32 years of Artificial Intelligence in aviation: Disclosing past success, unlocking next challenges

• Darío Pérez-Campuzano ^[1] ; Luis Rubio Andrada ^[1] ; Patricio Morcillo Ortega ^[1] ; Antonio López-Lázaro ^[2]
  1. [1] Universidad Autónoma de Madrid

     Universidad Autónoma de Madrid

     Madrid, España

     
  2. [2] Euroairlines
• Localización: ESIC Digital Economy & Innovation Journal, ISSN-e 2792-8721, Vol. 1, Nº. 1, 2021, págs. 138-157
• Idioma: inglés
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• Resumen

  • Artificial Intelligence (AI) and its related disciplines (Machine Learning, Data Mining, Big Data…) offer opportunities whose practical implementation pose complex challenges. Their fast evolution evidences potential but has caused a gap between academia and certain areas of the industry – which seem to lack the required agility to implement such technologies. This study aims to suggest some recommendations and a roadmap aligning both communities through a comprehensive quantitative meta-analysis and visualization of the existing literature. Although four modes of transport are initially compared, the focus is placed on AI within air transport (273 works since 1987) and its relationship with organizational areas. Results show that the most popular topics are Machine Learning and neural networks. Nevertheless, as many documents only mention one AI-related term, visibility is hindered in specific-keyword searches. Operations seem to be thoroughly explored while there is room for research in Strategy and Resourcing.