Air traffic delays have a major impact on the aviation industry, affecting airlines, passengers, and the broader ecosystem. With increasing regulatory and sustainability pressures, accurate delay predictions are now critical, as they enable reductions in contingency and discretio
...
Air traffic delays have a major impact on the aviation industry, affecting airlines, passengers, and the broader ecosystem. With increasing regulatory and sustainability pressures, accurate delay predictions are now critical, as they enable reductions in contingency and discretionary fuel on flights, lowering total fuel usage. This research aims to develop an explainable supervised learning model to improve existing en route delay predictions, focusing on intercontinental flights from North America to Amsterdam Schiphol Airport. While prior studies have explored flight delay prediction, they have not addressed two critical research gaps identified in this research: the inclusion of day-of-operations features, such as passenger information, aircraft weights, and cost index, and the use of transatlantic flight data for
predictions 90 minutes before departure. To address these gaps, two Gradient-Boosted models, CatBoost and LightGBM, were trained using internal airline, airport, and METAR data. Both models outperformed the airline’s current in-use statistical model, with CatBoost achieving an MAE of 3.44 minutes and RMSE of 4.61 minutes and LightGBM achieving an MAE of 3.43 minutes and RMSE of 4.56 minutes. However, the model’s overall predictive accuracy, as
indicated by the R2 scores, remained relatively low, reflecting the inherent challenges of en route delay forecasting. The most significant performance increase over the current model was observed under adverse weather conditions. Despite these improvements, the test run also showed that the performance of the models deteriorates quickly as significant
differences exist in TAFs and actual weather. An explainability framework was developed to aid end users of the model, providing insights into the model’s decision-making process and helping to build user confidence in its predictions. This research advances en route delay prediction by providing more accurate delay forecasts, particularly in critical weather conditions, and proposes practical improvements to support future studies focused on enhancing model adaptability across diverse operational contexts.
