From Microscopic Interactions to Macroscopic Feedback: Real-Time Traffic Control via Neural Operators

Real-time multi-scale traffic control requires capturing how microscopic vehicle interactions give rise to macroscopic flow dynamics and vice versa, yet existing methods either rely on computationally expensive PDE solvers or operate at the agent level without global awareness. This paper introduces a physics-informed surrogate model which aims at approximating the solution of the Aw-Rascle-Zhang equations by means of a neural operator capable of predicting macroscopic traffic evolution in real-time. The proposed approach integrates macroscopic field learning with microscopic feedback control, enabling a unified multi-scale framework in which a learned operator informs platoon behavior while respecting physical constraints. This framework embeds physics-based regularization and microscopic-macroscopic coupling within both the learning and control loops. The numerical results across dense and highdensity traffic scenarios show that the surrogate model is able to preserve the essential structure of traffic waves, maintain coherence with agent-level dynamics, and support string stable platoon responses during transient disturbances. These results demonstrate that physics-informed neural operators offer a computationally efficient alternative to classical PDE solvers for cooperative real-time intelligent transportation systems.