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Predicting the Operational Acceptance of Airborne Flight Reroute Requests Using Data MiningFor tools that generate more efficient flight routes or reroute advisories, it is important to ensure compatibility of automation and autonomy decisions with human objectives so as to ensure acceptability by the human operators. In this paper, the authors developed a proof of concept predictor of operational acceptability for route changes during a flight. Such a capability could have applications in automation tools that identify more efficient routes around airspace impacted by weather or congestion and that better meet airline preferences. The predictor is based on applying data mining techniques, including logistic regression, a decision tree, a support vector machine, a random forest and Adaptive Boost, to historical flight plan amendment data reported during operations and field experiments. Cross validation was used for model development, while nested cross validation was used to validate the models. The model found to have the best performance in predicting air traffic controller acceptance or rejection of a route change, using the available data from Fort Worth Air Traffic Control Center and its adjacent Centers, was the random forest, with an F-score of 0.77. This result indicates that the operational acceptance of reroute requests does indeed have some level of predictability, and that, with suitable data, models can be trained to predict the operational acceptability of reroute requests. Such models may ultimately be used to inform route selection by decision support tools, contributing to the development of increasingly autonomous systems that are capable of routing aircraft with less human input than is currently the case.
Document ID
20190026628
Acquisition Source
Ames Research Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
Antony D. Evans (Universities Space Research Association Columbia, Maryland, United States)
Paul Lee (Ames Research Center Mountain View, California, United States)
Banavar Sridhar (Universities Space Research Association Columbia, Maryland, United States)
Date Acquired
June 25, 2019
Publication Date
October 4, 2018
Publication Information
Publication: Transportation Research Part C: Emerging Technologies
Publisher: Elsevier
Volume: 96
Issue Publication Date: November 1, 2018
ISSN: 0968-090X
e-ISSN: 1879-2359
DOI: 10.1016/j.trc.2018.09.024
Subject Category
Air Transportation And Safety
Report/Patent Number
ARC-E-DAA-TN61783
Funding Number(s)
CONTRACT_GRANT: NNA16BD14C
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
NASA Technical Management
Keywords
Operational Acceptability
Air Traffic Management
Airborne Reroute
Data Mining

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