Competing interests EAF is a founder and share holder of VIDA Diagnostics, a company that is commercialising pulmonary image analysis software developed, in part, at the University of Iowa.Provenance and peer review Not commissioned; internally peer reviewed.

Future air traffic management will require a variety of automated decision support tools to provide conflict-free trajectories and their associated error margins. The ability to correctly forecast aircraft trajectories, i.e. trajectory prediction, is the central component of such automated tools, which will enable continued provision of safe and efficient services in increasingly congested skies. Current approaches for trajectory prediction, available in the open literature, make a number of assumptions in order to simplify the mathematical models of aircraft motion. Furthermore, many existing methods perform three-dimensional trajectory prediction, in which information on expected times of arrival at significant points along the intended aircraft route is not considered. This results in inaccurate trajectories not suitable for conflict detection and resolution. This paper presents a novel four-dimensional trajectory prediction scheme that makes full use of data on expected times of arrival. A three dimensional point-mass model for a standard civil aircraft is used to emulate aircraft dynamics, while the aircraft operating mode is characterised through a set of discrete variables. The aircraft performance model used relies on the EUROCONTROL Base of Aircraft Data (BADA) set and the computed trajectory accounts for the effects of wind. Inputs include navigation data and aircraft intent information, which unambiguously define the trajectory to be computed according to the flight plan. In the proposed model, aircraft intent information is summarised in a simple, but effective, set of instructions contained in a Flight Script. Furthermore, two key innovations to trajectory prediction are introduced. Firstly, a novel scheme to emulate the control system used for aircraft lateral guidance is proposed and secondly, on the basis of aircraft intent information, a new procedure to estimate speed is presented. The performance of the enhanced trajectory model proposed is quantified using a detailed operational dataset (real flight data) captured in a European airspace. The results show that, over an extended time-horizon, the enhanced model is more accurate than two representative existing methods, and that it is suitable for reliable trajectory prediction. K E Y

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Current state-of-the-art trajectory prediction tools typically model aircraft as three-dimensional point-masses, and make a number of simplifying assumptions about the actual and anticipated dynamics states of the aircraft. They are typically based on predefined settings obtained from existing databases such as EUROCONTROL's BADA rather than real-time information, including on the environment, available onboard the aircraft. This significantly limits trajectory prediction performance. This paper proposes a high-accuracy four-dimensional trajectory prediction model for use onboard civil aircraft, as well as by ground-based systems, which addresses these limitations. It is designed for strategic traffic capacity optimisation and conflict-detection and resolution over time-horizons covering the entire duration of a flight. The model incorporates a number of features including a novel flight-control-system and an enhanced flight-script that incorporates new taxonomy and content thereby enabling better definition of aircraft intent. The accuracy of the model is characterised using operational data acquired during a real flight trial. Results show that the performance of the proposed model is significantly better than the current models. Its accuracy is better than the required navigation performance for departure, en route and Non-Precision-Approach phases of flight.