Multistep Ahead Taxi-Out Time Prediction Using Long Short-Term Memory Networks

Abstract

For more reliable scheduling and management of air traffic, there is a need for a more accurate prediction of the taxi-out time before aircraft departure from the gate. This paper presents a multistep ahead taxi-out time prediction model that utilizes long short-term memory, considering airport surface congestion before aircraft departure. The comparison results showed that the prediction performance of the proposed model improves the accuracy compared to the three existing regression models and that the prediction error of the proposed model is consistent regardless of the traffic congestion on the airport surface.