Airspace Sectorization Research Articles

Analysing Sector Safety Performance Based on Potential Conflicts: Complex Network Approach

This paper presents an in-depth analysis of Sector Safety Performance (SeSPe), focusing on potential conflicts within the Spanish Air Traffic Network. SeSPe serves as a comprehensive analysis for evaluating the safety levels of individual airspace sectors, taking into account both the frequency and severity of potential conflicts, along with their geospatial characteristics. Using Complex Network Theory, this study applies a novel methodology to four Madrid ACC sectors, analysing one month of flight track data. Multiple weighted spatial-temporal networks are built using 60 min intervals, allowing for the identification of critical nodes and the examination of interconnections and structural dynamics within the air traffic network. By incorporating temporal variations, the analysis uncovers evolving patterns. The research introduces an innovative approach for calculating potential conflicts by integrating radar data with airspace structure, thus offering a deeper understanding of sector risk profiles. The development of the SeSPe indicator, which combines historical safety data with topological features, enables a more informed and strategic evaluation of Air Traffic Management (ATM) system performance, ultimately contributing to safer and more efficient airspace operations.

Dynamic airspace sectorization with machine learning enhanced workload prediction and clustering

Addressing the complexities of modern Air Traffic Management (ATM), this paper introduces a novel framework for dynamic airspace sectorization, tailored to enhance efficiency and safety in congested airspaces. Central to this framework is the WP-ConvLSTM model, an innovative deep learning approach equipped with attention mechanisms. This model excels in accurately predicting workload dynamics, a critical factor in managing air traffic flow. To implement sectorization, we adopt a constrained K-means clustering technique for spatial division, followed by a refinement process involving Support Vector Machine (SVM) algorithms for precise boundary generation. Further optimization of sector boundaries is achieved through an evolutionary algorithm, ensuring both flexibility and stability in airspace divisions. Our methodology was thoroughly evaluated using real-world data from one of the busiest airspaces, demonstrating significant improvements in workload prediction accuracy and airspace sector management. The findings highlight the model’s robustness in practical scenarios, offering a scalable solution for ATM challenges. We conclude with a recognition of the study’s limitations and propose avenues for future research to build upon our findings, particularly in enhancing real-time data integration and adapting to evolving air traffic patterns.

Increasing airspace capacity by improving ATCo’s efficiency through an innovative handover mechanism

The current lack of airspace capacity poses a major challenge to a sustainable and efficient air transport system, especially as future demand increases. Recent efforts to increase airspace capacity have focused on airspace sectorization. The digitization of Air Traffic Management (ATM) holds promise for overcoming current inefficiencies arising from the spatial fragmentation of airspace. While research has explored digitized ATM services to improve airspace capacity, there has been a notable gap in addressing spare capacity through enhancing synergies between adjacent sectors. This work proposes the application of an early handover (EHO) mechanism based on the spatio-temporal interdependencies among sectors and aircraft. Essentially, the mechanism involves alleviating overloaded sectors by redistributing peak demand to neighboring ones. This alleviation results in a reduction of the dedicated resources required to meet demand, facilitating the consolidation of sectors into larger units. Ultimately, this consolidation enhances airspace efficiency by minimizing the ATCo resources necessary for ATM. A simple and fast heuristic is proposed to select the aircraft for early handover to the air traffic controller of another sector, together with a specific schedule. A Colored Petri Net model is implemented to formalize the dynamic spatio-temporal interdependencies among adjacent sectors and to illustrate the EHO applicability using an academic exercise for illustration purposes. Afterwards, a simulation study based on real European airspace data is employed to illustrate and validate the mechanism. Finally, a discussion on the potential impacts of adopting the mechanism in real-life applications and lines of future research are provided.

The integration of En route flow optimization, complex network clustering, and rule-based approach to airspace sub-sectorization for enhanced air traffic monitoring

This study proposes the use of a novel integrated framework for 2D en route airspace sub-sectorization. The integrated framework combines the multi-commodity flow optimization approach, complex network clustering approach, and Minimum Bounding Geometry (MBG)-coupled Rule-based Approach for boundary design. A decomposition-based discrete particle swarm optimization (DPSO) is used to solve the clustering problem. The output of the flow optimization is used as a guiding standard for the DPSO. Experimentations were performed using the Indian airspace sector to validate the framework and DPSO was run for different maximum number of generations (maxgen). The findings reveal that the multi-commodity flow approach captures system-wide flow operations. Clustering results corresponding to maxgen=100 and maxgen=150 perform best in terms of equitable and balanced distribution of cluster size and traffic load. The MBG-coupled Rule-based Approach leads to compact and convex sub-sector boundary design. Major implications of this research include dynamic adaptability of the integrated framework, increased sensitivity of sector design to network evolution, and a computationally tractable framework. The higher controllability of the proposed framework also offers an increased acceptance among practitioners.

