Predictive Analytics for Airline Delay

Abstract

Flight delays have emerged as a critical challenge in civil aviation, causing substantial economic impacts across airlines and related industries. Accurate prediction of flight delays is increasingly valuable for airline operations, airport resource management, insurance risk assessment, and passenger planning. The complexity of delay factors characterized by their non-linear relationships and regional variations presents significant modeling challenges. This paper addresses limitations in existing prediction frameworks by introducing a novel flight delay prediction model with enhanced generalization capabilities and an optimized machine learning classification algorithm. Our approach incorporates multidimensional temporal and spatial features, including cascading effects from preceding flights, specific conditions at departure and arrival airports, and comprehensive route-based patterns. The model undergoes rigorous training with historical flight data and validation using recent operational data, demonstrating improved predictive accuracy across diverse aviation environments. Results indicate that this integrated approach captures the complex interplay of factors affecting flight delays more effectively than conventional methods.

Introduction

Flight Delay Prediction Using Machine Learning

Flight delays are a major issue in the aviation industry, causing economic losses, operational inefficiencies, and passenger dissatisfaction. Key causes include weather, traffic congestion, maintenance issues, and scheduling problems. In the U.S., a delay is defined as an arrival more than 15 minutes late. In 2018, delays cost the U.S. economy over $41 billion.

This project applies machine learning to predict flight delays using historical data such as airline, route, departure time, and weather, excluding directly delay-related features to avoid bias. The model helps passengers plan better and assists airlines in minimizing disruptions.

Literature Survey Highlights

1. Airport Movement Optimization – Simulations improve ground operations by identifying congestion points and redesigning layouts.

2. Gradient Boosting – Among KNN, SVM, RF, and GB, Gradient Boosting achieved highest accuracy (79.7%) for predicting delays.

3. Weather Disruption Forecasting – Combines weather and flight data using ML algorithms like AdaBoost and Random Forest for delay predictions.

4. System Classification – A review categorizing prediction methods by scope and data source, showing ML's growing importance.

5. Weather-Flight Correlation – Random Forest achieved 77% accuracy by analyzing sea-level pressure to forecast delays for a Japanese airline.

Methodology

• Data Sources: Flight data (2019–2021) from BTS and weather data from NOAA.

• Preprocessing: Included cleaning, feature engineering, and normalization.

• Modeling:

  • Gradient Boosting (main model): 100 trees, learning rate 0.1, max depth 5.

  • Random Forest (baseline): 200 trees, depth 7.

  • Evaluation: 80/20 split, 5-fold CV, metrics used were accuracy, F1, ROC-AUC.

• Deployment: Flask-based web app with input forms and delay prediction output, showing both classification and probability.

Results and Interface

The application includes:

• A home screen with branding.

• A login system ensuring secure access.

• A flight input form that predicts delay status and likelihood.

• Clear, user-friendly interface for practical use by travelers or airline personnel.

Conclusion

This research demonstrates that the Predictive Analytics for Airline Delay project successfully leverages machine learning to forecast flight delays based on historical data. By integrating Django, SQL, and machine learning models, the system provides real-time delay predictions, helping airlines, airport authorities, and passengers make informed decisions. The project optimizes accuracy by considering multiple influencing factors such as airline operators, flight routes, departure times, and weather conditions.

References

[1] Noriko, Etani, \"Development of a predictive model for on-time arrival flight of airliner by discovering correlation between flight and weather data,\" 2019. [2] Gupta, A., & Jain, \"A Comparative Study of Machine Learning Models for Flight Delay Prediction,\" 2023. [3] Robinson, T., & Martinez, \"Integrating Weather Data into Flight Delay Prediction Models Using Machine Learning,\" 2023. [4] Zhang, L., Wang, Q., & Chen, Y., \"Deep Learning Approaches for Flight Delay Prediction: A Comprehensive Review,\" Journal of Air Transport Management, 2022. [5] Peterson, K., & Ramirez, J., \"Real-time Flight Delay Prediction Using Ensemble Methods and Network Analysis,\" Transportation Research Part C: Emerging Technologies, 2021. [6] Singh, D., Kumar, V., & Sharma, A., \"A Hybrid CNN-LSTM Architecture for Improved Flight Delay Forecasting in High-Traffic Airports,\" IEEE Transactions on Intelligent Transportation Systems, 2023. [7] Kim, H., & Park, J., \"Multi-airport Delay Propagation Analysis Using Graph Neural Networks,\" Journal of Intelligent Transportation Systems, 2020. [8] Vasquez-Rodriguez, M., & Thompson, C., \"Explainable AI for Flight Delay Prediction: Improving Stakeholder Trust in Aviation Decision Support Systems,\" Computers in Industry, 2022

Copyright

Copyright © 2025 Yashwanth Kasula, Revathi Kadali, Rohan Sai Vurenuka, G Uma Devi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.