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Future Airspace and Air Traffic Management Design

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A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Air Traffic and Transportation".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 14025

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Special Issue Editors

Dr. Sebastian Schier-Morgenthal

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Guest Editor

German Aerospace Center (DLR), Institute of Flight Guidance, Lilienthalplatz 7, 38108 Braunschweig, Germany
Interests: air traffic management; air traffic control; automation; virtual agents

Prof. Dr. Daniel Delahaye

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Guest Editor

ENAC—National School of Civil Aviation, Avenue Edouard Belin CS 54005, CEDEX 4, 31055 Toulouse, France
Interests: machine learning; airspace design; aircraft trajectories optimization; urban air mobility
Special Issues, Collections and Topics in MDPI journals

Dr. Bernd Korn

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Guest Editor

German Aerospace Center (DLR), Institute of Flight Guidance, Lilienthalplatz 7, 38108 Braunschweig, Germany
Interests: airspace design; pilot assistance systems; sector-less air traffic management; 4D trajectory management

Ingrid S. Gerdes

E-Mail Website
Guest Editor

German Aerospace Center (DLR), Institute of Flight Guidance, Lilienthalplatz 7, 38108 Braunschweig, Germany
Interests: air traffic control; evolutionary algorithms; flexible airspace structure

Special Issue Information

Dear Colleagues,

The current air traffic system is reaching its limits. The traditional principle of dividing the airspace into sectors, assigning one air traffic control unit and organizing traffic via one radio communication line is not capable of handling the peak traffic numbers. Moreover, additional services as fuel optimized manoeuvres, traffic flows with reduced climate footprint and the integration of new vehicles struggle with the current airspace organisation and the air traffic controller availability. As such, novel techniques like sector-less control, digital assistants and datalink need to be further researched and combined into operational concepts for future air traffic management.

This Special Issue of Aerospace covers the latest advances in technologies and operational concepts that provide solutions for a future airspace and air traffic management design. The editor of this Special Issue invites authors to submit papers on flexible airspace structures, solutions for higher automation and technologies to overcome the current single sector air traffic management. As such, the potential for a more efficient and more sustainable airspace for future operations will be the focus of this issue.

Dr. Sebastian Schier-Morgenthal
Prof. Dr. Daniel Delahaye
Dr. Bernd Korn
Ingrid S. Gerdes
Guest Editors

Manuscript Submission Information

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Keywords

airspace design
future air traffic management structures and methods
air traffic control
automation
flexible airspace design
artificial intelligence in air traffic management
digital assistance
next (digital) level of situational awareness

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Published Papers (13 papers)

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Research

30 pages, 889 KiB

Open AccessArticle

Increased Safety Goes Hand in Hand with Higher Cost Efficiency: Single-Controller Operation Showcasing Its Advantages

by Robert Hunger, Julian Böhm, Leo Julius Materne, Lothar Christoffels, Lukas Tyburzy, Thorsten Mühlhausen, Matthias Kleinert and Andreas Pick

Aerospace 2025, 12(4), 321; https://doi.org/10.3390/aerospace12040321 - 9 Apr 2025

Viewed by 291

Abstract

While traffic levels are predicted to rise, nearly all European air navigation service providers suffer from staff shortages. In most cases, two air traffic controllers are deployed to control one airspace sector. Enabling the deployment of one controller per sector could be a solution to staff shortage problems. For this Single-Controller Operation (SCO) concept, a demonstrator with integrated support tools based on advanced information technology was developed. These partially automate some controller tasks to allow one controller to work off the same traffic amount as a controller team. The system was tested in a human-in-the-loop real-time simulation under varying traffic loads using a 2 × 2 within-subjects design. The variables assessed include separation minima infringements, exit flight level deviations, instantaneous self-assessment, voice communication, flight distance, and fuel burn. The results show no negative influence on safety, workload, situational awareness, operational efficiency, and environment, with 80% of maximum allowed declared capacity. Thus, SCO has the potential to mitigate staff shortages and raise cost efficiency by 40%. These results showcase the feasibility of the SCO concept under nominal conditions. Assessments with different traffic levels, non-nominal conditions, and an interdependent multi-sector SCO layout are recommended for further investigations. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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22 pages, 6290 KiB

