In response to the challenges associated with forecasting the trajectory of flight delay propagation, pinpointing pivotal nodes within the route network, and the substantial costs involved in enhancing operational efficiency, this study introduces an innovative approach to identifying critical nodes that influence delay propagation across route networks. The methodology commences by establishing a route network model for East China, leveraging the principles of complex network theory. It then incorporates the SEIR (Susceptible-Exposed-Infected-Recovered) model, typically used for analyzing the dynamics of infectious disease spread, to examine the propagation of delays between routes. Subsequently, the approach employs a GA to identify key nodes, which are then compared against those identified by network topology indices. The simulation outcomes demonstrate that the GA’s identification of key nodes offers superior insights into the overall network’s susceptibility to infection, thereby presenting operational managers with novel perspectives for analyzing the spread of flight delays.

An analysis of air traffic complexity in multi-airport terminal areas can assist air traffic controllers in accurately assessing the air traffic situation and collaboratively managing air traffic flows, thereby enhancing the utilization of airspace resources and reducing flight delays. This paper proposes an air traffic complexity analysis method for multi-airport terminal areas based on a route segment–flight state interdependent network. The interdependent network model consists of an upper-layer flight state network, a lower-layer route segment network, and inter-layer coupling edges. The upper-layer network is constructed with aircraft as nodes and flight conflicts between aircraft as edges. The lower-layer network uses route segments as nodes and the connectivity between route segments as edges. The inter-layer coupling edges are determined by evaluating the relationship between aircraft and route segments—if an aircraft is on a specific route segment, a coupling edge exists between the corresponding aircraft node and route segment node. Based on this model, node-level complexity metrics are established to analyze the importance and complexity of individual route segments. Additionally, network-level complexity metrics are introduced to assess the overall air traffic complexity in multi-airport terminal areas. Finally, the method proposed in this paper is validated using flight scenarios in the Guangdong–Hong Kong–Macao Greater Bay Area. By comparing and analyzing the results with the actual situation, it is shown that the proposed method can accurately assess the air traffic complexity in multi-airport terminal areas.

Aiming at the characterized networking and clustering of flight conflicts in high-density airspace under free-flight mode, this paper proposes a network-based conflict resolution method that integrates complex network theory and an improved particle swarm optimization (PSO) algorithm. First, a tunable Mixed Indicator (MI) is formulated by combining the Weighted Cycle Ratio (WCR) and node strength (NS) to accurately identify critical nodes within the flight conflict network. Subsequently, based on the circle structure, a Circle Complexity (CC) metric is proposed as a network structural indicator. Furthermore, the Analytic Hierarchy Process (AHP) is employed to incorporate network efficiency, the number of conflicting nodes, and robustness into a comprehensive performance evaluation index. On this basis, the Leader–Follower Particle Swarm Optimization (LDPSO) algorithm is applied to collaboratively optimize the discrete codes representing heading, speed, and composite resolution strategies, thereby achieving efficient resolution for multi-aircraft conflicts. Simulation experiments demonstrate that the proposed MI significantly outperforms traditional centrality metrics in identifying critical nodes. The LDPSO algorithm proves superior to comparative algorithms, including the standard PSO, Genetic Algorithm (GA), and Atomic Orbital Optimization (AOO), in terms of conflict resolution rate, convergence speed, and solution quality. It can achieve the rapid disintegration of large-scale conflict networks with relatively low computational cost, providing a feasible solution for real-time conflict resolution in dynamic airspace.