A quasi-dynamic air traffic assignment model for mitigating air traffic complexity and congestion for high-density UAM operations
In spite of the significant effects of COVID-19, UAM operations are still expected to grow
smoothly and healthily in the near future. If such dense UAM traffic relies on tactical planning …
smoothly and healthily in the near future. If such dense UAM traffic relies on tactical planning …
MAST-GNN: A multimodal adaptive spatio-temporal graph neural network for airspace complexity prediction
Airspace complexity is defined as an essential indicator to comprehensively measure the
safety of air traffic operational situations. A reliable prediction of airspace complexity can …
safety of air traffic operational situations. A reliable prediction of airspace complexity can …
Complexity optimal air traffic assignment in multi-layer transport network for Urban Air Mobility operations
Abstract Large numbers of Urban Air Mobility (UAM) vehicles are expected to operate in
urban airspace in the near future, exceeding the capacities of current airspace and Air Traffic …
urban airspace in the near future, exceeding the capacities of current airspace and Air Traffic …
Determination of air traffic complexity most influential parameters based on machine learning models
F Pérez Moreno, VF Gómez Comendador… - Symmetry, 2022 - mdpi.com
Today, aircraft demand is exceeding the capacity of the Air Traffic Control (ATC) system. As
a result, airspace is becoming a very complex environment to control. The complexity of …
a result, airspace is becoming a very complex environment to control. The complexity of …
Air traffic assignment for intensive urban air mobility operations
In high-density urban air mobility (UAM) operations, mitigating congestion and reducing
structural constraints are key challenges. Pioneering urban airspace design projects expect …
structural constraints are key challenges. Pioneering urban airspace design projects expect …
Deep learning architecture for UAV traffic-density prediction
A Alharbi, I Petrunin, D Panagiotakopoulos - Drones, 2023 - mdpi.com
The research community has paid great attention to the prediction of air traffic flows.
Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft …
Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft …
A multi-objective memetic algorithm with adaptive local search for airspace complexity mitigation
Airspace complexity is a paramount safety metric to measure the difficulty and effort required
to safely manage air traffic. The continuing growth in air traffic demand results in increasing …
to safely manage air traffic. The continuing growth in air traffic demand results in increasing …
Assuring Safe and Efficient Operation of UAV Using Explainable Machine Learning
A Alharbi, I Petrunin, D Panagiotakopoulos - Drones, 2023 - mdpi.com
The accurate estimation of airspace capacity in unmanned traffic management (UTM)
operations is critical for a safe, efficient, and equitable allocation of airspace system …
operations is critical for a safe, efficient, and equitable allocation of airspace system …
Predicting air traffic congested areas with long short-term memory networks
L Shi-Garrier, D Delahaye… - Fourteenth USA/Europe …, 2021 - enac.hal.science
The Extended ATC Planning (EAP) function aims at bridging the gap between Air Traffic
Flow & Capacity Management (ATFCM) and Air Traffic Control (ATC) by predicting air traffic …
Flow & Capacity Management (ATFCM) and Air Traffic Control (ATC) by predicting air traffic …
Sector entry flow prediction based on graph convolutional networks
Improving short-term air traffic flow prediction can help forecast demand and maximize
existing capacity by tactical air traffic flow management. Most existing studies in flow …
existing capacity by tactical air traffic flow management. Most existing studies in flow …