A Methodology for Automated Trajectory Prediction Analysis

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.