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Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning TechniquesAccurate taxi time prediction can be used for more efficient runway scheduling to increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. This paper describes two different approaches to predicting taxi times, which are a data-driven analytical method using machine learning techniques and a fast-time simulation-based approach. These two taxi time prediction methods are applied to realistic flight data at Charlotte Douglas International Airport (CLT) and assessed with actual taxi time data from the human-in-the-loop simulation for CLT airport operations using various performance measurement metrics. Based on the preliminary results, we discuss how the taxi time prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast-time simulation model for implementing it with an airport scheduling algorithm in real-time operational environment.
Document ID
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Lee, Hanbong
(California Univ. Santa Cruz, CA, United States)
Malik, Waqar
(California Univ. Santa Cruz, CA, United States)
Zhang, Bo
(NASA Ames Research Center Moffett Field, CA, United States)
Nagarajan, Balaji
(NASA Ames Research Center Moffett Field, CA, United States)
Jung, Yoon C.
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
July 9, 2019
Publication Date
June 21, 2015
Subject Category
Air Transportation And Safety
Report/Patent Number
Meeting Information
Meeting: AIAA AVIATION Forum
Location: Dallas, TX
Country: United States
Start Date: June 22, 2015
End Date: June 26, 2015
Sponsors: American Institute of Aeronautics and Astronautics (AIAA)
Funding Number(s)
WBS: 411931
Distribution Limits
Public Use Permitted.
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