A Deep BiLSTM Machine Learning Method for Flight Delay Prediction Classification

Abstract

Verbatim from Embry-Riddle Scholarly Commons. Not paraphrased, not summarized.

This 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.

Authors

• Bisandu, Desmond B, PhD Embry-Riddle Aeronautical University
• Moulitsas, Irene, PhD Embry-Riddle Aeronautical University

Keywords

• Analysis
• BiLSTM
• deep learning
• Flight delay
• Machine learning
• Categorical Data Analysis
• Data Science
• Management and Operations
• Multi-Vehicle Systems and Air Traffic Control
• Other Aerospace Engineering
• Other Computer Sciences