Air Traffic

Air traffic management research focuses on improving safety and efficiency in increasingly complex airspace, driven by rising passenger numbers and the integration of unmanned aerial vehicles. Current research utilizes machine learning, particularly deep learning models like transformers and recurrent neural networks, along with graph-based methods, to address challenges such as flight delay prediction, conflict resolution, and workload assessment for air traffic controllers. These advancements aim to automate tasks, enhance decision-making, and ultimately improve the safety and efficiency of air travel, impacting both the scientific understanding of complex systems and the practical operation of air traffic control.