Bibliometric Analysis of Studies on Artificial Intelligence in the Air Transportation Sector

Abstract

The use of artificial intelligence is becoming widespread in almost all sectors. The air transportation sector is naturally where artificial intelligence studies are frequently carried out. In both the application process and academic studies, studies on artificial intelligence have increased significantly in recent years. It is thought that examining the studies conducted in this context will contribute to the understanding of the existing literature on artificial intelligence and help predict the trends that will emerge in the future. For these reasons, this study aims to conduct a bibliometric analysis of studies on artificial intelligence in the air transportation sector. The analysis of 1712 academic studies obtained from the Scopus database was conducted with R Bibliometix and VOSViewer software. In the study, analyses such as the authors and countries with the highest number of publications, the most influential authors and countries, the institutions that contribute the most to the studies, the most influential journals, thematic analysis, co-occurrence, co-citation, and bibliographic coupling analysis were performed. As a result of the analysis, it was determined that most of the studies are from the Asian region, and the rate of cooperation in the studies is high, but the rate of international cooperation is relatively low. On the other hand, it was revealed that the motor themes in studies on artificial intelligence are air traffic control, Unmanned Aerial Vehicle, optimization, eye tracking, and automation, while the basic themes are machine learning, deep learning, aviation safety, neural network, and situation awareness.

Keywords

• Aviation
• Air transportation
• Artifical intelligence
• Bibliometric analysis

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