A hybrid ensemble machine learning approach for arrival flight delay classification prediction using voting aggregation technique

Simple item page
dc.contributor.authorBisandu, Desmond Bala
dc.contributor.authorMoulitsas, Irene
dc.date.accessioned2023-06-14T11:49:34Z
dc.date.available2023-06-14T11:49:34Z
dc.date.issued2023-06-08
dc.description.abstractThe number of flights keeps increasing with the development of civil aviation, and flight delays have become a severe issue of concern to the aviation industry. The need for reliable flight delay classification and prediction cannot be overemphasised because of its importance. In this research, we classify arrival flight delay using a hybrid ensemble machine learning algorithm based on voting aggregation. Each ensemble’s architecture consists of four supervised machine learning algorithms: Logistic Regression, Decision Tree, Support Vector Classifier and Random Forest. We conducted a comparative experiment using the United States Bureau of Statistics dataset to verify the efficacy of our proposed approach against other benchmark approaches. We employ multiple evaluation metrics to check the performance of our proposed model comprehensively. Accuracy, Recall, Precision, F1-Score, AUC Score, and ROC curve. Our experimental results show that our model outperforms the benchmark techniques on the binary classification of flight delays task. Hence, the hybrid ensemble model presented in this research can be used as a decision-support model to improve the flight scheduling management system design to assist the airline and airport operation managers and passengers with improved travel itineraries management.en_UK
dc.identifier.citationBisandu DB, Moulitsas I. (2023) A hybrid ensemble machine learning approach for arrival flight delay classification prediction using voting aggregation technique. In: 2023 AIAA Aviation and Aeronautics Forum and Exposition (AIAA AVIATION Forum), 12-16 June 2023, San Diego, USA. Paper number AIAA 2023-4326en_UK
dc.identifier.isbn978-1-62410-704-7
dc.identifier.urihttps://doi.org/10.2514/6.2023-4326
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19771
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectarrival flight delayen_UK
dc.subjectclassificationen_UK
dc.subjectensemble learningen_UK
dc.subjectpassenger experienceen_UK
dc.subjectpredictionen_UK
dc.titleA hybrid ensemble machine learning approach for arrival flight delay classification prediction using voting aggregation techniqueen_UK
dc.typeConference paperen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
arrival_flight_delay_classification_prediction-2023.pdf
Size:
1.02 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)