In order to improve the ability of airspace management, a multi-attribute decision-making tool based on q-rung orthopair probability hesitant fuzzy GRA-TOPSIS is proposed to solve the problem of airspace operation effectiveness evaluation; this is in view of the fact that there are few airspace operation effectiveness evaluation methods in general aviation airports. Firstly, taking general aviation airports as the research object, a complete airspace operation effectiveness evaluation system is newly established, its evaluation indicators are introduced, and its multi-attribute decision-making ideas are explained. Then, based on the q-rung orthopair probability hesitant fuzzy set, a new distance measure and information aggregation operator are defined, which can better deal with symmetry information. Secondly, we build a deviation maximization model to calculate the attribute weights of indicator elements in the decision-making process. Then, we combine the GRA method and TOPSIS method to rank the airspace operation effectiveness evaluation schemes. Finally, combined with calculation examples and comparative analysis, the reliability and rationality of the method proposed in this paper are verified, and the symmetry relationship between the evaluation results and the actual situation is better reflected. Experiments show that the method proposed in this paper can obtain more accurate airspace operation effectiveness evaluation results, and can provide reference for related research.

Urban air mobility is an effective solution to address the current issue of ground traffic congestion in future cities. However, as the user scale continues to expand, the current civil aviation flight scheduling and control methods are becoming inadequate to meet the high-volume flight guarantee demands of future urban air transportation. In order to effectively handle and resolve potential issues in this field in the future, this paper proposes a method for planning urban air mobility route networks. The planning process is divided into two stages: construction and optimization. Methods for constructing urban air mobility route networks based on flight routes and global optimization methods based on node movement are proposed in each stage. In the construction stage, a complete construction process is designed to generate routes based on existing flight routes, in line with the trend of urban air transportation development. In the optimization stage, inspired by the ant colony algorithm, node transfer rules and information transfer rules are incorporated to design a global optimization process and algorithm for route networks. Experimental results demonstrate the effectiveness and advancement of the proposed planning method.

With the rapid advancement of unmanned aerial vehicle (UAV) technology, the widespread utilization of UAVs poses significant challenges to urban low-altitude safety and airspace management. In the coming future, the quantity of drones is expected to experience a substantial surge. Effectively regulating the flight behavior of UAVs has become an urgent and imperative issue that needs to be addressed. Hence, this paper proposes a standardized approach to UAV flight through the design of an air channel network. The air channel network comprises numerous single air channels, and this study focuses on investigating the characteristics of a single air channel. To achieve optimal outcomes, the concept of the artificial potential field algorithm is integrated into the deep Q-learning algorithm during the establishment of a single air channel. By improving the action space and reward mechanism, the resulting single air channel enables efficient avoidance of various buildings and obstacles. Finally, the algorithm is assessed through comprehensive simulation experiments, demonstrating its effective fulfillment of the aforementioned requirements.

As the demand for air transport continues to increase, air traffic congestion in the terminal area is becoming more and more serious. In order to assist the controller in efficiently handling the symmetrical activities of aircraft take-off or landing and alleviate traffic congestion, this paper proposes a method for identifying traffic congestion situations based on complex networks and a multiclass random forest algorithm with symmetrical characteristics. First, the approach points, departure points, waypoints, and navigation stations are used as nodes, the flight paths as edges, and the busyness of the paths as edge weights to construct a traffic network model for the terminal area. On this basis, five congestion situation recognition indicators are selected from the perspective of network topology, and a symmetric multiclass random forest algorithm is proposed to recognize the congestion situation. Finally, this method is compared with the situation recognition method based on the traditional random forest algorithm. The results of the simulation experiment show that compared with the traditional random forest algorithm, the proposed recognition model improves the recognition accuracy by 17.5%, can better handle symmetry information, and can accurately determine the traffic congestion situation in the terminal area.

In the wartime scenario of large-scale air combat, the battlefield situation is dynamically changing, and damaged aircraft often face issues such as limited maneuverability and fuel shortages. Relying on fixed corridors for return flights is no longer feasible. To quickly address wartime demands, it is essential to determine alternative routes for damaged aircraft in real-time, which must not only avoid multi-source threats but also adapt to their performance constraints. This paper improves the traditional artificial potential field algorithm by adopting a two-tier strategy that combines the greedy algorithm with the artificial potential field algorithm. First, to address limitations of the traditional APF method, relative distance is introduced to solve target unreachability, adjustment force is introduced to break local minimum traps, a dynamic potential field reconstruction strategy is introduced to traverse dense obstacles, and a memory-based resultant force is introduced to suppress oscillations. Subsequently, the greedy algorithm optimizes the initial path planned by the APF to shorten the distance. However, when sudden or moving threats are encountered and their influence range is entered, the fast-computing APF algorithm is used for real-time avoidance. The simulation results confirm that the improved algorithm effectively resolves the above issues, with fast planning, smooth, high-quality paths, and guaranteed safe return of aircraft.

Air refueling extends aircraft range and endurance, but its operational value hinges on where the refueling airspace is placed and how tanker missions are sequenced. This paper addresses the joint optimization of refueling airspace planning and tanker scheduling, in which each receiver selects a refueling point from a continuous feasible interval along a fixed route. The upper level determines refueling point locations (continuous variables), while the lower level schedules multiple heterogeneous tankers (discrete combinatorial variables); the two levels are tightly coupled through spatiotemporal constraints and fuel propagation. We propose a bottleneck-driven decoupled update (BDDU) strategy built on the Whale Optimization Algorithm (WOA). BDDU extracts bottleneck states from lower-level scheduling feedback and applies per-dimension step-size control to damp the coupling amplification effect inherent in bi-level optimization. Across three scenarios of varying coupling intensities and scales, BDDU-WOA raises the feasibility rate from 50% (WOA baseline) to 90% (+40 percentage points; $p<0.05$, Fisher’s exact test). The gain stems from a bottleneck-aware, dimension-wise step-size control mechanism with an adaptive, parameter-free classification threshold and only two tunable parameters, adding roughly 10% computational overhead. The method is intended for pre-mission planning of large-scale air refueling operations.