About this Research Topic
We are delighted to launch this Research Topic in conjunction with the track on Ai & Aviation hosted by AMLD (Applied Machine Learning Days), to welcome contributions presented at the 2021 virtual event (or past sessions) in the form of extended papers.
Civil aviation enables flight services through networked operations provided by several players. In this fascinating industry, many different organizations and businesses work closely together to facilitate the safe and efficient transportation of passengers and cargo throughout the world. A broad range of companies is involved in the aviation industry, from aviation-specific organizations, such as airlines, airports, ground handlers, and manufacturers to various forms of suppliers with contrasting degrees of maturity (startups to established enterprises). They operate in a complex and intricate system in which large amounts of data are gathered and processed to ensure safe, time- and cost-efficient, reliable, and customer-friendly services.
Since the beginning of large-scale civil aviation, the system has become increasingly intricate, amplifying the need for more elaborate steering and optimization methods. The classical operation management methods used until today are no longer sufficient to fulfill the needs of stakeholders and passengers.
Artificial Intelligence (AI) methods such as Machine Learning (ML), Deep Learning (DL) and others, are beginning to influence the industry and become key methodologies in many research projects and new industrial developments. Access to large amounts of data simplifies machine learning and enables us to solve problems that were previously unthinkable. The application of AI to aviation can help address the current challenges related to capacity, forecasting, Air Traffic Management (ATM), delay management, the environment, connectivity, and safety.
In addition, the aviation industry is characterized by severe resource limitations in the form of infrastructure and personnel, while the number of passengers is steadily increasing. To sustain growth, the aviation industry must find ways to optimize existing infrastructure and processes, and AI can be instrumental in this endeavor.
The aim is to touch on the following five core topics from aviation within this article collection: Flight Operations, Airports and Ground Operations, Technics and Flight Safety, Dynamic Pricing, and Air Traffic Management. We encourage you, where the paper scope falls within at least one of the below categories, to submit a paper for the journal's selection.
• AI and ML applications in aviation,
• Reinforcement learning,
• Operation in aviation,
• (AI)-based airfield monitoring system,
• Capacity forecasting,
• Predictive maintenance in aviation,
• (AI)-based air traffic control,
• AI and safety,
• AI aircraft allocation,
• Stand allocation,
• (AI)-based unmanned air system,
• Intelligence planning,
• (AI)-based Traffic Prediction Improvements (TPI),
• (AI)-based Air Traffic Flow Capacity Management (ATFCM),
• Automated Flight Plan Processing,
• (AI)-based Network Management.
Civil aviation enables flight services through networked operations provided by several players. In this fascinating industry, many different organizations and businesses work closely together to facilitate the safe and efficient transportation of passengers and cargo throughout the world. A broad range of companies is involved in the aviation industry, from aviation-specific organizations, such as airlines, airports, ground handlers, and manufacturers to various forms of suppliers with contrasting degrees of maturity (startups to established enterprises). They operate in a complex and intricate system in which large amounts of data are gathered and processed to ensure safe, time- and cost-efficient, reliable, and customer-friendly services.
Since the beginning of large-scale civil aviation, the system has become increasingly intricate, amplifying the need for more elaborate steering and optimization methods. The classical operation management methods used until today are no longer sufficient to fulfill the needs of stakeholders and passengers.
Artificial Intelligence (AI) methods such as Machine Learning (ML), Deep Learning (DL) and others, are beginning to influence the industry and become key methodologies in many research projects and new industrial developments. Access to large amounts of data simplifies machine learning and enables us to solve problems that were previously unthinkable. The application of AI to aviation can help address the current challenges related to capacity, forecasting, Air Traffic Management (ATM), delay management, the environment, connectivity, and safety.
In addition, the aviation industry is characterized by severe resource limitations in the form of infrastructure and personnel, while the number of passengers is steadily increasing. To sustain growth, the aviation industry must find ways to optimize existing infrastructure and processes, and AI can be instrumental in this endeavor.
The aim is to touch on the following five core topics from aviation within this article collection: Flight Operations, Airports and Ground Operations, Technics and Flight Safety, Dynamic Pricing, and Air Traffic Management. We encourage you, where the paper scope falls within at least one of the below categories, to submit a paper for the journal's selection.
• AI and ML applications in aviation,
• Reinforcement learning,
• Operation in aviation,
• (AI)-based airfield monitoring system,
• Capacity forecasting,
• Predictive maintenance in aviation,
• (AI)-based air traffic control,
• AI and safety,
• AI aircraft allocation,
• Stand allocation,
• (AI)-based unmanned air system,
• Intelligence planning,
• (AI)-based Traffic Prediction Improvements (TPI),
• (AI)-based Air Traffic Flow Capacity Management (ATFCM),
• Automated Flight Plan Processing,
• (AI)-based Network Management.
Keywords: AI, machine learning, neural networks, deep learning, reinforcement learning, operation in aviation
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
