AI, Machine Learning and Automation for Air Traffic Control (ATC)

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 481

Special Issue Editors


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Guest Editor
Institute of Flight Systems, Bundeswehr University Munich, 85577 Neubiberg, Germany
Interests: air transportation; data-driven and model-based environments; predictive analysis; integrated airspace and airport management
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Air Traffic Management Research Institute, Singapore, Singapore
Interests: aerospace engineering; computer science and engineering

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Guest Editor
Aerospace Engineering Department, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands
Interests: artificial intelligence techniques for air transport; multiagent systems; complex sociotechnical systems; distributed planning and scheduling; airports and airlines; urban air mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI), machine learning (ML), and automation into air traffic control (ATC) has significant potential to improve the performance and safety of these systems. As ATC systems are encountering increasing complexity, this Special Issue, titled “AI, Machine Learning and Automation for Air Traffic Control (ATC)”, invites contributions from researchers and scientists focused on developing robust, data-driven approaches to these challenges.

We invite contributions that explore how these advanced technologies can be integrated into ATC to address current challenges and build more resilient and adaptive systems.

Potential topics that could be covered by these contributions include the following:

  • AI-based predictive analytics for traffic flow and capacity management: developing methods to forecast and optimize air traffic in dynamic environments.
  • Human-in-the-loop decision-making versus full or partial automation: evaluating the balance between automated systems and human oversight in ATC.
  • Real-time conflict detection and resolution algorithms: advancing computational techniques for identifying and resolving in-flight conflicts promptly.
  • Safety assurance and ethical considerations in AI-driven ATC: addressing the risks, ethical challenges, and safety protocols associated with integrating AI into critical control systems.
  • Next-generation decision support tools (e.g., machine learning pipelines): designing robust tools that aid controllers in making data-driven decisions.

We welcome original research papers, case studies, and review articles that examine these topics from technical, operational, ethical, and safety perspectives. Contributions that combine theoretical frameworks with practical implementations—offering actionable solutions to real-world ATC challenges—are especially encouraged.

Prof. Dr. Michael Schultz
Dr. Pham Duc Thinh
Dr. Alexei Sharpanskykh
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

  • AI-based predictive analytics for traffic flow and capacity management
  • human-in-the-loop decision-making
  • real-time conflict detection and resolution algorithms
  • safety assurance and ethical considerations in AI-driven ATC
  • next-generation decision support tools

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Published Papers (1 paper)

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Research

24 pages, 6794 KiB  
Article
A Multi-Scale Airspace Sectorization Framework Based on QTM and HDQN
by Qingping Liu, Xuesheng Zhao, Xinglong Wang, Mengmeng Qin and Wenbin Sun
Aerospace 2025, 12(6), 552; https://doi.org/10.3390/aerospace12060552 - 17 Jun 2025
Viewed by 217
Abstract
Airspace sectorization is an effective approach to balance increasing air traffic demand and limited airspace resources. It directly impacts the efficiency and safety of airspace operations. Traditional airspace sectorization methods are often based on fixed spatial scales, failing to fully consider the complexity [...] Read more.
Airspace sectorization is an effective approach to balance increasing air traffic demand and limited airspace resources. It directly impacts the efficiency and safety of airspace operations. Traditional airspace sectorization methods are often based on fixed spatial scales, failing to fully consider the complexity and interrelationships of airspace partitioning across different spatial scales. This makes it challenging to balance large-scale airspace management with local dynamic demands. To address this issue, a multi-scale airspace sectorization framework is proposed, which integrates a multi-resolution grid system and a hierarchical deep reinforcement learning algorithm. First, an airspace grid model is constructed using Quaternary Triangular Mesh (QTM), along with an efficient workload calculation model based on grid encoding. Then, a sector optimization model is developed using hierarchical deep Q-network (HDQN), where the top-level and bottom-level policies coordinate to perform global airspace control area partitioning and local sectorization. The use of multi-resolution grids enhances the interaction efficiency between the reinforcement learning model and the environment. Prior knowledge is also incorporated to enhance training efficiency and effectiveness. Experimental results demonstrate that the proposed framework outperforms traditional models in both computational efficiency and workload balancing performance. Full article
(This article belongs to the Special Issue AI, Machine Learning and Automation for Air Traffic Control (ATC))
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