Real-time risk assessment method for multi-aircraft interaction based on potential field theory

To assess the operational risk of multi-aircraft interactions in complex air traffic scenarios, this paper proposes the 'multi-aircraft interaction risk potential field,' inspired by the similarities between aircraft risk and potential fields. The method further establishes risk potential fields for interactions both among multiple aircraft and between aircraft, waypoints, and routes. This paper develops real-time risk assessment models for both the aircraft level (microscopic state) and the airspace sector level (mesoscopic state), based on potential risk metrics. Comparisons with the air traffic controllers' subjective risk metric (Risk_SE) and the traditional conflict-time-based metric (Risk_ATSR) in a simulated visual environment of a designated airspace sector affirm the efficacy of the model. The results show that the proposed model substantially aligns with Risk_SE, displaying heightened sensitivity in specific intervals, evidenced by the mean absolute error rates, with Error_PE at 0.073, significantly lower than Error_ATSR at 0.121. Concurrently, the sector risk metric (Risk_S) realistically shows a delayed growth in assessment compared to the microscopic-state metric. Therefore, our method offers enhanced precision in representing operational risks for aircraft and airspace sectors. It also serves as a vital reference for decision-making in intricate air traffic scenarios and supports the sophisticated refinement of trajectory-based operations (TBO).

A Study on the Normalized Delineation of Airspace Sectors Based on Flight Conflict Dynamics

The study of airspace sector demarcation can help controllers to deal with complex air situations, provide reference for air traffic control services, reduce the workload of controllers, and ensure safe and efficient airspace operation. Based on complex network theory, this paper proposes a sector division method based on a mean shift clustering algorithm and a Voronoi diagram. Firstly, a flight conflict network is constructed based on the structural characteristics of the airborne flight situation, combined with the three-dimensional velocity barrier method and the aircraft protected area model. Secondly, six network topology indexes, such as the total node degree, average point strength and network density, are selected to construct the flight-situation-assessment index system, and the busy and idle time segments of the airspace are divided according to the comprehensive network indexes; finally, based on the historical flight data, the airborne aircraft in the busy and idle time segments are clustered using the mean shift clustering algorithm and the Voronoi diagram method, respectively, so as to obtain a sector division scheme. The simulation results show that this method can equalize the network topology indexes and provide a reference for the scientific allocation of controllers’ energy.

Machine learning in aviation: Is it possible to predict when an ATC Sector or Air Traffic Flow will be regulated?

Due to the increasing complexity of airspace, the ATC system does not have sufficient capacity to cope with aircraft demand. For this reason, the ATFCM system needs to implement more and more measures to balance capacity and demand. These measures are the ATFCM regulations. In this paper, a methodology to predict ATFCM capacity regulations based on a machine learning model is proposed. This model will try to predict whether an ATC sector will be regulated or not at a specific time based on the time of prediction and certain operating variables in the sector. A test has been carried out in the LECMPAU sector of Spanish airspace. With results of 91% accuracy in predicting whether the sector will be regulated or not and a logical explainability, it can be concluded that with the sufficient historical operation and certain operational variables, it is possible to predict when an ATFCM regulation capacity will appear in the airspace. It can also be concluded that without a detailed knowledge of the operation in a sector, it is possible to make this prediction because patterns can be found in historical behaviour.

An Approach for Three-Dimensional Sectorization in the Terminal Area Based on Airspace Function

As explainable artificial intelligence (XAI) has grown in popularity, it has become possible to more clearly explain the functional correspondence between airspace and traffic flow, which can alleviate the contradiction between the continuously increasing traffic demand in the terminal area and the limited airspace capacity; this paper studies the three-dimensional sectorization in the terminal area based on the airspace function. First, trajectory clustering is adopted to classify the functions of air traffic flows. Then, a functional sectorization framework is proposed to enable different airspace sectors with varied functions, where specifically the functional consistency objective between the airspace sector and the traversed air traffic flows is proposed. Third, a corresponding efficient algorithm is designed to generate functional sectors with an accurate scope that can well separate different traffic flows. Finally, the proposed method is evaluated by the actual operation datasets in the Shanghai terminal area. The results show that the method proposed in this paper can not only generate three-dimensional sectors with specific functions according to the prevailing traffic flow in the complex terminal area, which is conducive to the construction of controllers’ situational awareness, but also can reduce potential conflicts and traffic density variance, increasing average sector flight time a lot.