Open AccessArticle

The Concept of an Early Warning System for Supporting Air Traffic Control

by Piotr Konopka and Paweł Rzucidło

Aerospace 2025, 12(4), 288; https://doi.org/10.3390/aerospace12040288 - 29 Mar 2025

Viewed by 278

Abstract

This article addresses the issue of loss of separation incidents and discusses currently implemented technological solutions designed to minimize the risk of such occurrences. An evaluation of these solutions is conducted, highlighting their key advantages and disadvantages. Additionally, a literature review of proposed new solutions is presented, emphasizing the necessity of introducing a new system to address previously identified shortcomings. This work proposes an early warning system for potential airspace collisions based on an artificial neural network. Drawing from the literature analysis, five fundamental assumptions for an early conflict warning system to support air traffic control are formulated. Each assumption is justified, with some addressing the weaknesses of existing solutions. The contributions of this paper, in relation to previously analyzed works, are as follows: (1) the system does not rely on the dynamics model of a specific aircraft type, (2) the possibility of radar vectoring (vectors to final) is considered, (3) the input data are not limited to the horizontal plane and time differences, (4) the system does not require identifying the most similar historical trajectories to assess minimum separation values and potential conflicts, and (5) the system is expected to perform better in airspace where radar vectoring prevails compared to flight along standard routes. The research methodology is discussed in detail, including the operational environment of the system and the applied algorithms. A feedforward neural network was selected, featuring 32 neurons in the first hidden layer and 16 neurons in the second hidden layer. The training process was conducted using the Levenberg–Marquardt algorithm, chosen for its fast convergence. The presented analyses confirm that the developed system meets the established assumptions. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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21 pages, 5148 KiB

Open AccessArticle

Designing an Urban Air Mobility Corridor Network: A Multi-Objective Optimization Approach Using U-NSGA-III

by Zhiyuan Zhang, Yuan Zheng, Chenglong Li, Bo Jiang and Yichao Li

Aerospace 2025, 12(3), 229; https://doi.org/10.3390/aerospace12030229 - 12 Mar 2025

Viewed by 542

Abstract

The corridor network serves as an effective solution for the airspace structure safety design of UAM. However, current studies rarely account for the ground risk posed by the corridor operation and typically consider a single design objective with limited variables. In this paper, we address these gaps by considering three key factors: demand, safety, and implementation costs. The corridor network design is formulated as a multi-objective optimization problem. In practice, firstly, we define the travel time-saving rate, average population density, and total length of corridors as optimization objectives. Then, we propose a straightforward and efficient corridor network encoding scheme that supports a variable number of corridors, significantly enhancing the diversity and flexibility of corridor network designs. Finally, based on this encoding scheme, we solve the corridor network problem using the unified non-dominated sorting genetic algorithm III (U-NSGA-III). Based on a detailed analysis of the obtained Pareto front, a relatively optimal design scheme across three optimization objectives is determined. The case study conducted in Chengdu illustrates that the corridor network obtained by our method not only achieves a 37.8% reduction in ground risk and a 69.9% decrease in implementation costs, but also saves a comparable 4.7% in time relative to traditional methods. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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26 pages, 6140 KiB

Open AccessArticle

Airspace Structure Study with Capacity Compensation for Increasing Diverse Operations

by Tobias Welsch and Marco-Michael Temme

Aerospace 2025, 12(3), 227; https://doi.org/10.3390/aerospace12030227 - 11 Mar 2025

Viewed by 510

Abstract

Future aircraft designs with a wide range of performance parameters, such as electric and supersonic aircraft, will have to be accommodated in traditional airspace designs in the future. Allowing an individual optimization of traditional approach speed profiles has a similar, broadening effect on approach speed characteristics. The resulting necessity of integrating Increasing Diverse Operations (IDO) will lead to a reduction in capacity at hub airports, as larger gaps will have to be inserted between aircraft with very different speed profiles. This is due to the large range of different approach speeds that IDO encompasses. Such a development will present a challenge for airports, which are already operating at or near their capacity limit. An alternative routing towards an intercept point at a late stage of the final approach can provide two approach options with low interference for subsequent traffic. Based on traffic data from London Heathrow, this study evaluates the performance in terms of runway capacity for different constellations of this procedure. Moreover, the biphasic evaluation, conducted through theoretical calculations for a constant separation distance and a fast-time simulation for a constant separation time, yielded key findings that facilitated the development of an optimized procedure for a traffic mix with significant speed differences to compensate IDO-related capacity losses as far as possible. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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13 pages, 604 KiB

