A Data-Driven Approach for Taxi-Time Prediction: A Case Study of Singapore Changi Airport

Abstract

The ground movement is one of the most critical airside operations. It includes two sub-problems: routing and scheduling and serves the purpose of guiding aircraft on the surface of an airport to meet the departure schedule while minimizing overall travel time. To achieve that purpose, ground movement controllers manage the taxi-route assignments and taxi-time estimation for each aircraft in arrival or departure queue. A high-accuracy taxi-time calculation is required to increase the efficiency of airport operations. In this study, we propose a data-driven approach to construct features set and build predictive models for taxi-time prediction for departure flights. The proposed approach can suggest the taxi-route and predict the corresponding taxi-time by analyzing ground movement data. The controller’s operational preferences are extracted and learned by machine learning algorithms for predicting taxi-route and taxi-time of given aircraft. In this approach, we take advantage of taxiing trajectories to learn the controller’s decision, which reflects how the controller had decided the routing for a given situation. Two machine learning models, random forest regression, and linear regression are implemented and show similar performances in estimating the taxi-time. However, since the random forest is an ensemble method that has advantages in handling outliers, performing feature selection, and assessing feature importance, it can provide more stable results and interpretability, for real operations. The predictive model for taxi-time can predict the taxi-out time with high accuracy with given assigned taxi-route. The model can cover the controller’s decision up to 70% in the top-1 and 89% in top-2 recommends. The mean absolute error is less than 2.07 min for all departure flights, and root mean square error is approximately 2.5 min. Moreover, the ± 3-minute error window can cover around 76% of departures, while more than 95% of departures are within the ± 5-minute error window.