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Weather Impact on Airport Arrival Meter Fix Throughput
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Author and Affiliation:
Wang, Yao(NASA Ames Research Center, Moffett Field, CA, United States)
Abstract: Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.
Publication Date: Sep 17, 2017
Document ID:
20170011063
(Acquired Nov 19, 2017)
Subject Category: AIR TRANSPORTATION AND SAFETY
Report/Patent Number: ARC-E-DAA-TN44745
Document Type: Conference Paper
Meeting Information: 36th Digital Avionics Systems Conference (DASC 2017) ; 17-21 Sep. 2017; St. Petersburg, FL; United States
Meeting Sponsor: Institute of Electrical and Electronics Engineers; New York, NY, United States
American Inst. of Aeronautics and Astronautics; Reston, VA, United States
Financial Sponsor: NASA Ames Research Center; Moffett Field, CA, United States
Organization Source: NASA Ames Research Center; Moffett Field, CA, United States
Description: 10p; In English
Distribution Limits: Unclassified; Publicly available; Unlimited
Rights: No Copyright; Work of the U.S. Government - Public use permitted
NASA Terms: AIR TRAFFIC; AIRSPACE; AIR TRAFFIC CONTROLLERS (PERSONNEL); FLOW DISTRIBUTION; AIRPORTS; TRAFFIC; SPACING; CORRELATION
Other Descriptors: AIR TRAFFIC CONTROL; AIRPORT METER FIX; WEATHER; MACHINE LEARNING MODEL.
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Last Modified: November 19, 2017
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