Open AccessArticle

Multi-Objective Airport Slot Allocation with Demand-Side Fairness Considerations

by Ruoshi Yang, Meilong Le and Qiangzhe Wang

Aerospace 2025, 12(2), 119; https://doi.org/10.3390/aerospace12020119 - 3 Feb 2025

Viewed by 1064

Abstract

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

ε

-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%. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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21 pages, 1687 KiB

Open AccessArticle

New Approaches for the Use of Extended Mock-Ups for the Development of Air Traffic Controller Working Positions

by Lennard Nöhren, Lukas Tyburzy, Marco-Michael Temme, Kathleen Muth, Thomas Hofmann, Deike Heßler, Felix Tenberg, Eilert Viet and Michael Wimmer

Aerospace 2025, 12(2), 114; https://doi.org/10.3390/aerospace12020114 - 31 Jan 2025

Viewed by 658

Abstract

Today, integrating new functions into air traffic controller working positions or developing completely new displays are time-consuming and expensive processes. The users are often only included during the concept phase and after the main development phase is completed. Therefore, they do not have the chance to influence the design and development process by giving structural feedback. Any subsequent changes to the system after completing the main development phase will be expensive and slow. This paper proposes a new approach to integrate designers and users more tightly in the development process for digital air traffic control systems. By creating and reviewing realistic mock-ups in small iterative steps, the look and feel of future support functions can be validated in advance of the actual implementation and easily adapted if changes are requested. We performed a series of steps to evaluate the new workflow as a case study, including idea development, design, validation, and implementation into the target system. In a validation campaign with air traffic controllers, the developed design and functionalities received very positive feedback and the new workflow was successfully applied and evaluated as a case study. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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35 pages, 13315 KiB

Open AccessArticle

Feasibility of Conflict Prediction of Drone Trajectories by Means of Machine Learning Techniques

by Victor Gordo, Javier A. Perez-Castan, Luis Perez Sanz, Lidia Serrano-Mira and Yan Xu

Aerospace 2024, 11(12), 1044; https://doi.org/10.3390/aerospace11121044 - 20 Dec 2024

Viewed by 911

Abstract

The expected number of drone operations in the coming decades, together with the fact that most of them will take place in very-low-level airspace, will lead to a density of drone flights much greater than that of conventional manned aviation. In this context, the number of conflicts (i.e., 4D convergence of drone trajectories below the safe separation minima) will be much more frequent than in manned aviation and, therefore, conventional air traffic management methods or even the specific proposed mechanisms for drone traffic management are unlikely to be able to solve them safely. This paper considers a set of simulated drone trajectories in a high-density urban environment to analyze the applicability of machine learning regression and classification techniques to detect conflicts among such trajectory times in advance of their occurrence in order to provide new methods to manage the expected drone traffic density safely and efficiently. This would not be possible with current drone traffic management solutions. The obtained results suggest that the Random Forest, Artificial Neural Networks and Logistic Regression algorithms could detect nearly all near-collisions up to 10 s before they occur, and the first two algorithms could also detect a significant number of near-collisions more than 60 s earlier. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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16 pages, 4125 KiB

Open AccessArticle

Optimizing Large-Scale Demand and Capacity Balancing in Air Traffic Flow Management Using Deep Neural Networks

by Yunxiang Chen, Yifei Zhao, Fan Fei and Haibo Yang

Aerospace 2024, 11(12), 966; https://doi.org/10.3390/aerospace11120966 - 25 Nov 2024

