Free Route Airspace (FRA) permits users to freely plan routes between defined entry and exit waypoints with the possibility of routing via intermediate waypoints, which is beneficial to improve flight efficiency. Dynamic management of sectors is essential for the future promotion of full-time FRA applications. In this paper, considering the demand uncertainty at the pre-tactical level, we construct an FRA complexity indicator system and use the XGBoost algorithm to predict the ATC workload. A two-stage sector boundary optimization method is proposed, using Binary Space Partition (BSP) to generate sector boundaries and an A*-based heuristic algorithm to automatically tune them to conform to the operational structure and “direct to” characteristics of FRA. Finally, this paper verifies the effectiveness of the proposed method for balancing ATC workload in a pre-designed Lanzhou FRA in China.

Aviation networks exhibit multi-layer characteristics and heterogeneity of nodes and edges. Identifying key nodes in a multi-layer heterogeneous aviation network (MHAN) and elucidating its cascading failure process are of great practical significance for enhancing the ability to resist failure and strengthening network resilience. Therefore, this paper first establishes the basic model of MHAN and then designs an intra-layer node importance evaluation method based on the improved TOPSIS-grey correlation analysis (ITG) method and an inter-layer influence weight quantification method based on the gravity model. By integrating the information transmission characteristics between network nodes, a key node identification method in MHAN through aggregating multi-source information is proposed. Finally, based on the actual overload operation of aviation networks, the initial load adjustment coefficient, capacity limit, and overload coefficient are introduced based on the traditional capacity–load model, a cascading failure model of MHAN considering overload condition and failure probability is constructed, and a node influence index based on this model is defined to verify the accuracy of the key node identification results. The instance analysis conducted on the aviation network in western China demonstrates that the intra-layer node importance evaluation method based on ITG yields results with better distinguishability and higher accuracy. The key nodes are predominantly hub en-route nodes and sector nodes. In the cascading failure model, increasing the overload coefficient and capacity limit within a specific range while reducing the initial load adjustment coefficient helps reduce the network failure scale. The key nodes identified by the node influence index are consistent with those identified by the method proposed in this paper, validating the accuracy and effectiveness of the key node identification method in MHAN through aggregating multi-source information herein.

In order to quantify the core competency of air traffic controllers (ATCO) in multi-post mode, and analyze the ATCO competency-post fit (CPF) degree, this paper proposes the AFE-TOPSIS evaluation method based on the analytic hierarchy process (AHP), factor analysis (FA), and entropy weight method (EWM). Through the analysis of job tasks, it preliminarily develops an evaluation index library of ATCO core competency. Then, it constructs an improved adaptive multi-dimension core competency model divided into four types of post modes. It proposes a method for determining the combined weight of core competency indexes, where the weights are determined subjectively through AHP, combined with the objective method of FA and EWM. Furthermore, it constructs a comprehensive evaluation model based on AFE-TOPSIS and analyzes the CPF degree by calculating the relative closeness degree of each evaluation index. The empirical analysis results show that the four types of core competency models in General, TWR, APP, and ACC post mode include seven dimensions, such as situation awareness, and workload management, which are also graded into the pyramid hierarchical structure of basic, advanced, and high-level. The calculation of the ATCO CPF degree based on the proposed AFE-TOPSIS method can scientifically assist in the fit of ATCO and multi-post mode.

Identifying the factors influencing airspace resources, quantifying the availability of airspace resources, and mastering their spatial distribution characteristics are the cornerstone of scientific and efficient airspace management. Therefore, this paper investigates the impact of prohibited, restricted, and dangerous areas (PRDs) on airspace resource availability from a traffic flow perspective, proposes a multi-layer network model, and establishes a flow-based sector resource availability (FSRA) calculation model. The FSRA in mainland China is calculated above the standard pressure altitude of 6000 m. The results show that the FSRA is lower when the sector is determined to have a higher PRD density, a more complex traffic flow pattern, and a more sophisticated interaction between the two. China’s mainland airspace is separated into three altitude ranges along the vertical direction according to the FSRA and sector distribution: 6000–7800 m, 7800–8900 m, and 8900–12,500 m. The spatial distribution characteristics of the FSRA are addressed using the ArcGIS software. The results demonstrate that spatial autocorrelation is exhibited for all three altitude ranges. The high–high cluster pattern mainly occurs in the western part of mainland Chinese airspace, while the low–low cluster pattern is distributed in the southeast. The three altitude ranges are divided into three groups, respectively, and suggestions for airspace management are made for each group.