Supervised Learning Approaches to Flight Delay Prediction

Year 2020, , 1223 - 1231, 01.12.2020

Mehmet Cemal Atlıoğlu Mustafa Bolat Murat Şahin Volkan Tunalı , Deniz Kılınç

https://doi.org/10.16984/saufenbilder.710107

Cited By: 2

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.

Keywords

air transportation, flight delay prediction, machine learning, data science

Supporting Institution

Research and Development Center of TAV Airports Holding

Thanks

Funding for this work was partially supported by the Research and Development Center of TAV Airports Holding accredited on Turkey - Ministry of Science.

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