Deep reinforcement learning (DRL) has been widely adopted recently for its ability to solve
decision-making problems that were previously out of reach due to a combination of …

Reinforcement learning (RL) has emerged as a powerful tool for addressing complex
decision making problems in various domains, including aviation. This paper provides a …

There has been significant interest in integrating sentiment analysis with graph neural
networks (GNNs) for stock prediction tasks. This article thoroughly reviews the application of …

With the continuous growth in the air transportation demand, air traffic controllers will have to
handle increased traffic and consequently, more potential conflicts. This gives rise to the …

Dense and complex air traffic requires higher levels of automation than those exhibited by
tactical conflict detection and resolution (CD&R) tools that air traffic controllers (ATCOs) use …

Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned
Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict …

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 …

Reinforcement learning (RL) techniques are under investigation for resolving conflict in air
traffic management (ATM), exploiting their computational capabilities and ability to cope with …

Reinforcement learning (RL) techniques have been studied for solving the conflict resolution
(CR) problem in air traffic management, leveraging their potential for computation and ability …

Optimizing flight trajectories emerges as a viable strategy to mitigate the non-CO 2 climate
impacts of aviation. However, integrating individually optimized trajectories into the air traffic …