Assessment and optimization of airport operational resilience under severe weather conditions: A case study of snowstorm

This study aims to establish a scientific basis for airport operational management under severe weather conditions, thereby enhancing resilience and response capabilities. To assess airport operational systems’ performance and resilience, the researchers developed a comprehensive performance-indicator model based on the operational status of arrival and departure flights. To address the limitations of conventional departure-rate metrics, the authors introduced the actual departure rate (ADR) alongside a resilience assessment model, and a clustering algorithm. The data analyzed consisted of operational and meteorological data from Wuhan Tianhe International Airport (WUH), China, during snowstorms.

The results demonstrated that ADR more accurately represented airport performance than conventional metrics and that resilience assessments based on ADR more closely reflected operational reality. Moreover the clustering algorithm, tested on data collected from 11 snowstorm-impacted airports, demonstrated superior clustering performance. Their results gave hourly departure optimization strategies for WUH, which were shown to improve resilience by 0.13 − 3.18 on the first day of the snowstorm and by 0.026 − 0.73 on the second. Beyond WUH, the framework demonstrated strong applicability at Chengdu Shuangliu and Chengdu Tianfu airports and exhibited high robustness at four U.S. airports.