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Landing Runway Assignment by Airport Traffic using Machine Learning
Author(s)
Date Issued
May 27, 2022
Type
Conference Paper
Abstract
This paper presents the solutions to the overwhelming burden of air traffic controllers by reducing workload and optimizing runway capacity using machine learning tools to assign runways for incoming aircraft based on critical information such as aerodrome traffic information of aircraft taking off and landing on the runway at Suvarnabhumi Airport, THAILAND. The model is composed of four layers and three hidden layers. ReLU and Adam are the activation and optimization functions used in this model, respectively. The model was trained using assigned landing runway and traffic runway factors. Predicting the assigned runway is 82.77 percent accurate.
Citation
ACM International Conference Proceeding Series, 40-45, 2022