Airport slot allocation is a key short-term solution to address airport capacity constraints, and it has long been a focus of research in the field of air traffic management. The existing studies primarily consider constraints such as airport capacity and flight operations, optimizing the slot allocation of arrival and departure flights to maximize the utilization of airport resources. This study proposes an airline fairness index based on a demand-side value system and addresses the problem of flight slot allocation by developing a tri-objective model. The model simultaneously considers the maximum slot deviation, total slot deviation, and airline fairness. Additionally, dynamic capacity constraints using rolling time windows and constraints on slot migration during peak periods are incorporated. The $\epsilon$-constraint method is employed in conjunction with a large-neighborhood search heuristic to solve a two-stage optimization process, yielding an efficient allocation scheme. The experimental results show that the introduction of rolling capacity constraints effectively resolves the issue of continuous overcapacity that arises when only a fixed capacity is considered. Additionally, the proposed airline fairness index, based on a demand-side value system, can significantly improve fairness during the slot allocation process. By sacrificing at most 16% of the total displacement, it is possible to reduce the unfairness index by nearly 80%.

Complex networks have encouraged scholars to develop an effective method for abstracting and optimizing aviation networks. However, researchers often overlook the aviation network’s temporal attribute and treat it as a static network. Aviation networks have strong temporal characteristics and the dynamic connection cannot be realistically described by a static network. It is necessary to more accurately and realistically represent these connections during the operation of an aviation network. This study explored temporal structures of the Chinese aviation temporal network (CATN) based on flight schedules and actual operational time data. Temporal networks based on time windows were represented to analyze the temporal topology features and robustness of the CATN. The results demonstrated the following: (1) based on the spatial-temporal aviation network, there is a morning departure peak (7:00–8:00) and an evening arrival peak at the airline hub (20:00–21:00); (2) examining the centrality of each airport in the CATN at various time intervals exposed fluctuations in their rankings, which could not be identified by a static network, and (3) the robustness of the CATN was found to be unaffected by time windows, but it displayed poor resilience against deliberate attacks, particularly when subjected to betweenness and closeness attacks, which target the network’s shortest paths. For obtaining a greater understanding of the operating situation of civil aviation, displaying the topological features and robustness of the temporal network is of great importance.

This paper presents an integrated approach for terminal airspace management which is limited by route topology and sector capacity constraints to improve arrival and departure operations. The study evaluates various arrival route topologies, including standard terminal arrival route, point merge system, vector for space, and trombone route, while departures are operated via standard instrument departure. A conflict detection and resolution mechanism is implemented for waypoint, link, and mixed-mode runway conflicts. A maximum capacity of the number of simultaneous flights within each approach sector is predefined, and sector congestion is monitored by precise entry/exit time tracking. An optimization framework is used to adjust speed, entry times, departure times and routing to minimize sector congestion, flight delays and extra flight distances. A tailored selective simulated annealing heuristic with local search is proposed to solve the corresponding problem. Computational experiments at Urumqi Tianshan International Airport show that the mixture of all topologies achieves the lowest average entry delay in terminal airspace (1.3 minutes), while standard and vectoring routes yield the lowest average landing delays (1.7 minutes). Point merge and trombone routes increase extra flight distances, while the mixture of three topologies achieves reasonable extra flight distances primarily through vectoring. Under nominal sector capacity, the mixture of three strategies continues to yield a lower average entry delay in terminal airspace. When sector capacity is severely restricted, the use of standard routes results in reduced in-sector flight times, though it requires significant deviations from scheduled time slots.

Air traffic controllers benefit from referencing historical dates with similar complex air traffic conditions to identify potential management measures and their effects, which is critical for understanding air transportation system laws and optimizing decisions. This study conducted data mining using flight timetables. It first explored airport congestion mechanisms and quantified congestion as time series, then proposed a higher-order cumulants based time series feature extraction method. This method was fused with other features to build a high-dimensional airport congestion feature vector, and finally K-means clustering was applied to extract and analyze congestion patterns. The clustering method was empirically validated with 2023 flight data from Guangzhou Baiyun International Airport and it accurately classified airport operational states. To verify universality, the same framework was applied to 6 airports under the "one-city, two-airports" layout in Beijing, Shanghai and Chengdu. Results showed significant congestion pattern differences between existing and newly constructed airports. Conclusions confirm the proposed feature extraction and clustering framework is effective and universal, and it can accurately capture airport congestion dynamics. Under the "one-city, two-airports" layout, existing and newly constructed airports differ significantly in operational modes, and most single-airport city airports have operational modes highly consistent with existing airports. This study provides valuable decision-making references for airport managers and air traffic controllers. It helps them deepen understanding of air traffic dynamics and airport congestion patterns, thereby optimizing traffic management strategies and improving airport operational efficiency.

“Cascading failure” is a critical concept for characterizing the structures and dynamics of complex networks, which describes the chain reaction triggered by a single failure or error in a system or process. In the field of airport networks, cascading failure refers to the airport cascading failure (ACF). In recent years, most studies on ACF only focused on the state changes of airport nodes with the cascading failure models and neglected the impacts of propagation of flight delay (PFD). To analyze the robustness of airports in the sense of ACF, the improved coupled map lattice (I-CML) model is proposed by combining the impacts of PFD and the throughput of the airport. As an illustration, the Chinese airport network (CAN) is classified into four categories with the K -means clustering algorithm and the airport classification indicator. Our exploration discovers two interesting results: firstly, the I-CML model is more robust than the original CML model when identical attacks exist; secondly, the critical nodes of CAN are more likely to cause cascading failures and the important node attack can be modeled effectively by the random attack. In the perspective of airport network operations, it is necessary to protect the critical airport nodes to ensure the stability of the airport network.