AI-Based Applications in Air Transportation over the Last Five Years: A Bibliometric Analysis and CRITIC–COPRAS-Based Evaluation

Abstract

This study aims to examine the trends of artificial intelligence–based applications in the field of air transportation over the last five years using bibliometric analysis and multi-criteria decision-making (MCDM) methods. Publications from the 2020–2025 period indexed in the Web of Science database were analyzed. The bibliometric results revealed that deep learning and predictive modeling themes have become prominent in recent years. Based on the conceptual themes, the identified criteria and alternatives were evaluated using the integrated CRITIC–COPRAS approach. The CRITIC analysis indicated that the criterion C4 (Cost Efficiency) had the highest information content, while the COPRAS results showed that the Deep Learning (A2) alternative achieved the highest relative performance score (100). The findings demonstrate that deep learning–based models possess strong potential in terms of accuracy, flexibility, and generalization performance in air transportation applications.

Keywords

• Air transportation
• Artificial intelligence
• Bibliometric analysis
• CRITIC
• COPRAS

Son Beş Yılda Hava Taşımacılığında Yapay Zekâ Tabanlı Uygulamalar: Bibliyometrik Analiz ve CRITIC–COPRAS Temelli Değerlendirme

Abstract

Bu çalışma, hava taşımacılığı alanında yapay zekâ tabanlı uygulamaların son beş yıldaki eğilimlerini bibliyometrik analiz ve çok kriterli karar verme (ÇKKV) yöntemleriyle incelemeyi amaçlamaktadır. Araştırmada, Web of Science veri tabanında yer alan 2020–2025 dönemine ait yayınlar analiz edilmiştir. Bibliyometrik analiz sonuçları, derin öğrenme ve tahminleme temalarının son dönemde öne çıktığını göstermektedir. Elde edilen temalar doğrultusunda belirlenen kriterler ve alternatifler, CRITIC–COPRAS yaklaşımıyla değerlendirilmiştir. CRITIC analizinde en yüksek bilgi yoğunluğuna sahip kriter C4 (Maliyet Verimliliği) olarak belirlenmiş, COPRAS analizinde ise Derin Öğrenme (A2) alternatifi en yüksek göreli performans puanını (100) almıştır. Sonuçlar, derin öğrenme tabanlı modellerin hava taşımacılığında doğruluk, esneklik ve genelleme başarısı açısından güçlü bir potansiyele sahip olduğunu göstermektedir.

Keywords

• Hava taşımacılığı
• Yapay zeka
• Bibliyometrik analiz
• CRITIC
• COPRAS

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