A METHODOLOGY FOR AUTOMATED TRAJECTORY PREDICTION ANALYSIS

Chester Gong and Dave McNally

ABSTRACT

A number of air traffic management decision support tools (DST) are 
being developed to help air traffic managers and controllers improve 
capacity, efficiency, and safety in the National Airspace System. 
Although DST functionality may vary widely, trajectory prediction 
algorithms can be found at the core of most DST. A methodology is 
presented for the automated statistical analysis of trajectory 
prediction accuracy as a function of phase of flight (level-flight, 
climb, descent) and look-ahead time. The methodology is focused on 
improving trajectory prediction algorithm performance for DST 
applications such as conflict detection and arrival metering. 
The methodology has been implemented in software and tested with 
air traffic data. Aggregate trajectory prediction accuracy statistics 
are computed and displayed in histogram format based on 2,774 large 
commercial jet flights from five different days of Fort Worth Center 
air traffic data. The results show that trajectory prediction 
anomalies can be detected by examining error distributions for large 
numbers of trajectory predictions. The ability of the trajectory 
analysis methodology to detect the effects of subsequent changes to 
the trajectory prediction algorithm and to aircraft performance 
model parameters was also demonstrated.