Demand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the complexity assessment of traffic flows. However, current complexity methodologies and metrics do not properly take into account the impact of trajectories’ uncertainty to the quality of complexity predictions of air traffic demand. This paper proposes the development of several Bayesian network (BN) models to identify the impacts of TBO uncertainties to the quality of the predictions of complexity of air traffic demand for two particular Demand Capacity Balance (DCB) solutions developed by SESAR 2020, i.e., Dynamic Airspace Configuration (DAC) and Flight Centric Air Traffic Control (FCA). In total, seven BN models are elicited covering each concept at different time horizons. The models allow evaluating the influence of the “complexity generators” in the “complexity metrics”. Moreover, when the required level for the uncertainty of complexity is set, the networks allow identifying by how much uncertainty of the input variables should improve.

This study examines improvements in local air quality and noise (LAQ&N) of the Landing and Take-Off cycle (LTO cycle) with the introduction of a hybrid electric/sustainable mid-range aircraft. Using Dortmund Airport as a case study, the results highlight significant environmental benefits. Future traffic scenarios estimate potential gains: INDIGO flights improve LAQ by up to 74% and noise impacts by 36%. When replacing 15% of the future traffic demand with INDIGO aircraft, up to a 3% benefit in LAQ&N can be expected. Full traffic replacement scenarios promise substantial environmental advantages, affirming the INDIGO aircrafts’ role in greener aviation.