Predicting the operational acceptance of airborne flight reroute requests using data mining

For 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.

• Record URL:
  • https://doi.org/10.1016/j.trc.2018.09.024
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X18313901
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Evans, Antony D
  • Lee, Paul
  • Sridhar, Banavar
• Publication Date: 2018-11

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 270-289
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 96
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Acceptance; Air traffic control; Aircraft operations; Automation; Data mining; Decision trees; Logistic regression analysis; Routing
• Uncontrolled Terms: Cross validation; Support vector machines
• Geographic Terms: Fort Worth (Texas)
• Subject Areas: Aviation; Data and Information Technology; Operations and Traffic Management;

Filing Info

• Accession Number: 01684383
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 26 2018 5:15PM