NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
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
20190027063
Document Type
Conference Paper
Authors
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
ARC-E-DAA-TN19294
Meeting Information
AIAA AVIATION Forum(Dallas, TX)
Funding Number(s)
WBS: 411931
CONTRACT_GRANT: NAS2-03144
Distribution Limits
Public
Copyright
Public Use Permitted.

Available Downloads

NameType 20190027063.pdf STI

Related Records

IDRelationTitle20160004946See AlsoTaxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques
No Preview Available