About this Research Topic
The global air transportation system currently faces numerous challenges. Some examples are the economic impacts of the COVID-19 pandemic, the pressing need to manage capacity constraints and congestion, the stringent performance targets set by regulatory agencies, the growing societal environmental concerns and the emergence of new vehicles and concepts (e.g., Unmanned Aircraft Systems, electric vehicles, Advanced Air Mobility). The availability of large-scale aviation data along with the recent advances and popularity of Artificial Intelligence (AI) and Data Analytics (DA) has been demonstrating a great potential to address these challenges towards enabling the air transportation system to evolve and scale sustainably. Large amounts of aviation data are continuously generated, such as flight schedule data, aircraft surveillance data, flight recorder data, weather data, behavioral data (e.g., air traffic controllers, pilots, passengers, etc), and will become increasingly available with the implementation of new digital infrastructure. At the same time, advanced AI and DA techniques in fields such as machine learning, deep learning, natural language processing and computer vision are a powerful tool to exploit this big data potential for the discovery, modeling, prediction and control of complex patterns of system behavior, leading to several opportunities to enhance airline, airport and Air Traffic Management (ATM) planning and operations processes.
The goal of this Research Topic is to compile innovative applications of AI and DA in the planning, design and management of future air transportation systems. The Research Topic will focus on presenting research efforts related to the use of big aviation data and state-of-the-art methods and algorithms in the fields of AI and DA towards the development of novel decision support and automation capabilities for airline, airport and ATM systems. The focus is also on discussing their practical implementation challenges and potential contributions to enhancing the multi-dimensional air transportation system performance (safety, efficiency, capacity, predictability, equity, etc) and achieving a more sustainable aviation sector.
Potential topics of interest include:
• Air travel demand modeling and forecasting
• Air traffic operational performance analysis
• Airspace and airport capacity estimation and allocation
• Air traffic flow management
• Collaborative decision-making
• Flight trajectory planning
• Air Traffic Management for Advanced Air Mobility and Unmanned Aircraft Systems
• Autonomy in safety-critical systems
• Airport level of service and passenger experience analysis
• Pricing and revenue management
• Airport and airline resource management
• Proactive airline safety management
• Aviation emissions/noise modeling and environmental impact analysis
The goal of this Research Topic is to compile innovative applications of AI and DA in the planning, design and management of future air transportation systems. The Research Topic will focus on presenting research efforts related to the use of big aviation data and state-of-the-art methods and algorithms in the fields of AI and DA towards the development of novel decision support and automation capabilities for airline, airport and ATM systems. The focus is also on discussing their practical implementation challenges and potential contributions to enhancing the multi-dimensional air transportation system performance (safety, efficiency, capacity, predictability, equity, etc) and achieving a more sustainable aviation sector.
Potential topics of interest include:
• Air travel demand modeling and forecasting
• Air traffic operational performance analysis
• Airspace and airport capacity estimation and allocation
• Air traffic flow management
• Collaborative decision-making
• Flight trajectory planning
• Air Traffic Management for Advanced Air Mobility and Unmanned Aircraft Systems
• Autonomy in safety-critical systems
• Airport level of service and passenger experience analysis
• Pricing and revenue management
• Airport and airline resource management
• Proactive airline safety management
• Aviation emissions/noise modeling and environmental impact analysis
Keywords: Data Analytics, Artificial Intelligence, Air Transportation, Environmental, Airports
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.
