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http://dx.doi.org/10.5139/JKSAS.2022.50.3.215

Machine Learning Based Capacity Prediction Model of Terminal Maneuvering Area  

Han, Sanghyok (Korea Aerospace University)
Yun, Taegyeong (Korea Aerospace University)
Kim, Sang Hyun (Korea Aerospace University)
Publication Information
Journal of the Korean Society for Aeronautical & Space Sciences / v.50, no.3, 2022 , pp. 215-222 More about this Journal
Abstract
The purpose of air traffic flow management is to balance demand and capacity in the national airspace, and its performance relies on an accurate capacity prediction of the airport or airspace. This paper developed a regression model that predicts the number of aircraft actually departing and arriving in a terminal maneuvering area. The regression model is based on a boosting ensemble learning algorithm that learns past aircraft operational data such as time, weather, scheduled demand, and unfulfilled demand at a specific airport in the terminal maneuvering area. The developed model was tested using historical departure and arrival flight data at Incheon International Airport, and the coefficient of determination is greater than 0.95. Also, the capacity of the terminal maneuvering area of interest is implicitly predicted by using the model.
Keywords
Air Traffic Flow Management; Capacity Prediction; Terminal Maneuvering Area; Machine learning;
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