A self-organized criticality model of extreme events and cascading disasters of hub-and-spoke air traffic networks

Critical infrastructure networks, such as transportation, power grids, and communication systems, exhibit complex interdependencies that can lead to cascading failures with catastrophic consequences. These cascaded 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 air transportation system in the United States, built on a hub-and-spoke model, exemplifies this type of critical infrastructure. Its reliance on a limited number of high-throughput hubs means that even localized disruptions—particularly those triggered by increasingly frequent and extreme weather events—can initiate cascades with nationwide impacts. We introduce a novel application of the theory of Self-Organized Criticality (SOC) to model and analyze cascading failures in such networks. Through a detailed case study of U.S. airline operations, we show how the SOC model exhibits the power-law distribution of disruptions and the long-tail risk of systemic failures, reflecting the real-world interplay between structural fragility and external shocks. Our approach enables quantitative assessment of network vulnerability, identification of critical nodes, and evaluation of proactive intervention strategies for disaster risk reduction. The results demonstrate that the SOC model successfully replicates the observed statistical patterns of disruption sizes—characterized by frequent small events and rare but severe cascading failures—offering a powerful systems-level framework for infrastructure resilience planning and emergency management. The model provides practitioners with actionable insights for anticipating and mitigating systemic risks in complex, interdependent systems.