As the demand for air transportation surges, issues like flight conflicts and air-route congestion within multi-airport terminal areas have grown progressively more serious. Analyzing the evolution of air traffic situations in these areas can effectively enhance the air traffic’s early-warning capability, reduce flight conflicts, and alleviate air-route congestion. This paper proposes a method for analyzing the evolution of air traffic situations in multi-airport terminal areas based on flight segment–flight state interdependent network. First, a flight segment–flight state interdependent network model is established. This interdependent network model consists of an upper-layer flight state network, a lower-layer air-route network, and coupling edges. The upper-layer network is constructed with aircraft as nodes and flight conflicts between aircraft as edges. The lower-layer network takes air-routes as nodes and the connection relationships between air-routes as edges. The inter-layer coupling edges are determined by judging the relationship between aircraft and air-routes. If an aircraft is on a certain air-route, there exists a coupling edge between the aircraft node and the air-route node. On this basis, by comprehensively considering three network indicators, namely node degree, weighted clustering coefficient, and node strength, the overall air traffic situation value is obtained. Finally, experimental verification and analysis were conducted in an actual flight scenario of a multi-airport terminal area in the Guangdong–Hong Kong–Macao Greater Bay Area. The results show that the proposed method can accurately reflect the air traffic situation. The time-series analysis of the situation evolution reveals that the evolution process has chaotic characteristics.

Accurate assessment and forecasting of air traffic conditions in multi-airport terminal areas are essential for improving early-warning capabilities, mitigating flight conflicts, and alleviating air-route congestion. Accordingly, this study develops an air traffic situation prediction approach that combines an air route–flight state interdependent network with an Optimal Training Sample Online Fuzzy Least-Squares Support Vector Machine (OTSOF-LSSVM). An interdependent network model is first established. The Analytic Hierarchy Process (AHP) is then employed to combine three network indicators, namely node degree, weighted clustering coefficient, and node strength, thereby producing a comprehensive air traffic situation value and its corresponding evolutionary time series. Considering the time-varying and long-periodic properties of this series, an OTSOF-LSSVM-based prediction method is developed. Training samples are selected according to their temporal and spatial proximity to the prediction moment. In addition, block-matrix operations are introduced during model updating to streamline the computational procedure and improve algorithmic efficiency. The proposed approach is validated using actual flight data from the multi-airport terminal area of the Guangdong–Hong Kong–Macao Greater Bay Area. The results demonstrate that the proposed assessment method can effectively characterize the prevailing air traffic situation. Moreover, in comparison with several existing prediction techniques, the proposed method achieves the best overall performance, yielding a mean absolute error of only 0.0111.

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.