The resilience and vulnerability of airport networks are significant challenges during the COVID-19 global pandemic. Previous studies considered node failure of networks under natural disasters and extreme weather. Herein, we propose a complex network methodology combined with data-driven to assess the resilience of airport networks toward global-scale disturbance using the Chinese airport network (CAN) and the European airport network (EAN) as a case study. The assessment framework includes vulnerability and resilience analyses from the network- and node-level perspectives. Subsequently, we apply the framework to analyze the airport networks in China and Europe. Specifically, real air traffic data for 232 airports in China and 82 airports in Europe are selected to form the CAN and EAN, respectively. The complex network analysis reveals that the CAN and the EAN are scale-free small-world networks, that are resilient to random attacks. However, the connectivity and vulnerability of the CAN are inferior to those of the EAN. In addition, we select the passenger throughput from the top-50 airports in China and Europe to perform a comparative analysis. By comparing the resilience evaluation of individual airports, we discovered that the factors of resilience assessment of an airport network for global disturbance considers the network metrics and the effect of government policy in actual operations. Additionally, this study also proves that a country’s emergency response-ability towards the COVID-19 has a significantly affectes the recovery of its airport network.

The COVID-19 global pandemic hit the aviation industry hard since the end of 2019. It has had an immediate, dramatic impact on airport traffic and revenue. Airports are the important nodes in the aviation system network, and the failure of a single airport can often affect the surrounding airports. The purpose of our analysis is to show how is the resilience and recovery of airports in the global public health crisis. Much research on resilience can be found in air transportation networks facing natural hazards or extreme weather, which focus on the robustness of the airport network. These methods are not suitable for the global public health event. Therefore, based on the collection of existing data, we combined with existing resilience measurement methods to analyze the resilience and recovery of airports during the global public health crisis. The resilience metrics results reflect the recovery of airports very well under different strategies. Here, we analyze airport network resilience by considering the performance-based methods. We integrate some metrics such as aircraft movements, passenger throughput, and freight throughput in the resilience metrics model, comparing the resilience evaluation under different preventive and control strategies, which can reflect the airport’s recovery speed during the COVID-19 pandemic. Our analysis indicates that the aviation system network deteriorates soon after the COVID-19 outbreak, but the recovery level of the aviation industry depends on what measures are taken to prevent and control the COVID-19 epidemic. In particular, the recovery of the aviation system network in Europe takes longer than in China, due to different prevention and control strategies for COVID-19. The study proves that the emergency response ability of the country for the public health crisis has a significant positive impact on speeding up the recovery of the aviation system.

As the COVID-19 pandemic fades, the aviation industry is entering a fast recovery period. To analyze airport networks’ post-pandemic resilience during the recovery process, this paper proposes a Comprehensive Resilience Assessment (CRA) model approach using the airport networks of China, Europe, and the U.S.A as case studies. The impact of COVID-19 on the networks is analyzed after populating the models of these networks with real air traffic data. The results suggest that the pandemic has caused damage to all three networks, although the damages to the network structures of Europe and the U.S.A are more severe than the damage in China. The analysis suggests that China, as the airport network with less network performance change, has a more stable level of resilience. The analysis also shows that the different levels of stringency policy in prevention and control measures during the epidemic directly affected the recovery rate of the network. This paper provides new insights into the impact of the pandemic on airport network resilience.

Modern airports, as pivotal nodes in global transportation networks, face increasing resilience challenges from compound threats such as extreme weather events and cyberattacks. However, current assessment methods primarily rely on subjective evaluations and lack probabilistic reasoning to account for the dynamic interdependencies among resilience factors. To address this gap, this study presents a hybrid Bayesian network–best worst method (BN–BWM) framework aimed at improving the accuracy and practicality of airport system resilience assessments. While Bayesian networks are effective for modeling complex probabilistic dependencies, expert-based probability assignments often introduce subjectivity. To mitigate this, we apply the best worst method (BWM) to conduct systematic pairwise comparison. Building on this, we leverage the BWM’s systematic pairwise comparisons, conducted with 10 aviation experts, to generate conditional probability tables for the Bayesian network. The results indicate that large airports demonstrate higher resilience levels (84–85%), whereas medium-sized airports exhibit moderate resilience (79%). Sensitivity analysis identifies key factors influencing resilience, including emergency repair systems and personnel capabilities, thereby offering actionable insights into improving airport operations. This study presents a robust, data-driven framework that enhances the objectivity and accuracy of resilience evaluations, providing theoretical support for sustainable airport management and operational safety.

Flight delays during extreme weather events exhibit spatio-temporal propagation and cascading effects, posing serious challenges to the resilience of aviation systems. Existing prediction approaches often neglect dynamic dependencies across flight chains and struggle to model sparse extreme events. This study develops a data-driven framework that explicitly models delay propagation paths, incorporates historical scenario retrieval to capture rare disruption patterns, and integrates meteorological, airport operational, and flight-specific information through multi-source fusion. Using U.S. flight operations and weather records, the framework demonstrates clear advantages over established baselines in extreme-delay scenarios, achieving a MAE of 3.23 min, an RMSE of 6.25 min, and an R² of 0.92—improving by 8.8%, 26.0%, and 5.75% compared to the best benchmark. Ablation studies confirm the contribution of the propagation modeling, historical retrieval, and multi-source integration modules, while cross-airport evaluations reveal consistent accuracy at both major hubs (e.g., Atlanta, Chicago O’Hare) and regional airports (e.g., Kona, Anchorage). These findings demonstrate that the proposed framework enables reliable forecasting of delay propagation under complex weather conditions, providing valuable support for proactive departure management and enhancing the resilience of aviation operations.