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Prediction of Aircraft Estimated Time of Arrival Using A Supervised Learning ApproachWe present a novel data-driven approach for prediction of the estimated time of arrival (ETA) of aircraft in the terminal area via the implementation of a Random Forest regression model. The model uses data fused from a number of sources (flight track, weather, flight plan information, etc.) and provides predictions for the remaining flight time for aircraft landing at Dallas/Fort Worth (DFW) International Airport. The predictions are made when the aircraft is at a distance of 200-miles from the airport. The results show that the model is able to predict estimated time of arrival to within ± 5 min for 90% of the flights in the test data with the mean absolute error being lower at 145 seconds. This paper covers the entire pipeline of data collection, preprocessing, setup and training of the ML model, and the results obtained for DFW.
Document ID
20230010340
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
James Z Wells
(University of Cincinnati Cincinnati, Ohio, United States)
Tejas G Puranik
(Universities Space Research Association Columbia, Maryland, United States)
Krishna M Kalyanam
(Ames Research Center Mountain View, California, United States)
Manish Kumar
(University of Cincinnati Cincinnati, Ohio, United States)
Date Acquired
July 14, 2023
Subject Category
Air Transportation and Safety
Meeting Information
Meeting: 3rd Modeling, Estimation, and Control Conference
Location: Lake Tahoe, NV
Country: US
Start Date: October 2, 2023
End Date: October 5, 2023
Sponsors: American Automatic Control Council, International Federation of Automatic Control
Funding Number(s)
CONTRACT_GRANT: OSTEM-Intern-Multi
CONTRACT_GRANT: NNA16BD14C
PROJECT: 031102
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
NASA Technical Management
Keywords
Machine learning
Random forest regression
Estimated time of arrival
Air traffic management
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