A deep BiLSTM machine learning method for flight delay prediction classification

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dc.contributor.authorBisandu, Desmond Bala
dc.contributor.authorMoulitsas, Irene
dc.date.accessioned2023-10-02T14:52:51Z
dc.date.available2023-10-02T14:52:51Z
dc.date.issued2023-09-25
dc.description.abstractThis paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features to train and test the models. The performance evaluation of the models and Confusion matrix shows that BiLSTM outperforms the LSTM model. In evaluating the models using the Mathews Correlation Coefficient (MCC), the BiLSTM model offers a better correlation of 0.99 between the original and predicted classes. Our experiment shows that for predicting flight delays, the BiLSTM model takes advantage of the forward and backward hidden sequences and the deep neural network for performance exploration and exploitation to achieve high accuracy, recall, and F1-Score. Our findings suggest that the BiLSTM model can effectively predict flight delays and provide valuable information for airlines, passengers, and airport managers.en_UK
dc.identifier.citationBisandu DB, Moulitsas I. (2023) A deep BiLSTM machine learning method for flight delay prediction classification. Journal of Aviation/Aerospace Education & Research (JAEER), Volume 32, Issue 2, Septermber 2023, Article number 4en_UK
dc.identifier.issn1065-1136
dc.identifier.urihttps://doi.org/10.58940/2329-258X.1992
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20316
dc.language.isoenen_UK
dc.publisherEmbry-Riddle Aeronautical Universityen_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAnalysisen_UK
dc.subjectBiLSTMen_UK
dc.subjectdeep learningen_UK
dc.subjectFlight delayen_UK
dc.subjectMachine learningen_UK
dc.titleA deep BiLSTM machine learning method for flight delay prediction classificationen_UK
dc.typeArticleen_UK

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