Machine Learning Techniques for Taxi-out Time Prediction with a Macroscopic Network Topology

Accurate prediction of taxi-out time is essential for enhancing airport performance and flight efficiency. In this paper, we apply machine learning techniques to predict the taxi-out time of departure aircraft at Shanghai Pudong International Airport. The exploration of historical data reveals several relevant influencing factors of taxi-out time as well as their correlations. We formulate an extensive system of predictors for our machine learning approach, based on a macroscopic network topology from an aggregate view. The predictors can be divided into 4 categories; namely surface instantaneous flow indices (SIFIs), surface cumulative flow indices (SCFIs), aircraft queue length indices (AQLIs) and slot resource demand indices (SRDIs). Three machine learning methods: linear regression (LR), support vector machines (SVM) and random forest (RF) are formulated using one-day and one-month training samples, and applied to new test dataset to validate the prediction performance. Computational results show that the training RF model using one-month sample significantly outperform other models in terms of prediction accuracy. The proposed methodology can bring significant benefits to analyzing airport ground movement performance and support the activities of airport decision making.