Combining Monte Carlo and worst-case methods for trajectory prediction in air traffic control: a case study.

We illustrate, through a case study, a novel combination of probabilistic Monte Carlo methods and deterministic worst-case methods to perform model-based trajectory prediction in Air Traffic Control. The objective is that of computing and updating predictions of the trajectory of an aircraft on the basis of received observations. We assume that uncertainty in computing the predictions derives from observation errors, from the action of future winds and from inexact knowledge of the mass of the aircraft. Our novel approach provides worst-case prediction sets to which the future trajectory of the aircraft is guaranteed to belong and, at the same time, an empirical distribution of the most probable trajectories which can be used to compute various estimates such as the probability of conflict and the expected time of arrival. The case study is developed using the aircraft performance model developed by the EUROCONTROL Experimental Centre in BADA (Base ofAircraft DAta