A Self-Organized Criticality Model of Extreme Events and Cascading Disasters of Hub and Spoke Air Traffic Networks

TL;DR

This paper applies Self-Organized Criticality theory to model cascading failures in hub-and-spoke air traffic networks, revealing their vulnerability to extreme weather and systemic disruptions.

Contribution

It introduces a novel SOC-based model for analyzing and predicting cascading failures in critical infrastructure networks like U.S. airline systems.

Findings

The SOC model captures power-law distribution of disruptions.

It identifies critical nodes for vulnerability.

The model aids in assessing resilience and planning mitigation strategies.

Abstract

Critical infrastructure networks--including transportation, power grids, and communication systems--exhibit complex interdependencies that can lead to cascading failures with catastrophic consequences. These disasters often originate from failures at critical points in the network, where single-node disruptions can propagate rapidly due to structural dependencies and high-impact linkages. Such vulnerabilities are exacerbated in systems that have been highly optimized for efficiency or have self-organized into fragile configurations over time. The U.S. air transportation system, built on a hub-and-spoke model, exemplifies this type of critical infrastructure. Its reliance on a small number of high-throughput hubs means that even localized disruptions--especially those triggered by increasingly frequent and extreme weather events--can initiate cascades with nationwide impact. We introduce a novel application of Self-Organized Criticality (SOC) theory to model and analyze cascading failures in such systems. Through a detailed case study of U.S. airline operations, we show how the SOC model captures the power-law distribution of disruptions and the long-tail risk of systemic failures, reflecting the interplay between structural fragility and climate shocks. Our approach enables quantitative assessment of network vulnerability, identification of critical nodes, and evaluation of proactive strategies for disaster risk reduction. The results demonstrate that the SOC model replicates the observed statistical patterns--frequent small events and rare, severe failures--offering a powerful systems-level framework for infrastructure resilience planning and emergency response.