Transactions of Nanjing University of Aeronautics & Astronautics
Prediction of Departure Aircraft Taxi Time Based on Deep Learning
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Abstract:
With the continuous increase in the number of flights, the use of airport collaborative decision-making（A-CDM） systems has been more and more widely spread. The accuracy of the taxi time prediction has an important effect on the A-CDM calculation of the departure aircraft’s take-off queue and the accurate time for the aircraft block-out. The spatial-temporal-environment deep learning (STEDL) model is presented to improve the prediction accuracy of departure aircraft taxi-out time. The model is composed of time-flow sub-model (airport capacity, number of taxiing aircraft, and different time periods), spatial sub-model (taxiing distance) and environmental sub-model (weather, air traffic control, runway configuration, and aircraft category). The STEDL model is used to predict the taxi time of departure aircraft at Hong Kong Airport and the results show that the STEDL method has a prediction accuracy of 95.4%. The proposed model also greatly reduces the prediction error rate compared with the other machine learning methods.
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U8
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