Deciphering Air Travel Disruptions: A Machine Learning Approach
This is an incremental study that addresses flight planning strategies for aviation operations by analyzing delay components like security and weather.
This research tackled flight delay prediction by comparing time-series models like LSTM and Bi-LSTM with baseline regression methods, aiming to identify influential factors such as departure time and airline, but it reported considerable errors in the baseline models without providing concrete numerical results.
This research investigates flight delay trends by examining factors such as departure time, airline, and airport. It employs regression machine learning methods to predict the contributions of various sources to delays. Time-series models, including LSTM, Hybrid LSTM, and Bi-LSTM, are compared with baseline regression models such as Multiple Regression, Decision Tree Regression, Random Forest Regression, and Neural Network. Despite considerable errors in the baseline models, the study aims to identify influential features in delay prediction, potentially informing flight planning strategies. Unlike previous work, this research focuses on regression tasks and explores the use of time-series models for predicting flight delays. It offers insights into aviation operations by independently analyzing each delay component (e.g., security, weather).