Finding Similar Historical Scenarios for Better Understanding Aircraft Taxi Time: A Deep Metric Learning Approach

Abstract

Accurate prediction of aircraft taxi time is crucial to optimizing route scheduling and improving airport efficiency. However, studies usually give only a single deterministic taxi time predicted by models, which lacks interpretability and affects controllers’ trust. To solve this problem, using the idea of auxiliary decision-making based on similar scenarios, a deep metric learning model is proposed to learn the similarity among historical situations, consisting of flight properties, surface traffic, and meteorological conditions. Combining the model with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest-neighbor regression algorithm, a set of historical circumstances similar to a reference scenario can be found and used to predict the taxi time. Experimental verification using Shanghai Pudong International Airport data shows that the model achieves better performance than the state-of-the-art variants for taxi-out time prediction accuracy. More importantly, similar historical scenarios can provide insights into the uncertainty quantification of aircraft taxi time and serve as a basis for controllers to make decisions. The methodology enriches decision support tools and enhances airports’ refined management capabilities.