This paper presents a method developed to predict the flight-time employed by a drone to
complete a planned path adopting a machine-learning-based approach. A generic path is …

With the increased use of intelligent Decision Support Tools in Air Traffic Management
(ATM) and inclusion of non-traditional entities, regulators and end users need assurance …

Future high traffic densities with drone operations are expected to exceed the number of
aircraft that current air traffic control procedures can control simultaneously. Despite …

Conflict detection and resolution is one of the main topics in air traffic management.
Traditional approaches to this problem use all the available information to predict future …

Artificial Intelligence provides both opportunities and considerable challenges to the
continued growth of Air Traffic Control (ATC) services. This paper presents a study where a …

U-Space services will be used by millions of Unmanned Aerial Vehicles (UAVs) also called
drones in the near future. To respond to growing demand, urban airspace needs more …

The current aim in response to the anticipated growth in air traffic demand is to expand
airspace capacity. However, this poses a challenge, as many airspace volumes are nearing …

Air traffic demand has increased at an unprecedented rate in the last decade (albeit
interrupted by the COVID pandemic), but capacity has not increased at the same rate …

Abstract This article evaluates Machine Learning (ML) classification techniques applied to
air-traffic conflict detection. The methodology develops a static approach in which the conflict …

Abstract Machine learning (ML) is the ability of computers to learn mathematical relations to
make predictions based on historical data. In recent years, ML has evolved rapidly and has …