traffic-flow

HypNO introduces a graph-based neural operator that uses physics-informed message passing on a space-time finite-volume cell graph to solve scalar hyperbolic conservation laws, accurately capturing shocks and discontinuities. The method is benchmarked on LWR and ARZ traffic-flow models.

This paper proposes a semiparametric framework for stochastic fundamental diagram modeling that combines physically-constrained functional forms with neural network structures to capture complex traffic flow patterns and uncertainty, demonstrating superior performance on real-world datasets.