Trajectory Prediction Sensitivity Analysis Using Monte Carlo Simulations Based on Inputs’ Distributions

Abstract

To facilitate the increasing amount of air traffic, current and future decision support tools for air traffic management require an efficient and accurate trajectory prediction. With uncertainty inherent to almost all inputs of a trajectory predictor, the accurate prediction is not a simple task. In this study, Monte Carlo simulations of a ground-based trajectory predictor are performed to estimate the prediction uncertainty up to 20 min look-ahead time and to assess the correlation between inputs and prediction errors. Selected inputs are aircraft bank angle, constant calibrated airspeed and Mach number speed settings, vertical speed, temporary level-offs, air temperature, lapse rate, wind, and air traffic control intent. These inputs are provided in the form of their distribution functions obtained from observed data such as surveillance data, weather forecasts, and air traffic controllers’ inputs. Simulations are performed for heavy and medium wake turbulence category aircraft. Results indicate that with 20 min look-ahead time, when outliers are not considered, along-track errors can reach up to 18 nmi, whereas altitude errors can reach up to around 13,000 ft. Cross-track errors in cruise highly depend on the lateral deviations due to Air Traffic Control instructions, and, in this study, are within 10 nmi. Wind conditions, vertical speed, calibrated airspeed, Mach number speed setting, and temporary level-offs are determined to be the most influential inputs.