Scale-free congestion clusters in large-scale traffic networks: a continuum modeling study

Recent empirical studies have reported that spatiotemporal congestion clusters in urban traffic exhibit scale-free statistics, with cluster size following a power-law distribution.

In this study, we address whether macroscopic continuum descriptions of traffic flow are capable of generating such scale-free spatiotemporal congestion patterns.

To this end, we analyze the second-order Aw-Rascle-Zhang model on directed networks under junction coupling.

The governing equations are solved by a high-order discontinuous Galerkin scheme, and junction fluxes are determined by an optimization-based coupling procedure enforcing conservation and admissibility at intersections.

Congestion is defined by thresholding the road-averaged density, and spatiotemporal clusters are extracted as connected components in space and time.

Numerical experiments on lattice networks of varying sizes reveal that the cluster size follows a robust power-law distribution.

Moreover, when rescaled by the linear system size inherent to the two-dimensional network geometry, the distribution collapses onto an approximately universal curve, indicating finite-size scaling governed by the linear system size.

The observed power-law statistics and finite-size scaling are reminiscent of scale-invariant dynamics characteristic of self-organized criticality.

These results demonstrate that macroscopic continuum traffic models can reproduce large-scale statistical features observed in real urban congestion dynamics.