Identification and initial characterization of sources of uncertainty affecting the performance of future trajectory management automation systems

The implementation of the Trajectory Based Operations (TBO) concept in the future ATM system will require the capability of performing accurate and robust trajectory predictions. TBO aims at enabling the efficient management of the expected increasing air traffic volumes strategically, only resorting to tactical interventions as a last resort. To achieve this aim, TBO will rely on Trajectory Management functions, such as strategic planning, trajectory negotiation, collaborative deconfliction and optimization, supported by new automation tools, DSTs (Decision Support Tools), which will support humans in executing those functions. These DSTs will need to operate on the basis of information describing future intended aircraft trajectories. Such information will typically be obtained from a trajectory prediction. A rigorous and unambiguous information sharing process has to be defined to deal not only with 4D trajectory information but also with the uncertainty associated to this information. Future DSTs may consider this uncertainty information to increase the robustness of the decision making process. Trajectory predictions are usually the output of resolving a deterministic mathematical problem that describes the aircraft motion. The modeling assumptions and simplifications used in the implementation of a trajectory predictor introduce potential prediction errors, i.e. sources of prediction uncertainty, that are typically not explicitly considered by such deterministic approaches. This paper proposes an alternative stochastic formulation of the trajectory prediction problem that explicitly considers the sources of uncertainty affecting a 3 degrees-of-freedom point mass model of the aircraft motion. This model is assumed to represent the aircraft model at the core of any trajectory predictor. The paper also addresses the mathematical characterization of the uncertainty sources affecting this model. This characterization is a key initial step to understand the effects of uncertainty in futures DSTs relying on trajectory prediction.