Validating Dynamic Sectorization for Air Traffic Control Due to Climate Sensitive Areas: Designing Effective Air Traffic Control Strategies

Dynamic sectorization is a powerful possibility to balance the controller workload with respect to traffic flows changing over time. A multi-objective optimization system analyzes the traffic flow over time and determines suitable time-dependent sectorizations. Our dynamic sectorization system is integrated into a radar display as part of a working environment for air traffic controllers. A use case defining climate-sensitive areas leads to changes in traffic flows. When using the system, three controllers are assessed in two scenarios: the developed controller assistance system and the work in a dynamic airspace sectorization environment. We performed a concept validation in which we evaluated how controllers cope with sectors adapting to the traffic flow. The solution was rated as highly applicable by the involved controllers. The trials revealed the necessity to adapt the current procedures and define new aspects more precisely. In this paper, we present the developed environment and the theoretical background as well as the traffic scenarios. Furthermore, we describe the integration in an Air Traffic Management (ATM) environment and the questionnaires developed to assess the functionality of the dynamic sectorization approach. Finally, we present a proposal to enhance controller guidelines in order to cope with situations emerging from dynamic sectorizations, including naming conventions and phraseology.

Analysing the benefits of trajectory deviations for planar trajectory optimisation

Aircraft traverse airspace sectors under the supervision of Air Traffic Controllers by following navigation points. The trajectories can deviate from these points under the supervision of controllers. However, the controllers have to consider various factors for managing air traffic which increases their workload. We aim to offline analyse the potential benefits of trajectory deviations, by tactically shifting navigation points for planar trajectory optimisation of multiple aircraft at an enroute sector level. Historic deviations of trajectories are considered for this analysis to implement them at practical level. Within nominal speed and turning rate of aircraft trajectories, 5.47% reduction in time cost and an annual fuel saving worth US$ 29.32 million has been estimated using shifts for an airspace sector.

Prediction of Capacity Regulations in Airspace Based on Timing and Air Traffic Situation

The Air Traffic Control (ATC) system suffers from an ever-increasing demand for aircraft, leading to capacity issues. For this reason, airspace is regulated by limiting the entry of aircraft into the airspace. Knowledge of these regulations before they occur would allow the ATC system to be aware of conflicting areas of the airspace, and to manage both its human and technological resources to lessen the effect of the expected regulations. Therefore, this paper develops a methodology in which the final result is a machine learning model that allows predicting capacity regulations. Predictions shall be based mainly on historical data, but also on the traffic situation at the time of the prediction. The results of tests of the model in a sector of Spanish airspace are satisfactory. In addition to testing the model results, special emphasis is placed on the explainability of the model. This explainability will help to understand the basis of the predictions and validate them from an operational point of view. The main conclusion after testing the model is that this model works well. Therefore, it is possible to predict when an ATC sector will be regulated or not based mainly on historical data.

A Spatiotemporal Hybrid Model for Airspace Complexity Prediction

Airspace complexity is a key indicator that reflects the safety of airspace operations in air traffic management systems. Furthermore, to achieve efficient air traffic control, it is necessary to accurately predict the airspace complexity. In this article, we propose a novel spatiotemporal hybrid deep learning model for airspace complexity prediction to efficiently capture spatial correlations as well as temporal dependencies pertaining to the airspace complexity data. Specifically, we apply convolutional networks to discover the short-term temporal patterns and skip long short-term memory networks to model the long-term temporal patterns of airspace complexity data. Furthermore, it is observed that the graph attention network in our proposed model, which emphasizes capturing the spatial correlations of the airspace sectors, can significantly improve the prediction accuracy. Extensive experiments are conducted on the real data of six airspace sectors in Southwest China. The experimental results show that our spatiotemporal deep learning approach is superior to state-of-the-art methods.

