Deciphering Air Travel Disruptions: A Machine Learning Approach

TL;DR

This paper compares various machine learning models, including time-series approaches, to predict flight delays based on factors like departure time and airline, aiming to improve understanding and management of air travel disruptions.

Contribution

It introduces the use of time-series models such as LSTM, Hybrid LSTM, and Bi-LSTM for flight delay prediction, expanding beyond traditional regression methods.

Findings

Time-series models outperform baseline regressions in delay prediction.

Key features influencing delays are identified through model analysis.

Insights into aviation operations and delay components are provided.

Abstract

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).