Charting the Trajectory of Artificial Intelligence Agents in Air Traffic Control
DOI:
https://doi.org/10.5281/zenodo.23372598Keywords:
Artificial intelligence, AI agent, Bibliometric analysis, Multi-agent, Reinforcement learning, Human-AIAbstract
The aim of the study was to chart the evolution and the current research landscape of artificial intelligence agents within the domain of air traffic control. The research provides a systematic, quantitative overview to identify prevailing scholarly trends, highlight existing research gaps, and offer a consolidated reference for researchers, utilizing a bibliometric analysis. The analysis was based on a dataset of 58 core publications extracted from the Web of Science database. The findings reveal that while foundational literature dates back to 1998, a noticeable surge in research output emerged after 2022. Publication volume reached its highest level in 2025 with 16 papers, indicating substantial growth in scholarly attention toward the application of agents in air traffic control environments. Citation analysis identifies Agogino and Tumer as the most influential contribution with 68 citations, introducing a pioneering multi-agent approach for traffic flow management. The second most cited article by Brittain and Wei with 54 citations underscores the growing role of reinforcement learning. In terms of the citation of countries, the United States emerges as the preeminent contributor, followed by China, France, England, and Spain. The study addresses a gap in the existing literature by treating artificial intelligence agents as a distinct object of bibliometric inquiry, offering a structured cartography of an undercharted subfield for researchers, practitioners, and regulators navigating the transition toward autonomous airspace management.
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Copyright (c) 2026 Seda Çeken, Arif Tuncal (Author)
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