Air traffic management (ATM) is gradually moving towards the notion of allowing aircraft to fly their preferred trajectory, otherwise known as trajectory-based operations (TBO). One of the challenges related to implementing TBO is being able to identify, model and manage the uncertainty associated with a trajectory.
Integrating uncertainty models in planning systems improves trajectory predictions and helps assess the feasibility of incorporating the models into existing demand and capacity balancing (DCB) tools.
Between 2016 and 2018, the COPTRA project researched three areas related to uncertainty modelling. In this context, COPTRA:
- defined and assessed probabilistic trajectories in a TBO environment;
- combined probabilistic trajectories to build probabilistic traffic prediction;
- applied probabilistic traffic prediction to air traffic control planning.
COPTRA showed that in addition to quantifying uncertainty through data analytics, it is possible to limit uncertainty through model-driven state estimation techniques. Not only were flight intent or initial condition uncertainties included but model uncertainties were also taken into account.
COPTRA's models provided a clear quantitative understanding of delay propagation dynamics in space and time. The project results gave insight into how more efficient ATM operations can be achieved in the future.