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

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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 December 2025) | Viewed by 7847

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Special Issue Editors

Prof. Dr. Michael Schultz

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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
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Dr. Pham Duc Thinh

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Guest Editor

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

Dr. Alexei Sharpanskykh

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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 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

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 (10 papers)

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Research

23 pages, 3413 KB

Open AccessArticle

DDA-SIM-ATT: A Synergistic Multi-Module Fusion Model for High-Precision Prediction of Departure Flight Taxi-Out Time

by Yue Lu, Yanzhi Li, Qingwei Zhong, Jifei Zhong, Yingxue Yu and Yuxin Zhang

Aerospace 2026, 13(4), 314; https://doi.org/10.3390/aerospace13040314 - 27 Mar 2026

Viewed by 268

Abstract

Accurate prediction of departure flight taxi-out time is critical for enhancing airport surface efficiency and reducing flight delays. However, existing methods often struggle with data sparsity, inadequate representation of complex spatio-temporal interactions among aircraft, and imbalanced sample distributions. To address these challenges, this paper proposes a synergistic multi-module fusion model named DDA-SIM-ATT-CatBoost. The model integrates three core modules: a Dynamic Data Augmentation (DDA) module that expands the training distribution through operationally consistent perturbations to mitigate data imbalance; a Similarity Theory (SIM) module employing K-Prototypes clustering and Mahalanobis distance to achieve precise matching of historical operational patterns; and an Attention Mechanism (ATT) module that dynamically recalibrates feature weights to emphasize critical influencing factors. These modules work synergistically to provide a robust and discriminative input representation for the CatBoost regressor, which excels at handling categorical features and complex nonlinearities. Using real-world departure data from a major hub airport, the proposed model achieves prediction accuracies of 74.57%, 89.12%, and 97.76% within error margins of ±120 s, ±180 s, and ±300 s, respectively, with a Mean Absolute Percentage Error (MAPE) of 10.34%, Mean Absolute Error (MAE) of 87.55 s, and Root Mean Square Error (RMSE) of 125.61 s. Ablation studies validate the positive contribution and synergistic effect of each module, while comparative experiments demonstrate that our model significantly outperforms baseline models such as XGBoost and Random Forest. The DDA-SIM-ATT framework provides a generalizable and high-precision solution for taxi-out time prediction, offering reliable decision support for airport surface operations. Full article

(This article belongs to the Special Issue AI, Machine Learning and Automation for Air Traffic Control (ATC))

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22 pages, 5283 KB

Open AccessArticle

Air Traffic Noise Prediction Method Based on Machine Learning Driven by Quick Access Recorder

by Zhixing Tang, Yijie Fan, Xuanting Chen, Xinyan Shi, Zhaolun Niu, Yuming Zhong, Meng Jia and Xiaowei Tang

Aerospace 2026, 13(3), 208; https://doi.org/10.3390/aerospace13030208 - 24 Feb 2026

Viewed by 362

Abstract

Accurate prediction of air traffic noise is critical for advancing environmentally sustainable operations in high density terminal areas. Conventional noise prediction models often exhibit significant limitations due to discrepancies between actual and nominal flight trajectories. To overcome this challenge, this study introduces a probabilistic framework that integrates real air-traffic-flow data to generate realistic flight trajectory distributions. The proposed methodology extracts key operational features—including trajectory distribution probabilities, and essential trajectory operation features—within a machine learning architecture. Furthermore, we develop a dedicated air traffic noise prediction model for clustered flight paths that explicitly incorporates traffic flow patterns, enabling high-fidelity simulation of noise propagation under actual air traffic operation. The framework is validated using a QAR (Quick Access Recorder) dataset from the terminal area of Changsha Huanghua International Airport. Experimental results demonstrate the model’s high predictive accuracy for both air traffic noise distribution and its influence, coupled with computational efficiency and practical applicability. The findings indicate that the proposed approach successfully addresses the challenge of predicting air traffic noise from divergent, real-world flight trajectories, offering a robust method for supporting noise-abatement strategies and sustainable aviation-planning initiatives. Full article

(This article belongs to the Special Issue AI, Machine Learning and Automation for Air Traffic Control (ATC))

