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Prediction of Weather Impacts on Airport Arrival Meter Fix Capacity
ISSN: 2641-9637, e-ISSN: 2641-9645
Published March 19, 2019 by SAE International in United States
Event: AeroTech Americas
Citation: Wang, Y., "Prediction of Weather Impacts on Airport Arrival Meter Fix Capacity," SAE Int. J. Adv. & Curr. Prac. in Mobility 1(2):343-351, 2019, https://doi.org/10.4271/2019-01-1350.
This paper introduces a data driven model for predicting airport arrival capacity with 2-8 hour look-ahead forecast data. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport’s arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system. Severe imbalances between demand and capacity occur most frequently when the airport capacity is severely degraded due to either bad airport terminal surface weather or inclement convective weather around airport arrival fixes. A model that predicts the weather-impacted airport arrival meter fix throughput may help ATM personnel to plan GDP operations more efficiently. This paper identifies the characteristics of air traffic flow across arrival meter fixes at Newark Liberty International Airport (EWR). The proposed approach, based on machine-learning methods, is developed to predict the weather impacted EWR arrival Meter Fix (MF) throughput. Sector forecast coverage is used to envision the weather impact on airport arrival MF flow, and the validation is accomplished by using Convective Weather Avoidance Model (CWAM) 0.5 to 2-hour and Collaborative Convective Forecast Product (CCFP) 4 to 8-hour look-ahead forecast data for the period of April-September in 2014. Furthermore, the regression tree ensemble learning of random forests approach for translating a sector forecast coverage model to EWR arrival meter fix throughput is examined. The results suggest that ATM decision makers in charge of MF flow control and GDP planning may benefit from adopting the airport arrival meter capacity prediction models to estimate the inclement weather impacts.
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