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Aerospace
Special Issues
Future Airspace and Air Traffic Management Design
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Future Airspace and Air Traffic Management Design

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 4459

Special Issue Editors

Dr. Sebastian Schier-Morgenthal

E-Mail Website
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

E-Mail Website
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

E-Mail Website
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (5 papers)

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Research

26 pages, 2375 KiB  
Article
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 478
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 [...] Read more.
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  
Article
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
Viewed by 640
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 [...] Read more.
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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27 pages, 3282 KiB  
Article
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 726
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 [...] Read more.
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  
Article
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
Viewed by 772
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. [...] Read more.
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  
Article
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
Viewed by 734
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 [...] Read more.
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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