Unraveling the Research Trends of Artificial Intelligence in Aviation: A Bibliometric Analysis
Keywords:
Artificial intelligence, Deep learning, Machine learning, Aviation industries, Bibiometric analysisAbstract
This study employs bibliometric methods utilizing VOSviewer analysis of Scopus data from 2013 to 2023 to investigate trends in artificial intelligence (AI) research within the aviation industry. The analysis reveals a substantial increase in publication volume over time, peaking at 406 articles in 2022, signifying a heightened interest in AI implementation within the aviation sector. Key publication sources notably include conferences such as AIAA IEEE Digital Avionics Systems Conference Proceedings and ACM International Conference Proceeding Series. Leading contributions in publications emerge from countries such as the United States, China, India, Germany, the United Kingdom, and France, reflecting global involvement in AI research within the aviation industry. Citation analysis identifies highly cited articles addressing topics such as Digital Twin (DT) optimization processes in aviation, AI application in aircraft navigation, and machine learning for weather forecasting. These findings underscore researchers' interest in fundamental topics such as aviation, aircraft-related artificial intelligence, flight delay, and deep learning. Furthermore, co-citation analysis delineates research clusters, illustrating thematic similarities within AI research in the aviation industry. Overall, this bibliometric analysis provides comprehensive insights into the evolution of AI research in the aviation industry, potentially guiding researchers, practitioners, and stakeholders in directing research efforts, formulating policies, and understanding current trends in the application of artificial intelligence within the aviation sector.
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Copyright (c) 2023 Sabam Danny Sulung, Muhammad Nur Cahyo Hidayat Nasrullah, Untung Lestari Nur Wibowo
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