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32 pages, 6063 KB

Open AccessArticle

DBO-PSO: Mechanism Modeling Method for the E-ECS of B787 Aircraft Based on Adaptive Hybrid Optimization

by Yanfei Han, Zixuan Bai, Fuchao Chen, Tong Mu, Lunlong Zhong and Renbiao Wu

Aerospace 2026, 13(2), 195; https://doi.org/10.3390/aerospace13020195 - 18 Feb 2026

Viewed by 366

Abstract

In view of the highly coupled, time-varying, and susceptible to differences in aircraft configuration of the Boeing 787 Electric Environmental Control System (E-ECS), a simplified mechanism model based on effectiveness-number of transfer units is proposed. Firstly, considering the influence of differences in aircraft configuration, part number, and optional components, a heat conduction correction coefficient is introduced to adjust the calculation process of heat exchange efficiency. Secondly, the steady-state characteristic equation of the electric compressor/turbine is established by utilizing the principle of isentropic work. Then, the outlet temperature value of the water removal component is calculated by using secondary heat recovery technology. Finally, to solve the problem of easily getting stuck in local optima during high-dimensional parameter identification, an adaptive hybrid optimization algorithm combining Dung Beetle Optimization (DBO) with mutation operator and Particle Swarm Optimization (PSO) is proposed. The experimental results show that the proposed mechanism model can achieve dynamic representation of the outlet temperature of each component of E-ECS under different aircraft stages. The DBO-PSO algorithm has a fast convergence speed and a low probability of falling into local optima. The temperature values calculated by the model have high computational accuracy, which can provide reliable data support for component level E-ECS health monitoring and early fault warning. Full article

(This article belongs to the Special Issue AI, Machine Learning and Automation for Air Traffic Control (ATC))

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16 pages, 1641 KB

Open AccessArticle

Edge-Based GNN for Network Delay Prediction Enhanced by Flight Connectivity

by Zhixing Tang, Zhaolun Niu, Xuanting Chen, Shan Huang and Xinping Zhu

Aerospace 2026, 13(2), 161; https://doi.org/10.3390/aerospace13020161 - 10 Feb 2026

Viewed by 449

Abstract

Accurate prediction of network-wide delay is crucial for air traffic management and passenger service. However, the inherent complexity of large-scale air traffic networks, with their dense interconnectivity and multi-dimensional operational dynamics such as flight connectivity, makes this task highly challenging. While Graph Neural Networks (GNNs) offer a promising framework, prevailing models are constrained by a “node → edge → node” representation paradigm, which fails to preserve the high-fidelity, edge-centric operational data that encodes delay propagation paths. To overcome this limitation, we propose a novel edge-based GNN. Our approach begins with a flight-connectivity-informed delay characterization, introducing delay width and delay strength as core metrics. The model implements an “edge → node” message-passing mechanism that explicitly encodes inbound and outbound flights, enabling direct learning of delay diffusion dynamics along air routes. Extensive experiments on real-world datasets demonstrate that our method outperforms state-of-the-art benchmarks, achieving the lowest RMSE, MAE, and MSE. A layered performance analysis reveals a key strength: the model delivers superior accuracy at major hub airports—which are critical to network performance—while maintaining robust precision at small-to-medium-sized airports. This balanced capability underscores the model’s practical utility and its enhanced capacity to capture the essential spatial–temporal dependencies governing delay propagation across diverse airport tiers. Full article

(This article belongs to the Special Issue AI, Machine Learning and Automation for Air Traffic Control (ATC))

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22 pages, 3108 KB

Open AccessArticle

Cell-Based Optimization of Air Traffic Control Sector Boundaries Using Traffic Complexity

by César Gómez Arnaldo, José María Arroyo López, Raquel Delgado-Aguilera Jurado, María Zamarreño Suárez, Javier Alberto Pérez Castán and Francisco Pérez Moreno

Aerospace 2026, 13(1), 101; https://doi.org/10.3390/aerospace13010101 - 20 Jan 2026

