Christina M. Young

Impact of missing longitudinal aircraft intent on descent trajectory prediction

Accurate trajectory prediction is the cornerstone for future Air Traffic Management (ATM) concepts worldwide. To this purpose, researchers from Airservices Australia, the Federal Aviation Administration (FAA), and EUROCONTROL have collaborated on research into this important topic. Trajectory prediction is the process in which the future trajectory of an aircraft is computed based on a model of the aircrafts performance, forecast meteorological conditions and aircraft intent: a script that indicates how the aircraft is operated to fulfill the user-intentions of the flight while satisfying imposed constraints. The lateral component of the trajectory is mostly defined by the flight plan and if the plan is kept up to date, lateral uncertainty should be minimal. Longitudinally however, the problem is more complicated since not all required longitudinal aircraft intent is known to the ground-based Trajectory Predictor (TP). This paper presents the longitudinal performance of operational TPs from the FAA, EUROCONTROL, and Airservices Australia, which illustrates that inaccurate longitudinal aircraft intent and resulting excessive prediction errors are evident in all three TPs. The performance of the ground-based TPs was subsequently compared to data extracted from the aircrafts Flight Management System (FMS) through Future Air Navigation Systems (FANS) technology. FANS is standard equipment on wide-body aircraft and some domestic fleets, yet its full potential is widely under utilized. The paper argues significant improvement to ground based trajectory prediction accuracy is possible if the ground-TP is enhanced with aircraft data. It further demonstrates that this benefit can be achieved using aircraft data-link technologies, available today, to transmit FMS trajectory information.

Evaluation of a genetic algorithm that modifies air traffic data for conflict probe testing

Air traffic scenarios based on recorded live data are essential for the development, testing and evaluation of air traffic automation. It is often desirable to modify the live data to introduce additional encounters and conflicts because live data generally contains no conflicts. To this end a genetic algorithm (GA) has been developed that time shifts individual flights in the scenario based on a set of characteristics. Although this approach has been successful, larger scenarios and additional constraints dramatically increase the time for the GA to reach an acceptable solution. This paper first introduces an enhanced GA technique that distributes the processing over several computers. Following the biological metaphor further, this approach is called the Island Model because each instance of the running algorithm represents an island where periodic sharing of information represents migration between islands. The paper concludes with an evaluation of the Island Model utilizing a set of designed experiments. The evaluation will not only consider time to solve but the quality of the solution produced relative to the specified constraints, with the goal of identifying important factors and optimal settings for those factors.

Operational impact of trajectory prediction accuracy on air traffic automation tools

The relationship between the accuracy of aircraft trajectory predictions and performance of automation tools which depend on those trajectories is a key element in Trajectory Based Operations (TBO) concepts. This paper includes a literature review of research concerning this relationship. In addition, a study is presented which uses predicted trajectories from the En Route Automation Modernization (ERAM) system. In laboratory simulations based on historical air traffic data collection, discrepancies are introduced between the “as flown” trajectory (based on recorded radar surveillance data) and the predicted trajectory used to provide decision support services to air traffic control. The performance of the Conflict Probe (CP) is then evaluated using established methods, and System Operating Characteristics (SOC) curves are generated at different levels of trajectory accuracy. This paper attempts to characterize and quantify trajectory prediction improvements in terms the end users can appreciate: impact to decision support tools.

Fast-Time Simulation of Conflict Resolution Advisories to Evaluate Benefits from Strategic Maneuvering

An advanced separation management tool called Conflict Resolution Advisories (CRA) is being developed as a part of the Next Generation Air Transportation System (NextGen) initiative. CRA is a capability designed to aid air traffic controllers by providing a rank- ordered listing of potential conflict resolution maneuvers that ensure safe separation of air traffic. It is expected to result in more strategic resolution maneuvers, thereby improving operational efficiencies. A fast-time simulation study by the Concept Analysis Branch investigated this expected benefit using the Airspace Concept Evaluation System (ACES), developed by the National Aeronautics and Space Administration Ames Research Center (NASA/ARC), and within ACES, the Advanced Airspace Concept (AAC) package. Experimental design techniques were applied to study several factors including three future years of forecasted traffic demand, three different airspace regions, and an action time parameter which indicates how far in advance of a predicted conflict the resolution could be issued. Large values of the action time parameter represent the change from the tactical approach currently used to a more strategic approach anticipated in the future. The results of the experimental study are discussed in depth and compared to the results from an independent study by NASA/ARC.

30 th DASC paper: Trajectory predictor performance experiment using required time of arrival during descent

• Small changes in RTA constraint [+3, −2] minutes from nominal induce large ERAM trajectory prediction errors, specifically for arrival metrics at 4 minutes before TOD: before TOD: — Up to 26 nm along track error at TOD — Up to 20% metering time error • Approx 4 min error at 20 min Lookahead — Up to 1100 feet altitude error at meter fix • Recommendations-Future Studies — Impact of sharing RTA clearance and FMS generated speed profiles with ERAM trajectory predictor • Trajectory prediction performance • Conflict probe (i.e., missed and false alerts) — May provide further insight into benefits

Evaluation of a genetic algorithm for conflict probe testing

• Extended capability of GA • Decrease run time — Distributed via Island Model — Find significant factors & optimal levels • 5× speedup — DOE runs vs followup

Trajectory predictor performance experiment using Required Time of Arrival during descent

Most commercial aircraft today have advanced navigation computer systems, referred to as the Flight Management System or FMS. In recent years, the FMS has been increasingly utilized to support a type of performance-based navigation that allows an aircraft to fly a specific path between two defined 3-dimensional points in space. However, current deployed FMS architectures can do even more, calculating a Required Time of Arrival (RTA) at a precise 3-dimensional point in space and then automatically controlling the aircrafts airspeed and rate-of-descent to reach that point within a very small tolerance. This capability could offer increased efficiency for airlines and reduced workload for air traffic control under certain operational concepts such as metering to a transition fix into a terminal area. One challenge is that while RTA clearances are commonly used by en-route controllers when metering, the en-route air traffic automation tools that predict aircraft conflicts do not have the capability to input RTA clearances. The automation depends on flight plan information to generate aircraft trajectories, which are then used to predict conflicts. Adjustments in speed and Top of Descent (TOD) point implemented to meet an RTA would not be known to the automation and therefore not reflected in the trajectories generated. This paper describes an experiment where a real FMS platform was utilized in a simulation environment and simulated flight data was run through the current ground based automation in en route airspace, referred to as En Route Automation Modernization (ERAM). The predicted trajectories were compared under various RTA settings, weather conditions, and flight paths to examine how the current ERAM trajectory predictor performed. The results provide guidance on where additional research is needed and insights into using this FMS capability in current operations.