TY - JOUR
T1 - Applications of Artificial Intelligence in the Air Transport Industry
T2 - A Bibliometric and Systematic Literature Review
AU - Sadou, Abderrahmane Moubarek
AU - Tchouamou Njoya, Eric
N1 - Publisher Copyright:
© 2023, Departamento de Ciencia e Tecnologia Aeroespacial. All rights reserved.
PY - 2023/9/22
Y1 - 2023/9/22
N2 - The use of artificial intelligence, along with its various components, is rapidly increasing in various fields of study today, going beyond the traditional domains of computer science and mathematics. To gain insights into how artificial intelligence is being applied in the air transport industry, uncover underlying correlations and trends in the literature, and identify potential research gaps, we conducted a systematic literature review supplemented with bibliometric elements such as keyword co-occurrence and author influence. The key findings of our research shed light on the most prolific institutions and authors globally involved in generating knowledge about AI applications in air transport. Additionally, we identified five research clusters that dominate the overall research direction: prediction and optimisation (constituting 65% of the articles), interindustry collaborations (17% of the articles), human experience (9% of the articles), safety, risks, and ethical considerations (6% of the articles), and ecology and sustainable development (3% of the articles). Overall, further research is needed to explore the ethical implications, legal considerations, integration processes, and impact on employment and the environment in the air transport industry.
AB - The use of artificial intelligence, along with its various components, is rapidly increasing in various fields of study today, going beyond the traditional domains of computer science and mathematics. To gain insights into how artificial intelligence is being applied in the air transport industry, uncover underlying correlations and trends in the literature, and identify potential research gaps, we conducted a systematic literature review supplemented with bibliometric elements such as keyword co-occurrence and author influence. The key findings of our research shed light on the most prolific institutions and authors globally involved in generating knowledge about AI applications in air transport. Additionally, we identified five research clusters that dominate the overall research direction: prediction and optimisation (constituting 65% of the articles), interindustry collaborations (17% of the articles), human experience (9% of the articles), safety, risks, and ethical considerations (6% of the articles), and ecology and sustainable development (3% of the articles). Overall, further research is needed to explore the ethical implications, legal considerations, integration processes, and impact on employment and the environment in the air transport industry.
KW - Artificial Intelligence
KW - Air Transport
KW - Big Data Technologies
KW - Air Traffic Management
KW - Airlines
KW - Airports
KW - Air Traffic Management: Airlines
UR - http://www.scopus.com/inward/record.url?scp=85172411174&partnerID=8YFLogxK
U2 - 10.1590/jatm.v15.1312
DO - 10.1590/jatm.v15.1312
M3 - Review article
VL - 15
JO - Journal of Aerospace Technology and Management
JF - Journal of Aerospace Technology and Management
SN - 1984-9648
M1 - e2223
ER -