| タイトル | Using Historical Data to Automatically Identify Air-Traffic Control Behavior |
| 本文（外部サイト） | http://hdl.handle.net/2060/20140008654 |
| 著者（英） | Lauderdale, Todd A.; Tretto, Celeste; Wu, Yuefeng |
| 著者所属（英） | NASA Ames Research Center |
| 発行日 | 2014-02-25 |
| 言語 | eng |
| 内容記述 | This project seeks to develop statistical-based machine learning models to characterize the types of errors present when using current systems to predict future aircraft states. These models will be data-driven - based on large quantities of historical data. Once these models are developed, they will be used to infer situations in the historical data where an air-traffic controller intervened on an aircraft's route, even when there is no direct recording of this action. |
| NASA分類 | Air Transportation and Safety |
| レポートNO | ARC-E-DAA-TN13430 |
| 権利 | Copyright, Distribution as joint owner in the copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/71546 |
