Probabilistic Forecasting of Aircraft Transit Time within a Flight Information Region: A Case Study of Schiphol Airport
E. Süülker
P.R.J.R. Lothaller (TU Delft - Aerospace Engineering)
M.J. Ribeiro (TU Delft - Aerospace Engineering)
Junzi Sun (TU Delft - Aerospace Engineering)
Jasper de Wilde (KLM Royal Dutch Airlines)
Alexander Piva (Koninklijke Luchtvaart Maatschappij N.V., KLM Engineering and Maintenance)
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Abstract
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.