Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning
Open Access
- 1 August 2021
- journal article
- research article
- Published by Elsevier in Journal of Air Transport Management
- Vol. 95
Abstract
No abstract availableKeywords
Funding Information
- Ministerio de Ciencia, Innovación y Universidades (RTI2018-098471-B-C32)
This publication has 14 references indexed in Scilit:
- A data-driven methodology for characterization of a terminal manoeuvring area in multi-airport systemsTransportation Research Part C: Emerging Technologies, 2020
- Multi-aircraft optimal 4D online trajectory planning in the presence of a multi-cell storm in developmentTransportation Research Part C: Emerging Technologies, 2020
- A machine learning approach to air traffic interdependency modelling and its application to trajectory predictionTransportation Research Part C: Emerging Technologies, 2019
- Robust aircraft trajectory planning under uncertain convective environments with optimal control and rapidly developing thunderstormsAerospace Science and Technology, 2019
- A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring areaTransportation Research Part C: Emerging Technologies, 2018
- On maximizing safety in stochastic aircraft trajectory planning with uncertain thunderstorm developmentAerospace Science and Technology, 2018
- Probabilistic Planning and Risk Evaluation Based on Ensemble Weather ForecastingIEEE Transactions on Automation Science and Engineering, 2018
- A machine learning nowcasting method based on real‐time reanalysis dataJournal of Geophysical Research: Atmospheres, 2017
- Evaluation of geostationary satellite observations and the development of a 1–2 h prediction model for future storm intensityJournal of Geophysical Research: Atmospheres, 2016
- Dynamic Weather Routes: Two Years of Operational Testing at American AirlinesAir Traffic Control Quarterly, 2015