On the Performance of Machine Learning Based Flight Delay Prediction – Investigating the Impact of Short-Term Features


  • Delia Schösser Technical University Dresden, "Friedrich List" Faculty of Transport and Traffic Sciences
  • Jörn Schönberger Technical University Dresden, "Friedrich List" Faculty of Transport and Traffic Sciences




flight delay prediction, machine learning, aviation, feature importance, classification, SHAP


People and companies today are connected around the world, which has led to a growing importance of the aviation industry. As flight delays are a big challenge in aviation, machine learning algorithms can be used to forecast those. This paper investigates the prediction of the occurrence of flight arrival delays with three prominent machine learning algorithms for a data set of domestic flights in the USA. The task is regarded as a classification problem. The focus lies on the investigation of the influence of short-term features on the quality of the results. Therefore, three scenarios are created that are characterised by different input feature sets. When forgoing the inclusion of short-term information in order to shift the prediction timing to an early point in time, an accuracy of 69.5% with a recall of 68.2% is achieved. By including information on the delay that the aircraft had on its previous flight, the prediction quality increases slightly. Hence, this is a compromise between the early prediction timing of the first model and the good prediction quality of the third model, where the departure delay of the aircraft is added as an input feature. In this case, an accuracy of 89.9% with a recall of 83.4% is obtained. The desired timing of prediction therefore determines which features to use as inputs since short-term features significantly improve the prediction quality.


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How to Cite

Schösser, D., & Schönberger, J. (2022). On the Performance of Machine Learning Based Flight Delay Prediction – Investigating the Impact of Short-Term Features. Promet - Traffic&Transportation, 34(6), 825–838. https://doi.org/10.7307/ptt.v34i6.4132