Machine Learning-Based Prediction of Aircraft Taxi-In Time: A Case Study at Beijing Capital International Airport

Abstract

Accurate prediction of flight taxi-in time has a significant meaning in allocating aircraft support resources reasonably and improving airport surface movement efficiency. It can effectively overcome the deficiency of extensive aircraft arrival time prediction in major airports currently. Taking Beijing Capital International Airport as the research object, we analyzed the influence factors of taxi-in time and constructed the feature set; Then we applied linear regression, K-nearest neighbor, support vector regression, decision tree, random forest and gradient boosting regression tree, which were widely used in the prediction of taxi-out time, to predict the taxi-in time. The results show that the prediction accuracy of the six machine learning models is over 90%within \pm3minutes, it means that the construction of feature set and the selection of models are effective; The gradient boosting regression tree model has the best performance based on the prediction results and model fitting evaluation results; The surface traffic flow features have the largest contribution to the prediction model, and the newly introduced cross-regional feature has more contribution to the prediction model than most traditional features, according to the prediction results of gradient boosting regression tree.