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Real-time Unimpeded Taxi Out Machine Learning Service.
| 题名： | Real-time Unimpeded Taxi Out Machine Learning Service. |
| 作者： | Alexandre Amblard##Sarah YoultonWilliam J. Coupe |
| 摘要： | This paper describes a study on the estimation of the unimpeded taxi out time using Machine Learning (ML) tools and proposes an implementation that can be used to make real-time predictions at any airport in the National Airspace System. Kedro, an open-source pipeline framework, is used to develop the model definition and training. Models are stored in scikit-learn containers on a MLFlow server where they can be retrieved and served to make predictions in the live system. These open source frameworks provide common structures between ML services, allow for easier maintenance and updates, and overall deliver an easier CI/CD (Continuous Integration/Continuous Deployment) process. The current models were trained on data acquired at KCLT and KDFW from June 1 st to December 31 st , 2019 and computetaxitimeintheramp,airportmovementarea(AMA)andtotal(fromgatestorunways). The current versions of the models achieve relatively low uncertainties of about 10 to 15% for the total and AMA taxi times and about 20% for the ramp taxi time at both KCLT and KDFW. Initial tests on offline data from 2020 and 2021 show a small degradation (10 to 15%) in accuracy performance indicating the model’s resilience to operational changes over time. |
| 总页数： | 6 |
| 报告类型： | 科技报告 |
| 发布日期： | 2021 |
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