Prediction of airport on-time performance

TL;DR

This paper develops predictive models using machine learning techniques to estimate flight delays at a major airport, identifying key factors influencing punctuality and demonstrating potential operational improvements.

Contribution

It introduces a comprehensive approach combining multiple models to predict delays and identifies critical predictors affecting airport on-time performance.

Findings

Models achieve high accuracy in predicting delays

Key predictors include weather, congestion, and historical delay rates

Operational insights can improve airport and airline efficiency

Abstract

We investigate the factors contributing to departure and arrival delays at a major international airport and develop predictive models to estimate both the likelihood and duration of delays. Using logistic regression, random forest, and gradient boosting methods, we identify key predictors of flight punctuality, including historical delay rates of flight numbers and airlines, weather conditions, runway traffic, walk time from security to gate, and overall airport congestion. Our models achieve strong inference and predictive performance in both classification and regression tasks, demonstrating the potential for targeted operational interventions to improve on-time performance and providing actionable insights for airport management and airline operations.