Taxi-Out Time Prediction Model at Charles de Gaulle Airport

Abstract

Accurate taxi-out time predictions are a valuable asset in enabling efficient runway scheduling in real-time operations so as to reduce taxi-out times and fuel consumption on the airport surface. This paper will focus on how the neural networks, regression tree, reinforcement learning, and multilayer perceptron methods can be used for predicting taxi-out time. These four methods are assessed based on their performance indicators, applied on Charles de Gaulle operational taxi data and benchmarked against real-life taxi-out time profiles. The root-mean-squared error metric is chosen as the most important performance indicator, which gives, for the applied regression tree method, on any given day, an average error of 1.6 min. The regression tree turns out to be the most efficient method, which is then subsequently applied in a case study for predicting the taxi-out time and finding the key-related precursors extracted from the top 10 features.