Charting the Trajectory of Artificial Intelligence Agents in Air Traffic Control

Abstract

The aim of the study was to chart the evolution and the current research landscape of artificial intelligence (AI) agents within the domain of air traffic control. Using bibliometric analysis, the study identified major research trends and gaps and provided a consolidated reference for researchers. The analysis included 58 publications retrieved from the Web of Science Core Collection. The findings showed that the earliest publication in the dataset appeared in 1998 and that research output increased noticeably after 2022. Publication output peaked in 2025, with 16 articles, indicating growing scholarly interest in AI agents for air traffic control. Citation analysis identified Agogino and Tumer’s (2012) article as the most influential publication, with 68 citations. The article introduced a multi-agent approach to air traffic flow management. Brittain and Wei’s (2022) article ranked second, with 54 citations, highlighting the increasing role of reinforcement learning. At the country level, the United States had the highest total citation count, followed by China, France, England, and Spain. By examining AI agents as a distinct research topic, the study provides a focused overview of an underexplored area relevant to researchers, practitioners, and regulators.