Decision Support for Aircraft Taxi Time based on Deep Metric Learning

As an essential part of a flight life cycle, the surface taxiing process is closely related to the operational efficiency of the airport. Routing and scheduling can be optimized with an accurate prediction of aircraft taxi time in advance, thus improving the ability of refined management of airport surface. However, the past methods merely provide a taxi time predicted by their models, which are of limited help to airport controllers in decision-making. In order to alleviate this problem, this paper proposes to use a deep metric learning (DML) model to learn the similarity between historical operation scenarios based on basic flight properties, surface traffic situation, and airport weather information. For a given reference flight, the taxi time can be reasonably predicted by finding its similar historical scenarios. In this way, the relevant controllers can make flexible decisions at the tactical level. Experimental verification on the historical data of Shanghai Pudong International Airport shows that the deep model can effectively capture the similarity of taxi time between different scenarios. Besides, compared with the classical machine learning prediction models, the proposed model can predict the taxi time more accurately. With similar historical scenarios as the basis for decision support, the implementation and interpretability of Airport Collaborative Decision-Making (A-CDM) system will be enhanced.