Predicting the operational acceptance of airborne flight reroute requests using data mining
Authors:
- 1 November 2018
- journal article
- research article
- Published by Elsevier in Transportation Research Part C: Emerging Technologies
- Vol. 96, 270-289
Abstract
No abstract availableKeywords
Funding Information
- NASA (NNA16BD14C)
This publication has 15 references indexed in Scilit:
- Prediction of aircraft performances based on data collected by air traffic control centersTransportation Research Part C: Emerging Technologies, 2016
- A methodology for identifying similar days in air traffic flow management initiative planningTransportation Research Part C: Emerging Technologies, 2016
- Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoringTransportation Research Part C: Emerging Technologies, 2016
- Dynamic Weather Routes: Two Years of Operational Testing at American AirlinesAir Traffic Control Quarterly, 2015
- A framework for the assessment of collaborative en route resource allocation strategiesTransportation Research Part C: Emerging Technologies, 2013
- Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: A case-study of Tampa Bay departuresTransportation Research Part C: Emerging Technologies, 2010
- An introduction to ROC analysisPattern Recognition Letters, 2006
- SMOTE: Synthetic Minority Over-sampling TechniqueJournal of Artificial Intelligence Research, 2002
- Random ForestsMachine Learning, 2001
- A Decision-Theoretic Generalization of On-Line Learning and an Application to BoostingJournal of Computer and System Sciences, 1997