A Vulnerability-Resilience-Criticality Framework for Airport Cluster Airspace Congestion Identification

Abstract

With the rapid growth of air traffic demand, congestion in multi-airport airspace has become a critical challenge affecting operational efficiency and flight safety. This study proposes an airspace congestion identification framework based on a vulnerability–resilience–criticality (VRC) indicator system for airport cluster airspace. The proposed VRC framework establishes a multi-dimensional representation of airspace operational characteristics by considering disturbance response, system recovery capability, and congestion evolution dynamics, thereby providing a more comprehensive description of congestion formation mechanisms. Based on this indicator system, Random Forest is employed for representative indicator screening, while entropy weighting and the Fuzzy C-Means (FCM) algorithm are integrated to quantify indicator contributions and identify four airspace operational states: smooth, normal, slightly congested, and congested. A case study of the Pearl River Delta airport cluster is conducted, and the transferability of the proposed framework is further validated using Jiangsu regional data. Results demonstrate that the proposed VRC-based framework can effectively distinguish different congestion states and exhibits good transferability across different regional operational environments, providing quantitative support for airspace resource allocation and air traffic management decision-making.