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Journals / Sakarya University Journal of Science / 2020 / Volume: 24 Issue: 6

Supervised Learning Approaches to Flight Delay Prediction

Mehmet Cemal Atlıoğlu(TAV TECHNOLOGY, İSTANBUL)0000-0003-1289-2715
Mustafa Bolat(TAV TECHNOLOGY, İSTANBUL)0000-0001-8169-0629
Murat Şahin(MANISA CELAL BAYAR UNIVERSITY, FACULTY OF TECHNOLOGY)0000-0002-2866-8796
Volkan Tunalı(MALTEPE ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ)0000-0002-2735-7996
Deniz Kılınç(İZMİR BAKIRÇAY UNIVERSITY, FACULTY OF ENGINEERING AND ARCHITECTURE)0000-0002-2336-8831
Pages
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Publication Date
2020-12-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Similarly, GBM, XGB, and LGBM showed very good performance in most of the cases, achieving F-Scores around 0.810.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Delays in flights and other airline operations have significant consequences in quality of service, operational costs, and customer satisfaction.
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Key Findings & Data:In addition, we trained and tested 11 machine learning models on the datasets that we created from the original dataset via feature selection and transformation.
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Conclusion & Impact:Similarly, GBM, XGB, and LGBM showed very good performance in most of the cases, achieving F-Scores around 0.810.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:air transportation · flight delay prediction · machine learning

Abstract

Delays in flights and other airline operations have significant consequences in quality of service, operational costs, and customer satisfaction. Therefore, it is important to predict the occurrence of delays and take necessary actions accordingly. In this study, we addressed the flight delay prediction problem from a supervised machine learning perspective. Using a real-world airline operations dataset provided by a leading airline company, we identified optimum dataset features for optimum prediction accuracy. In addition, we trained and tested 11 machine learning models on the datasets that we created from the original dataset via feature selection and transformation. CART and KNN showed consistently good performance in almost all cases achieving 0.816 and 0.807 F-Scores respectively. Similarly, GBM, XGB, and LGBM showed very good performance in most of the cases, achieving F-Scores around 0.810.

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Cite This Article

Atlıoğlu, M. C., Bolat, M., Şahin, M., Tunalı, V., Kılınç, D. (2020). Supervised Learning Approaches to Flight Delay Prediction. Sakarya University Journal of Science, Volume: 24 Issue: 6. https://doi.org/10.16984/saufenbilder.710107

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