Air navigation service providers currently rely on deterministic demand forecasts for Air Traffic Flow Management (ATFM), which inherently fail to quantify forecast uncertainty. This study presents a top-down probabilistic forecasting framework for inbound air traffic demand using Air Traffic Control the Netherlands (LVNL) as a case study. The framework models the uncertainty of an existing deterministic forecasting system using quantile regression, thereby capturing heteroscedasticity directly at the aggregate demand level. To improve empirical coverage under non-exchangeable conditions, Conformalized Quantile Regression is combined with Adaptive Conformal Inference. The framework was applied using approximately two years of operational ATFM data. Compared to a statistical error-margin baseline, the proposed machine learning approach produced substantially narrower prediction intervals and achieved empirical coverage closer to the target, leading to a 25.1% lower Mean Winkler Interval Score (MWIS). Furthermore, Adaptive Conformal Inference more closely tracked the nominal 90% target coverage level over time than static conformal calibration, while also slightly reducing average interval width and MWIS. A retrospective operational analysis indicates that the probabilistic framework enables more flexible risk-based decision-making compared to deterministic forecasting alone. For a representative 150-minute prediction horizon, specific probabilistic decision thresholds simultaneously reduced both the false positive and false negative rates of capacity breaches relative to the deterministic baseline across the evaluated test dataset. These results demonstrate the potential of adaptive conformal prediction techniques to support probabilistic inbound demand forecasting within ATFM operations. ... Read more

Trajectory-Based Operations (TBO) are intended to improve Air Traffic Management (ATM) by enabling earlier planning, more consistent trajectory prediction, and reduced tactical conflict management. During the transition towards advanced Automatic Dependent Surveillance–Contract (ADSC) and Controller–Pilot Data Link Communications (CPDLC) services, aircraft will provide different levels of downlinked intent and Flight Management System (FMS)-integrated trajectory update capability. This paper evaluates how this mixed datalink equipage affects a TBO concept for Amsterdam Area Control Centre (ACC) airspace. A strategic trajectory management model is developed for mixed inbound and outbound traffic between FL110 and FL260, combining fixed Flight Path Angle (FPA) descents with probabilistic Conflict Detection & Resolution (CD&R). Equipage-dependent uncertainty is represented through descent angle, target descent speed, and wind, while traffic density and fleet composition are varied in a Monte Carlo experiment. The results show that increasing equipage capability reduces trajectory adjustments and residual losses of separation, with the clearest benefits in higher density scenarios. Flown track distance and modelled work also decrease as more advanced equipage becomes available, although work reductions are small and occur mainly outside the Amsterdam ACC conflict management volume. Expected Approach Time (EAT) adherence remains broadly comparable across equipage compositions. ... Read more