Viewed by 356

Abstract

The increasing demand for air travel has intensified the need for more efficient air traffic management (ATM) solutions. One of the key challenges in this domain is the optimal sectorization of airspace to ensure balanced controller workload and operational efficiency. Traditional airspace sectors, typically static and based on historical flow patterns, often fail to adapt to evolving traffic complexity, resulting in imbalanced workload distribution and reduced system performance. This study introduces a novel methodology for optimizing ATC sector geometries based on air traffic complexity indicators, aiming to enhance the balance of operational workload across sectors. The proposed optimization is formulated in the horizontal plane using a two-dimensional cell-based airspace representation. A graph-partitioning optimization model with spatial and operational constraints is applied, along with a refinement step using adjacent-cell pairs to improve geometric coherence. Tested on real data from Madrid North ACC, the model achieved significant complexity balancing while preserving sector shapes in a real-world case study based on a Spanish ACC. This work provides a methodological basis to support static and dynamic airspace design and has the potential to enhance ATC efficiency through data-driven optimization. Full article

(This article belongs to the Special Issue AI, Machine Learning and Automation for Air Traffic Control (ATC))

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19 pages, 1187 KB

Open AccessArticle

Dual-Pipeline Machine Learning Framework for Automated Interpretation of Pilot Communications at Non-Towered Airports

by Abdullah All Tanvir, Chenyu Huang, Moe Alahmad, Chuyang Yang and Xin Zhong

Aerospace 2026, 13(1), 32; https://doi.org/10.3390/aerospace13010032 - 28 Dec 2025

Viewed by 499

Abstract

Accurate estimation of aircraft operations, such as takeoffs and landings, is critical for airport planning and resource allocation, yet it remains particularly challenging at non-towered airports, where no dedicated surveillance infrastructure exists. Existing solutions, including video analytics, acoustic sensors, and transponder-based systems, are often costly, incomplete, or unreliable in environments with mixed traffic and inconsistent radio usage, highlighting the need for a scalable, infrastructure-free alternative. To address this gap, this study proposes a novel dual-pipeline machine learning framework that classifies pilot radio communications using both textual and spectral features to infer operational intent. A total of 2489 annotated pilot transmissions collected from a U.S. non-towered airport were processed through automatic speech recognition (ASR) and Mel-spectrogram extraction. We benchmarked multiple traditional classifiers and deep learning models, including ensemble methods, long short-term memory (LSTM) networks, and convolutional neural networks (CNNs), across both feature pipelines. Results show that spectral features paired with deep architectures consistently achieved the highest performance, with F1-scores exceeding 91% despite substantial background noise, overlapping transmissions, and speaker variability These findings indicate that operational intent can be inferred reliably from existing communication audio alone, offering a practical, low-cost path toward scalable aircraft operations monitoring and supporting emerging virtual tower and automated air traffic surveillance applications. Full article

(This article belongs to the Special Issue AI, Machine Learning and Automation for Air Traffic Control (ATC))

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19 pages, 3961 KB

Open AccessArticle

Risk-Aware Multi-Horizon Forecasting of Airport Departure Flow Using a Patch-Based Time-Series Transformer

by Xiangzhi Zhou, Shanmei Li and Siqing Li

Aerospace 2025, 12(12), 1107; https://doi.org/10.3390/aerospace12121107 - 15 Dec 2025

Viewed by 511

Abstract

Airport traffic flow prediction is a basic requirement for air traffic management. Building an effective airport traffic flow prediction model helps reveal how traffic demand evolves over time and supports short-term planning. At the same time, a large amount of air traffic data supports using deep learning to learn traffic patterns with stable and accurate performance. In practice, airports need forecasts at short time intervals and need to know the departure flow and its uncertainty 1–2 h in advance. To meet this need, we treat airport departure flow prediction as a multi-step probabilistic forecasting problem on a multi-airport dataset that is organized by airport and time. Scheduled departure counts, recent taxi-out time statistics (P50/P90 over 30- and 60-minute windows), and calendar variables are put on the same time scale and standardized separately for each airport. Based on these data, we propose an end-to-end multi-step forecasting method built on PatchTST. The method uses patch partitioning and a Transformer encoder to extract temporal features from the past 48 h of multivariate history and directly outputs the 10th, 50th, and 90th percentile forecasts of departure flow for each 10 min step in the next 120 min. In this way, the model provides both point forecasts and prediction intervals. Experiments were conducted on 80 airports with the highest departure volumes, using April–July for training, August for validation, September for testing, and October for robustness evaluation. The results show that at a 10 min interval, the model achieves an MAE of 0.411 and an RMSE of 0.713 on the test set. The error increases smoothly with the forecast horizon and remains stable within the 60–120 min range. When the forecasts are aggregated to 1 h intervals in time or aggregated by airport clusters in space, the point forecast errors decrease further, and the average empirical coverage is 0.78 and the width of the percentile-based intervals is 1.29, which can meet the risk-awareness requirements of tactical operations management. The proposed method is relatively simple and also provides a unified modeling framework for later including external factors such as weather, runway configuration, and operational procedures, and for applications across different airports and years. Full article

