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Aerospace
Special Issues
AI-Driven Innovations in Air Traffic Management and Aviation Safety
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AI-Driven Innovations in Air Traffic Management and Aviation Safety

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2537

Special Issue Editor

Dr. Milad Memarzadeh

E-Mail Website
Guest Editor
Aviation Systems Division, NASA Ames Research Center, Moffett Field, Mountain View, CA 94035, USA
Interests: AI and machine learning in air traffic management; airspace operations; aviation safety; generative AI

Special Issue Information

Dear Colleagues,

The Special Issue “AI-Driven Innovations in Air Traffic Management and Aviation Safety” highlights emerging applications of artificial intelligence (AI) and machine learning (ML) in advancing air traffic management and airspace safety. With the rapid growth of both structured and unstructured aviation data, along with the increasing complexity of operations—especially with the integration of new airspace entrants—the need for automated, data-driven solutions has become essential. This issue invites contributions that combine aviation domain expertise with AI/ML methods, including deep learning and generative AI, to enhance the efficiency, resilience, and safety of modern airspace operations. Authors are invited to submit research articles or review manuscripts addressing (but not limited to) the following topics:

  • AI/ML as a decision support tool for the airspace operations;
  • Successful deployment of data-driven and automated technologies in the field operations;
  • Applications of generative AI in understanding the unstructured corpus of data in aviation;
  • Responsible AI techniques for deployment of data-driven tools in the domain.
  • Verification and validation of AI/ML software solutions;
  • AI as a service for air traffic management.

The focal topics listed above are not intended to exclude articles from additional related areas. We look forward to receiving your submissions and kindly invite you to address the Guest Editor in case of further questions.

Dr. Milad Memarzadeh
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • machine learning
  • artificial intelligence
  • generative AI
  • air traffic management
  • aviation safety
  • responsible AI
  • deep learning
  • decision support systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

24 pages, 2900 KB  
Article
A TCN-FEP Hybrid Model with Multi-Scale Feature Interaction Network for Departure Runway Occupation Time Prediction
by Zhousheng Huang, Zichao Yue, Weizhen Tang, Tianjiao Wang and Xu Zhang
Aerospace 2026, 13(6), 510; https://doi.org/10.3390/aerospace13060510 - 30 May 2026
Viewed by 192
Abstract
Currently, improving runway utilization under operational safety constraints has become a critical concern for small and medium airports. Existing research focuses primarily on landing-phase runway occupation time, while predictive studies on the takeoff phase remain limited. Analysis of 1749 Quick Access Recorder (QAR) [...] Read more.
Currently, improving runway utilization under operational safety constraints has become a critical concern for small and medium airports. Existing research focuses primarily on landing-phase runway occupation time, while predictive studies on the takeoff phase remain limited. Analysis of 1749 Quick Access Recorder (QAR) records from ten airports reveals that departure runway occupation time is strongly correlated with ground speed at liftoff (0.72) and airport elevation (0.67) but weakly correlated with aircraft weight and meteorological conditions, providing guidance for feature engineering. To address the prediction of departure runway occupation time, this study proposes a TCN-FEP hybrid model. The model employs an enhanced Temporal Convolutional Network (TCN) module with multi-scale convolutions (kernel sizes 3, 5, 7) and dilated convolutions (rates 2, 4, 8) to capture multi-scale feature interactions, alongside a Feature Enhancement Projection (FEP) module that maps local features into a high-dimensional latent space for implicit relationship mining and global information integration. Experimental results demonstrate that the proposed TCN-FEP model achieves an MSE of 90.20, RMSE of 9.49, MAE of 5.84 s, MAPE of 3.80%, and R2 of 0.97, outperforming Informer (MSE 117.95), Longformer (MSE 132.11), XGBoost (MSE 92.30), and LightGBM (MSE 91.45). Under 5% outlier injection, MSE increases by 7.9%, compared to 24.3% for LSTM and 18.4% for Informer. With 94% of prediction errors within ±5 s, the model’s accuracy may offer a useful reference for runway resource optimization at small and medium airports. Full article
(This article belongs to the Special Issue AI-Driven Innovations in Air Traffic Management and Aviation Safety)
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15 pages, 3643 KB  
Article
Responsible AI for Air Traffic Management: Application to Runway Configuration Assistance Tool
by Milad Memarzadeh, Zili Wang, Farzan Masrour Shalmani, Pouria Razzaghi and Krishna M. Kalyanam
Aerospace 2025, 12(10), 872; https://doi.org/10.3390/aerospace12100872 - 27 Sep 2025
Viewed by 1607
Abstract
The complexity and magnitude of airspace operations are ever increasing, which creates new challenges for air traffic controllers. With the increase in the volume of operations, the size of available data is also increasing. Data-driven AI solutions can provide actionable information for complex [...] Read more.
The complexity and magnitude of airspace operations are ever increasing, which creates new challenges for air traffic controllers. With the increase in the volume of operations, the size of available data is also increasing. Data-driven AI solutions can provide actionable information for complex decision-making processes that controllers face and assist them in improving the efficiency and safety of operations. However, for such solutions to be trusted by the users and stakeholders, they need to undergo a comprehensive validation process. In this paper, the literature in the development of responsible AI is studied and a subset of the framework is applied to an AI tool proposed for airport runway configuration management. The focus of this study is tackle two main challenges: (1) detection and mitigation of existing bias in the training data and the trained AI tool; and (2) quantification and improvement of the AI tool’s robustness to potential sources of noise in the data. We validate several responsible AI techniques using historical data and simulation studies on three major US airports and quantify their effectiveness in reducing the detected bias and also improving the robustness of the model to adversarial noise in the input data. Full article
(This article belongs to the Special Issue AI-Driven Innovations in Air Traffic Management and Aviation Safety)
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