TY - JOUR AU - Delia Schösser AU - Jörn Schönberger PY - 2022/12/01 Y2 - 2024/03/29 TI - On the Performance of Machine Learning Based Flight Delay Prediction – Investigating the Impact of Short-Term Features JF - Promet - Traffic&Transportation JA - Promet VL - 34 IS - 6 SE - Articles DO - 10.7307/ptt.v34i6.4132 UR - http://traffic.fpz.hr/index.php/PROMTT/article/view/4132 AB - 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 promi-nent machine learning algorithms for a data set of do-mestic flights in the USA. The task is regarded as a clas-sification 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 for-going 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 predic-tion 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. ER -