Predicting Delayed Flights for International Airports Using Artificial Intelligence Models & Techniques

Abstract

Delayed flights are a pervasive challenge in the aviation industry, significantly impacting operational efficiency, passenger satisfaction, and economic costs. This thesis aims to develop predictive models that demonstrate strong performance and reliability, capable of maintaining high accuracy within the tested dataset and showcasing potential for application in various real-world aviation scenarios. These models leverage advanced artificial intelligence and deep learning techniques to address the complexity of predicting delayed flights. The study evaluates the performance of Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and their hybrid model (LSTM-CNN), which combine temporal and spatial pattern analysis, alongside Large Language Models (LLM, specifically OpenAI's Babbage model), which excel in processing structured and unstructured text data. Additionally, the research introduces a unified machine learning framework utilizing Gradient Boosting Machine (GBM) for regression and Light Gradient Boosting Machine (LGBM) for classification, aimed at estimating both flight delay durations and their underlying causes. The models were tested on high-dimensional datasets from John F. Kennedy International Airport (JFK), and a synthetic dataset from King Abdulaziz International Airport (KAIA). Among the evaluated models, the hybrid LSTM-CNN model demonstrated the best performance, achieving 99.91% prediction accuracy with a prediction time of 2.18 seconds, outperforming the GBM model (98.5% accuracy, 6.75 seconds) and LGBM (99.99% precision, 4.88 seconds). Additionally, GBM achieved a strong correlation score (R² = 0.9086) in predicting delay durations, while LGBM exhibited exceptionally high precision (99.99%) in identifying delay causes. Results indicated that National Aviation System delays (correlation: 0.600), carrier-related delays (0.561), and late aircraft arrivals (0.519) were the most significant contributors, while weather factors played a moderate role. These findings underscore the exceptional accuracy and efficiency of LSTM-CNN, establishing it as the optimal model for predicting delayed flights due to its superior performance and speed. The study highlights the potential for integrating LSTM-CNN into real-time airport management systems, enhancing operational efficiency and decision-making while paving the way for smarter, AI-driven air traffic systems.