Optimization and Machine Learning-Based Methods in Air Traffic Management and Aeronautical Domains
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 15
Special Issue Editor
Special Issue Information
Dear colleagues,
In the domain of Air Traffic Management and Aeronautics, optimization and machine learning techniques are crucial for addressing the challenges posed by increasing air traffic and the growing demands for safety, efficiency, and environmental sustainability. Traditional approaches face limitations in managing complex air traffic systems. Optimization methods, including mathematical programming and heuristic algorithms, are applied to optimize airspace resource allocation, flight path planning, and airport ground traffic scheduling, aiming to enhance airspace capacity, reduce flight delays, and lower operational costs. Machine learning approaches, such as supervised learning, unsupervised learning, and deep learning, leverage vast amounts of historical data to uncover underlying patterns and trends. They enable accurate air traffic flow prediction, flight risk assessment and early warning, and intelligent aircraft performance monitoring, thereby providing robust decision-making support. The integration of optimization and machine learning, such as using machine learning to model complex air traffic systems and then applying optimization algorithms to solve them, offers new pathways for tackling intricate problems in Air Traffic Management and Aeronautics, driving the aviation industry toward greater safety, efficiency, and sustainability.
Dr. Yicheng Zhang
Guest Editor
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Keywords
- air traffic management
- optimization
- machine learning
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