Alexa, Predict My Flight Delay

TL;DR

This paper presents a flight delay prediction system using machine learning models trained on US domestic flight data, aiming to improve accuracy and understanding of delay factors for better airline scheduling.

Contribution

It develops a comprehensive flight delay prediction system that analyzes US domestic flight data, addressing limitations of previous models by learning delay causes and their interrelations.

Findings

Models effectively predict flight delays based on multiple factors.

The system enhances understanding of delay causes and their interconnections.

Results demonstrate improved prediction accuracy over earlier methods.

Abstract

Airlines are critical today for carrying people and commodities on time. Any delay in the schedule of these planes can potentially disrupt the business and trade of thousands of employees at any given time. Therefore, precise flight delay prediction is beneficial for the aviation industry and passenger travel. Recent research has focused on using artificial intelligence algorithms to predict the possibility of flight delays. Earlier prediction algorithms were designed for a specific air route or airfield. Many present flight delay prediction algorithms rely on tiny samples and are challenging to understand, allowing almost no room for machine learning implementation. This research study develops a flight delay prediction system by analyzing data from domestic flights inside the United States of America. The proposed models learn about the factors that cause flight delays and cancellations and the link between departure and arrival delays.