Viewed by 1075

Abstract

Over the past forty years, air traffic flow management (ATFM) has garnered significant attention since the initial approach was introduced to address single-airport ground delay issues. Traditional methods for solving both single- and multi-airport ground delay problems primarily rely on operations research techniques and are typically formulated as mixed-integer problems (MIPs), with solvers employed to approximate optimal solutions. Despite their effectiveness in smaller-scale problems, these approaches struggle with the complexity and scalability required for large-scale, multi-sector ATFM, leading to suboptimal performance in real-time scenarios. To overcome these limitations, we propose a novel neural network-based demand and capacity balancing (NN-DCB) method that leverages neural branching and neural diving to efficiently solve the ATFM problem. Using data from 15,927 flight trajectories across 287 airspace sectors on a typical day in February 2024, our method re-allocates trajectory entry and exit times in each sector. The results demonstrate that large-scale ATFM problems can be solved within 15 min, offering a significant performance improvement over the state-of-the-art methods. This study confirms that neural network-based approaches are more effective for large-scale ATFM problem-solving. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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26 pages, 2375 KiB

Open AccessArticle

Flight-Based Control Allocation: Towards Human–Autonomy Teaming in Air Traffic Control

by Gijs de Rooij, Adam Balint Tisza and Clark Borst

Aerospace 2024, 11(11), 919; https://doi.org/10.3390/aerospace11110919 - 8 Nov 2024

Viewed by 1216

Abstract

It is widely recognized that airspace capacity must increase over the coming years. It is also commonly accepted that meeting this challenge while balancing concerns around safety, efficiency, and workforce issues will drive greater reliance on automation. However, if automation is not properly developed and deployed, it represents something of a double-edged sword, and has been linked to several human–machine system performance issues. In this article, we argue that human–automation function and task allocation may not be the way forward, as it invokes serialized interactions that ultimately push the human into a problematic supervisory role. In contrast, we propose a flight-based allocation strategy in which a human controller and digital colleague each have full control authority over different flights in the airspace, thereby creating a parallel system. In an exploratory human-in-the-loop simulation exercise involving six operational en route controllers, it was found that the proposed system was considered acceptable after the users gained experience with it during simulation trials. However, almost all controllers did not follow the initial flight allocations, suggesting that allocation schemes need to remain flexible and/or be based on criteria capturing interactions between flights. In addition, the limited capability of and feedback from the automation contributed to this result. To advance this concept, future work should focus on substantiating flight-centric complexity in driving flight allocation schemes, increasing automation capabilities, and facilitating common ground between humans and automation. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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35 pages, 4365 KiB

Open AccessArticle

Validating Flow-Based Arrival Management for En Route Airspace: Human-In-The-Loop Simulation Experiment with ESCAPE Light Simulator

by Katsuhiro Sekine, Daiki Iwata, Philippe Bouchaudon, Tomoaki Tatsukawa, Kozo Fujii, Koji Tominaga and Eri Itoh

Aerospace 2024, 11(11), 866; https://doi.org/10.3390/aerospace11110866 - 22 Oct 2024

Cited by 1 | Viewed by 1406

Abstract

The advancement of Arrival MANager (AMAN) is crucial for addressing the increasing complexity and demand of modern airspace. This study evaluates the operational feasibility and effectiveness of an innovative AMAN designed for en route airspace, the so-called En Route AMAN. The En Route AMAN functions as a controller support system, facilitating the sharing of information between en route air traffic controllers (ATCos), approach controllers (current AMAN), and airport controllers (Departure Managers) in airports with multiple runways. The En Route AMAN aims to support upstream ATCos by sequencing and spacing of incoming streams via speed control and runway assignment, thereby enhancing overall air traffic efficiency. Human-In-The-Loop simulations involving rated ATCos are performed under scenarios that replicate real-world traffic and weather conditions. These simulations focus on upstream airspace to assess the impact of En Route AMAN on delay mitigation and ATCos’ performance. Unlike previous studies that solely relied on theoretical models and fast-time simulation for operational feasibility evaluation, this approach incorporates ATCos’ real-time decision-making, situational awareness, and task management, addressing critical operationalization challenges. The results demonstrated that the En Route AMAN could reduce the average flight duration by up to 25.6 s and decrease the total number of ATCo instructions by up to 20% during peak traffic volume. These findings support that the En Route AMAN is both operationally viable and effective in mitigating arrival delays, highlighting the importance of Human-In-The-Loop for practical validation. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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23 pages, 3282 KiB

