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

TL;DR

This study shows that macroscopic continuum traffic models can generate scale-free congestion patterns with power-law distributions, resembling real urban traffic phenomena and indicating self-organized criticality.

Contribution

It demonstrates that second-order continuum traffic models on networks can reproduce scale-free congestion clusters and finite-size scaling observed in empirical urban traffic data.

Findings

Cluster size distribution follows a power-law.

Rescaled distributions collapse onto a universal curve.

Results suggest continuum models can mimic real congestion statistics.

Abstract

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.