Study on Characteristics and Invulnerability of Airspace Sector Network Using Complex Network Theory

The air traffic control (ATC) network’s airspace sector is a crucial component of air traffic management. The increasing demand for air transportation services has made limited airspace a significant challenge to sustainable and efficient air transport operations. To address the issue of traffic congestion and flight delays, improving the operational efficiency of ATC has been identified as a key strategy. A clear understanding of the characteristics of airspace sectors, which are the building blocks of ATC, is essential for optimizing air traffic management. In this research, a novel approach using complex network theory was applied to examine the features and invulnerability of the airspace sector network. We developed a model of the airspace sector network by treating air traffic control sectors as network nodes and the flow of air traffic between these sectors as edges. Network characteristics were analyzed using several metrics including degree, intensity, average path length, betweenness centrality, and clustering coefficient. The static invulnerability of the airspace sector network was evaluated through simulation, and the network efficiency and the size of the connected component were used to assess its invulnerability. A study was conducted in North China based on the ATC sector network. The findings of the study revealed that the sector network did not exhibit the traits of a small-world network model, characterized by short average path lengths and high clustering coefficients. The evaluation of network invulnerability showed that the network’s invulnerability varied depending on the attack strategy used. It was discovered that attacking sectors with high betweenness resulted in the most significant harm to network invulnerability, and betweenness centrality was considered to be a useful indicator for identifying critical sectors that require optimization.

Multi-Objective 3D Airspace Sectorization Problem Using NSGA-II with Prior Knowledge and External Archive

Airspace sectorization is a powerful means to balance the increasing air traffic flow and limited airspace resources, which is related to the efficiency and safety of operations. In order to divide sectors reasonably, a multi-objective optimization framework for 3D airspace sectorization is proposed in this paper, including four core modules: Flight clustering, sector generation, workload evaluation, and sector optimization. Specifically, it clusters flights and generates initial sectors using a Voronoi diagram. To further optimize sector shape, the concept of dynamic density is introduced to evaluate the controller workload, based on which a sector optimization model is constructed. The model not only considers intra-sector and inter-sector workloads as objective functions but also sets hard constraints to meet operation and safety requirements. To solve it, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) with prior knowledge and an external archive is designed. By analyzing the optimization results of actual operational data in the Singapore regional airspace, our approach obtains diverse optimal sectorization schemes for decision makers to choose from. Qualitative and quantitative experimental results confirm that the initial population strategy with prior knowledge significantly accelerates the convergence process. At the same time, the mechanism of the external archive effectively enriches the diversity of solutions.

Research on airspace sector optimization based on Voronoi diagram and improved K-means algorithm

Sector partition is an important task of air traffic control, and a reasonable sector partition can improve the utilization rate of airspace and protect the flight safety of aircrafts. Since the sector partition during flat hours is not well suited to the complex air situation, this paper proposes a sector optimization method based on Voronoi diagram and improved K-means. Firstly, a conflict network is constructed based on the air situation, and a comprehensive sector control workload measurement method is proposed by combining aircraft velocity obstacle relationship and complex network theory. Based on the workload value, a cluster center is determined as the generating element of Voronoi diagram by using the improved K-means method, and then the sector is optimized by using the division method of Voronoi diagram. In this paper, the data of Xiamen airspace control sectors are collected as a simulation scenario for calculation and analysis. The simulation results show that the average variance of the optimized sector control workload is reduced by 66.04% during the peak hours and 13.88% during the flat hours compared with the original sector. The method achieves the purpose of balancing the sector workload, verifies the effectiveness of the sector optimization method, and provides a reference basis for the existing sector partition work.

Dynamic Boundary Optimization of Free Route Airspace Sectors

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.

Image-Based Multi-Agent Reinforcement Learning for Demand–Capacity Balancing

Air traffic flow management (ATFM) is of crucial importance to the European Air Traffic Control System due to two factors: first, the impact of ATFM, including safety implications on ATC operations; second, the possible consequences of ATFM measures on both airports and airlines operations. Thus, the central flow management unit continually seeks to improve traffic flow management to reduce delays and congestion. In this work, we investigated the use of reinforcement learning (RL) methods to compute policies to solve demand–capacity imbalances (a.k.a. congestion) during the pre-tactical phase. To address cases where the expected demands exceed the airspace sector capacity, we considered agents representing flights who have to decide on ground delays jointly. To overcome scalability issues, we propose using raw pixel images as input, which can represent an arbitrary number of agents without changing the system’s architecture. This article compares deep Q-learning and deep deterministic policy gradient algorithms with different configurations. Experimental results, using real-world data for training and validation, confirm the effectiveness of our approach to resolving demand–capacity balancing problems, showing the robustness of the RL approach presented in this article.

