic Traffic Flow Models with Symbolic Regression

Traffic flows are complex systems that can be studied from a macroscopic perspective. In particular, first-order models are tractable but oversimplified, while higher-order models capture richer dynamics at the cost of complexity. Here, we introduce SR-Traffic, a data-driven, physics-informed framework that uses symbolic regression to learn effective phenomenological relations directly from experimental data while embedding them into an efficient, first-order PDE formulation. Our approach balances accuracy and interpretability, ensures physical consistency, and shows good generalization, overcoming the limitations of purely data-driven models. Overall, our findings could support the design of digital solutions for improving mobility systems.