Open AccessArticle

Joint Optimization of Cost and Scheduling for Urban Air Mobility Operation Based on Safety Concerns and Time-Varying Demand

by Yantao Wang, Jiashuai Li, Yujie Yuan and Chun Sing Lai

Aerospace 2024, 11(10), 861; https://doi.org/10.3390/aerospace11100861 - 20 Oct 2024

Viewed by 1624

Abstract

As the value and importance of urban air mobility (UAM) are being recognized, there is growing attention towards UAM. To ensure that urban air traffic can serve passengers to the greatest extent while ensuring safety and generating revenue, there is an urgent need for a transportation scheduling plan based on safety considerations. The region of Beijing–Tianjin–Hebei was selected as the case study in this research. A real-time demand transportation scheduling model for a single day was constructed, with the total service population and total cost as objective functions, and safety intervals, eVTOL performance, and passenger maximum waiting time as constraints. A Joint Optimization of Cost and Scheduling Particle Swarm Optimization (JOCS-PSO) algorithm was utilized to obtain the optimal solution. The optimal solution obtained in this study can serve 138,610,575 passengers during eVTOLs’ entire lifecycle (15 years) with a total cost of CNY 368.57 hundred million, with the cost of CNY 265.9 per passenger. Although it is higher than the driving cost, it saves 1–1.5 h and thus has high cost effectiveness during rush hours. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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15 pages, 3382 KiB

Open AccessArticle

Adaptive Dynamic Programming with Reinforcement Learning on Optimization of Flight Departure Scheduling

by Hong Liu, Song Li, Fang Sun, Wei Fan, Wai-Hung Ip and Kai-Leung Yung

Aerospace 2024, 11(9), 754; https://doi.org/10.3390/aerospace11090754 - 13 Sep 2024

Cited by 1 | Viewed by 1615

Abstract

The intricacies of air traffic departure scheduling, especially when numerous flights are delayed, frequently impede the implementation of automated decision-making for scheduling. To surmount this obstacle, a mathematical model is proposed, and a dynamic simulation framework is designed to tackle the scheduling dilemma. An optimization control strategy is based on adaptive dynamic programming (ADP), focusing on minimizing the cumulative delay time for a cohort of delayed aircraft amidst congestion. This technique harnesses an approximation of the dynamic programming value function, augmented by reinforcement learning to enhance the approximation and alleviate the computational complexity as the number of flights increases. Comparative analyses with alternative approaches, including the branch and bound algorithm for static conditions and the first-come, first-served (FCFS) algorithm for routine scenarios, are conducted. Moreover, perturbation simulations of ADP parameters validate the method’s robustness and efficacy. ADP, when integrated with reinforcement learning, demonstrates time efficiency and reliability, positioning it as a viable solution for decision-making in departure management systems. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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16 pages, 5492 KiB

Open AccessArticle

Adaptive Scheduling Method for Passenger Service Resources in a Terminal

by Qifeng Mou, Qianyu Liang, Jie Tian and Xin Jing

Aerospace 2024, 11(7), 528; https://doi.org/10.3390/aerospace11070528 - 27 Jun 2024

Cited by 1 | Viewed by 1120

Abstract

To alleviate the tense situation of limited passenger service resources in the terminal and to achieve the matching of resource scheduling with the flight support process, the process–resource interdependent network is constructed according to its mapping relationship and the time-varying characteristics of the empirical network and network evolution conditions are analyzed. Then, node capacity, node load, and the cascading failure process are investigated, the impact of average service rate and service quality standard on queue length is considered, the node capacity model is constructed under the condition of resource capacity constraints, and the load-redistribution resource adaptive scheduling method based on cascading failure is proposed. Finally, the method’s effectiveness is verified by empirical analysis, the service efficiency is assessed using the total average service time and variance, and the network robustness is assessed using the proportion of maximum connected subgraph. The results indicate that the resource adaptive scheduling method is effective in improving service efficiency, and the average value of its measurement is smaller than that of the resource average allocation method by 0.069; in terms of the robustness improvement of the interdependent network, the phenomenon of re-failure after the load redistribution is significantly reduced. Full article

(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)

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