During the transition from the en-route phase to landing, an aircraft’s flight time is subject to significant uncertainty. This uncertainty arises primarily from unpredictable weather, varying aircraft performance characteristics, and the human element in executing ATC instructions. Improved estimation of the approach phase duration could yield significant benefits for airline fuel planning and flight scheduling, yet current practice still largely relies on fixed deterministic buffers. Existing work on arrival delay prediction focuses on deterministic models at smaller forecast horizons during the airborne phase.

This paper develops and validates an explainable probabilistic forecasting model for flight duration within the Amsterdam Schiphol (AMS) Flight Information Region (FIR), with a forecast moment in the pre-departure phase. The primary objective is to forecast the duration within the AMS FIR using information available at planning, while providing interpretable contributors of delay that can be clearly communicated to flight dispatchers and pilots.

Results show that our model achieves a reduced MAE of 33% and a reduced RMSE of 26% relative to the current operational baseline, while capturing about one third of the variance in FIR duration (R² = 0.33). Additionally, quantile-based transit time forecasts can provide airlines with a more risk-aware basis for fuel and schedule planning than fixed deterministic buffers. However, the relatively low R² shows that a substantial share of the variation in FIR duration remains unexplained, largely associated with tactical ATC interventions that occur under otherwise acceptable weather and capacity conditions. ... Read more

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 critical as they allow for precise determination of the contingency and discretionary fuel required for flights. 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. The most significant performance increase over the current model was observed under adverse weather conditions. 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. ... Read more

Aircraft carry additional fuel reserves, referred to as contingency fuel, used to account for unforeseen events during a flight. Previous research has attempted to quantify the magnitude of such events, most notably the probability of adverse weather or ATFM regulation, yet their inherent unpredictability introduces uncertainty and frequently results in the overestimation of contingency fuel requirements. Recent studies use data-driven fuel-burn predictions to better estimate contingency fuel sizing; however, most are confined to specific routes or regions, limiting generalizability. To address this, we utilise real operational airline data covering both regional and intercontinental flights, and develop a quantile regression framework for predicting contingency fuel requirements, capable of adapting to more diverse set of flight characteristics. Our framework integrates flight-plan data, TAF weather forecasts, and proxy congestion features to predict required contingency fuel at varying quantile levels, enabling trade-offs between efficiency and safety. Unlike the current Statistical Contingency Fuel process, which applies different coverage levels by risk category, this evaluation uses a single fixed quantile for all flights when generating predictions. In a four-month out-of-sample evaluation, a single fixed quantile matched the safety performance of the Statistical Contingency Fuel process while reducing excess fuel carriage by up to 235,364 kg (≈11%). A more conservative quantile configuration yielded smaller savings but reduced abnormal flight-phase events by 22.2%. The key drivers of the final predictions are evaluated, offering pilots and dispatchers transparent explanations that can build trust and reduce reliance on discretionary fuel loading. ... Read more