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 213
Special Issue Editors
Interests: air transportation; data-driven and model-based environments; predictive analysis; integrated airspace and airport management
Special Issues, Collections and Topics in MDPI journals
Interests: aerospace engineering; computer science and engineering
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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