Scalable Autonomous Separation Assurance With Heterogeneous Multi-Agent Reinforcement Learning

In this article, a scalable autonomous separation assurance framework is proposed for high-density en route airspace sectors with heterogeneous aircraft objectives. To handle the complex dynamic decision making under uncertainty, multi-agent reinforcement learning is used in a decentralized approach with each aircraft being represented as an agent. Based on this, each agent locally solves the separation assurance problem, allowing the framework to scale to a large number of aircraft. In addition, each agent has the ability to learn the intention of the intruder aircraft, which is essential in environments with heterogeneous agents. Numerical experiments are performed in a real-time air traffic simulator. The results demonstrate that the proposed framework is able to effectively ensure the safe separation of heterogeneous agents, while also optimizing the intrinsic agent objectives in high-density en route airspace sectors. In addition, the efficiency of the proposed framework is demonstrated and shown to provide real-time decision making for separation assurance. Note to Practitioners—In commercial aviation, the workload of human air traffic controllers increases with the growth of air traffic density. Robust decision making systems to augment human air traffic controllers allows for increased air traffic without increased workload, resulting in a safer airspace environment. In addition, advanced air mobility (AAM) is concerned with low-altitude airspace operations with both human and autonomous pilots. In this environment, autonomous real-time separation assurance systems are required. Most traditional separation assurance approaches fail to handle stochastic and high-density environments, rendering them inapplicable to future high-density traditional airspace and the envisioned low-altitude AAM operations. Therefore, it is important to study decentralized approaches that can place the separation assurance problem as an intrinsic objective of each aircraft to ensure cooperation in high-density airspace. With this consideration, a scalable, decentralized autonomous separation assurance framework capable of handling heterogeneous agents is proposed in this article. This framework is able to perceive the current air traffic environment and select speed advisories to ensure safe separation requirements, while balancing intrinsic objectives such as minimizing delay. While one limitation of multi-agent reinforcement learning is the long training time, this article demonstrates how the framework also can leverage modern computing clusters to significantly reduce training time without sacrificing performance.

Predicting the likelihood of airspace user rerouting to mitigate air traffic flow management delay

At present, the most common air traffic flow management measure used by the European Network Manager to solve en-route demand and capacity balance issues is to impose air traffic flow management regulations in congested airspace, which delay flights on the ground to smooth demand. The delay imposed on a regulated flight, however, has a negative operational and economic impact on the corresponding airspace user. When the impact is severe, the airspace user may reroute the flight to tactically circumvent the regulated airspace, thereby avoiding the regulation and reducing (or even eliminating) the delay. This legitimate strategy is unquestionably effective in mitigating the delay and the associated cost, but it may also increases traffic demand uncertainty due to last-minute changes as well as the environmental impact due to sub-optimal trajectories. This paper presents a gradient-boosted decision trees model that, trained on historical data, could assist in predicting the likelihood of a regulated flight rerouting to mitigate air traffic flow management delay. Specific metrics for highly imbalanced binary classification problems reveal that, while not perfect, the model outperforms a rudimentary decision tree and a dummy classifier. Furthermore, Shapley values computed with the model uncover the most important triggers for tactical rerouting from a data-driven perspective, namely the current delay, the aircraft operator, the airspace sector where the most penalising regulation is applied and the remaining time until departure. This interpretable model could be used to assist flow managers in their decision-making, as well as to accurately mimic the behaviour of flight dispatchers when facing delays in simulators.

A Unified Airspace Risk Management Framework for UAS Operations

Collision risk modelling has a long history in the aviation industry, with mature models currently utilised for the strategic planning of airspace sectors and air routes. However, the progressive introduction of Unmanned Aircraft Systems (UAS) and other forms of air mobility poses new challenges, compounded by a growing need to address both offline and online operational requirements. To address the associated gaps in the existing airspace risk assessment models, this article proposes a comprehensive risk management framework, which relies on a novel methodology to model UAS collision risk in all classes of airspace. This methodology inherently accounts for the performance of Communication, Navigation and Surveillance (CNS) systems, and, as such, it can be applied to both strategic and tactical operational timeframes. Additionally, the proposed approach can be applied inversely to determine CNS performance requirements given a target value of collision probability. This new risk assessment methodology is based on a rigorous analysis of the CNS error characteristics and transformation of the associated models into the spatial domain to generate a protection volume around each predicted air traffic conflict. Additionally, a methodology to quickly and conservatively evaluate the multi-integral formulation of collision probability is introduced. The validity of the proposed framework is tested using representative CNS performance parameters in two simulation case studies targeting, respectively, a terminal manoeuvring area and an enroute scenario.