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 minutes 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 a 20 minute look-ahead time, when outliers are not considered, along-track errors can reach up to 18 nautical miles, whereas altitude errors can reach up to around 13,000 feet. Cross-track errors in cruise highly depend on the lateral deviations due to ATC instructions, and, in this study, are within 10 nautical miles. Wind conditions, vertical speed, calibrated airspeed, Mach number speed setting, and temporary level-offs are determined to be the most influential inputs.

Trajectory Predictor and Conflict Detection Figures of Merit for a Performance-Based Adaptive Air Traffic Monitoring System

“…Predictions are always subject to error. The inaccuracies of predictions stem from diverse sources, leading to a high level of uncertainty [17][18][19][20], and their applicability depends on the operational context [21]. The consideration of this uncertainty is crucial in decision-making processes within ATM.…”

Air Traffic Prediction as a Video Prediction Problem Using Convolutional LSTM and Autoencoder

“…Air traffic prediction over different time horizons is one of the important research topics in the field of air traffic management for optimizing the use of airspace and airport resources in addition to safety purposes, such as conflict probes [1,2]. Various methods for aircraft trajectory prediction have been proposed [3][4][5][6][7], but their accuracy is still limited by many factors [8]. One of the major factors that contribute to an inaccurate trajectory prediction is that the trajectories often deviate from the filed flight plans [9].…”