Differentiable Stochastic Traffic Dynamics: Physics-Informed Generative Modelling in Transportation

ArXi:2603.09174v1 Announce Type: cross Macroscopic traffic flow is stochastic, but the physics-informed deep learning methods currently used in transportation literature embed deterministic PDEs and produce point-valued outputs; the stochasticity of the governing dynamics plays no role in the learned representation. This work develops a framework in which the physics constraint itself is distributional and directly derived from stochastic traffic-flow dynamics.