On the Performance of Machine Learning Based Flight Delay Prediction – Investigat-ing the Impact of Short-Term Features
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
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.
Details
Original language | English |
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Pages (from-to) | 825-838 |
Journal | Promet - traffic & transportation : scientific journal on traffic and transportation research |
Volume | 34 |
Issue number | 6 |
Publication status | Published - 2 Dec 2022 |
Peer-reviewed | Yes |
External IDs
Scopus | 85148502086 |
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