Tools
Tools
Devasia, Dona Elizebeth (2024) Flight Delay Prediction: Harnessing the Power of AI for Proactive Air Travel Management. Masters thesis, Dublin, National College of Ireland.
Preview |
PDF (Master of Science)
Download (1MB) | Preview |
Preview |
PDF (Configuration manual)
Download (952kB) | Preview |
Abstract
The complexity of air travel management necessitates proactive strategies to address the persistent challenge of flight delays. This research focuses on leveraging Artificial Intelligence (AI) to predict flight delays accurately, with a specific emphasis on enhancing operational efficiency for a targeted airline company. The core challenge involves predicting whether a flight will be delayed, on time, or early. Building upon foundational methodologies, it is intended to extend and refine existing approaches to cater specifically to the operational context of the airline industry. This methodology encompasses a thorough exploration of classification algorithms, ranging from basic models to advanced techniques like XGBoost. Through rigorous hyperparameter tuning and strategic feature engineering, nuanced patterns within the data have been uncovered. By delving into the intricacies of machine learning, conventional approaches is transcended, aiming to enhance the precision of flight delay predictions. The results of the analysis demonstrate the effectiveness of the tailored approach, showcasing improved accuracy compared to baseline models. Utilizing AI, profound insights into the factors influencing flight delays is revealed, providing actionable intelligence for enhanced operational management. This research contributes not only to the academic discourse on flight delay prediction but, more critically, offers tangible advancements to the targeted airline company's air travel management strategies. Through a synthesis of theoretical foundations and practical applications, this study envisions a paradigm shift in the realm of proactive air travel management.
Actions (login required)
 |
View Item |