(This article belongs to the Special Issue AI, Machine Learning and Automation for Air Traffic Control (ATC))

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25 pages, 2585 KB

Open AccessArticle

Flight Conflict Network Resolution Based on Weighted Cycle Ratio and LDPSO

by Hanchen Xie, Xiangxi Wen, Zekun Wang, Minggong Wu and Shuangfeng Li

Aerospace 2025, 12(12), 1042; https://doi.org/10.3390/aerospace12121042 - 24 Nov 2025

Viewed by 477

Abstract

Aiming at the characterized networking and clustering of flight conflicts in high-density airspace under free-flight mode, this paper proposes a network-based conflict resolution method that integrates complex network theory and an improved particle swarm optimization (PSO) algorithm. First, a tunable Mixed Indicator (MI) is formulated by combining the Weighted Cycle Ratio (WCR) and node strength (NS) to accurately identify critical nodes within the flight conflict network. Subsequently, based on the circle structure, a Circle Complexity (CC) metric is proposed as a network structural indicator. Furthermore, the Analytic Hierarchy Process (AHP) is employed to incorporate network efficiency, the number of conflicting nodes, and robustness into a comprehensive performance evaluation index. On this basis, the Leader–Follower Particle Swarm Optimization (LDPSO) algorithm is applied to collaboratively optimize the discrete codes representing heading, speed, and composite resolution strategies, thereby achieving efficient resolution for multi-aircraft conflicts. Simulation experiments demonstrate that the proposed MI significantly outperforms traditional centrality metrics in identifying critical nodes. The LDPSO algorithm proves superior to comparative algorithms, including the standard PSO, Genetic Algorithm (GA), and Atomic Orbital Optimization (AOO), in terms of conflict resolution rate, convergence speed, and solution quality. It can achieve the rapid disintegration of large-scale conflict networks with relatively low computational cost, providing a feasible solution for real-time conflict resolution in dynamic airspace. Full article

(This article belongs to the Special Issue AI, Machine Learning and Automation for Air Traffic Control (ATC))

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38 pages, 2865 KB

Open AccessArticle

A Deep Learning Approach to Accelerate MILP Solvers with Application to the Aircraft Routing Problem

by Haiwen Xu, Yanbin Pan and Chenglung Wu

Aerospace 2025, 12(11), 1027; https://doi.org/10.3390/aerospace12111027 - 20 Nov 2025

Viewed by 1472

Abstract

Large-scale Aircraft Routing Problems (ARPs) remain challenging for standard Branch-and-Bound (B&B) and modern Mixed-Integer Linear Programming (MILP) solvers due to vast search spaces and instance-agnostic heuristics. Methods: We develop a learning-to-accelerate framework centered on a Two-Stage Route Selection Graph Convolutional Network (TRS-GCN) that predicts the importance of flight string variables using structural, LP relaxation, and operational features. Predictions are injected into the solver via three mechanisms: an ML-guided feasibility pump for warm starts, static problem reduction through predictive pruning, and a dynamic hybrid branching rule that blends ML scores with pseudo-costs. A synthetic generator produces realistic ARP instances with seed solutions for robust training. Results: On large instances derived from Bureau of Transportation Statistics data, TRS-GCN-guided static reduction safely pruned up to 49.2% of variables and reduced the time to reach the baseline solver’s 12-h target objective by 52.4%. The dynamic search strategy also yielded more incumbents within fixed time budgets compared with baselines. Conclusion: Integrating TRS-GCN into MILP workflows improves search efficiency for ARPs, offering complementary gains from warm-starting, pruning, and branching without changing the underlying optimality guarantees. Full article

(This article belongs to the Special Issue AI, Machine Learning and Automation for Air Traffic Control (ATC))

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24 pages, 6794 KB

Open AccessArticle

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

Cited by 2 | Viewed by 1504

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 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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