Scipediacontent: Scipediacontent moved page Draft Content 908703762 to Wang et al 2018b

2021-01-28T20:45:49Z

Scipediacontent moved page Draft Content 908703762 to Wang et al 2018b

Scipediacontent: Created page with " == Abstract == This paper is an extension of work originally reported in SESAR Innovation Days 2018 : Zhengyi Wang, Man Liang, Daniel Delahaye. Automated Data-Driven Predict..."

2021-01-28T20:45:44Z

Created page with " == Abstract == This paper is an extension of work originally reported in SESAR Innovation Days 2018 : Zhengyi Wang, Man Liang, Daniel Delahaye. Automated Data-Driven Predict..."

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== Abstract ==

This paper is an extension of work originally reported in SESAR Innovation Days 2018 : Zhengyi Wang, Man Liang, Daniel Delahaye. Automated Data-Driven Prediction on Aircraft Estimated Time of Arrival. SID 2018, 8th Sesar Innovations Days, Dec 2018, Salzburg, Austria. ⟨hal-01944608⟩; International audience; 4D trajectory prediction is the core element of the future air transportation system. It aims to improve the operational ability and the predictability of air traffic. In this paper, a novel automated data-driven framework to deal with the prediction of Estimated Time of Arrival (ETA) on the runway at the entry point of Terminal Manoeuvring Area (TMA) is introduced. The proposed framework mainly consists of data preprocessing and machine learning models. Firstly, the dataset is divided, analyzed, cleaned, and estimated. Then, the flights are clustered into partitions according to different runway-in-use (QFU). Several candidate machine learning models are trained and selected on the corresponding dataset of each QFU. Feature engineering is conducted to transform raw data into features. After that, the experiments are performed on real ADS-B data in Beijing TMA with nested cross validation. By comparing the prediction performance on the preprocessed and un-preprocessed datasets, the results demonstrate that the proposed data preprocessing is able to improve the data quality. It is also robust to outliers, missing data, and noise. Finally, an ensemble learning strategy named stacking is introduced. Compared to other individual models, the stacked model has a more complex structure and performs best in ETA prediction. This fact reveals that the framework proposed in this study could make accurate and reliable ETA predictions.

== Original document ==

The different versions of the original document can be found in:

* [https://hal-enac.archives-ouvertes.fr/hal-02612361 https://hal-enac.archives-ouvertes.fr/hal-02612361]

* [https://hal-enac.archives-ouvertes.fr/hal-02612361/file/Automated%20Data-Driven%20Prediction%20on%20Aircraft%20Estimated%20Time%20of%20Arrival.pdf https://hal-enac.archives-ouvertes.fr/hal-02612361/file/Automated%20Data-Driven%20Prediction%20on%20Aircraft%20Estimated%20Time%20of%20Arrival.pdf]

* [https://hdl.handle.net/11541.2/135989 https://hdl.handle.net/11541.2/135989]

* [https://hdl.handle.net/11541.2/143570 https://hdl.handle.net/11541.2/143570]

* [https://api.elsevier.com/content/article/PII:S0969699719304429?httpAccept=text/xml https://api.elsevier.com/content/article/PII:S0969699719304429?httpAccept=text/xml],
: [https://api.elsevier.com/content/article/PII:S0969699719304429?httpAccept=text/plain https://api.elsevier.com/content/article/PII:S0969699719304429?httpAccept=text/plain],
: [http://dx.doi.org/10.1016/j.jairtraman.2020.101840 http://dx.doi.org/10.1016/j.jairtraman.2020.101840] under the license https://www.elsevier.com/tdm/userlicense/1.0/

* [https://hal-enac.archives-ouvertes.fr/hal-01944608 https://hal-enac.archives-ouvertes.fr/hal-01944608],
: [https://hal-enac.archives-ouvertes.fr/hal-01944608/document https://hal-enac.archives-ouvertes.fr/hal-01944608/document],
: [https://hal-enac.archives-ouvertes.fr/hal-01944608/file/SIDs_2018_paper_2.pdf https://hal-enac.archives-ouvertes.fr/hal-01944608/file/SIDs_2018_paper_2.pdf]

* [https://hal-enac.archives-ouvertes.fr/hal-02612361 https://hal-enac.archives-ouvertes.fr/hal-02612361],
: [https://hal-enac.archives-ouvertes.fr/hal-02612361/document https://hal-enac.archives-ouvertes.fr/hal-02612361/document],
: [https://hal-enac.archives-ouvertes.fr/hal-02612361/file/Automated%20Data-Driven%20Prediction%20on%20Aircraft%20Estimated%20Time%20of%20Arrival.pdf https://hal-enac.archives-ouvertes.fr/hal-02612361/file/Automated%20Data-Driven%20Prediction%20on%20Aircraft%20Estimated%20Time%20of%20Arrival.pdf]

* [https://www.sciencedirect.com/science/article/pii/S0969699719304429 https://www.sciencedirect.com/science/article/pii/S0969699719304429],
: [https://hal-enac.archives-ouvertes.fr/hal-02612361 https://hal-enac.archives-ouvertes.fr/hal-02612361],
: [https://hal-enac.archives-ouvertes.fr/hal-02612361/document https://hal-enac.archives-ouvertes.fr/hal-02612361/document],
: [https://academic.microsoft.com/#/detail/3042273304 https://academic.microsoft.com/#/detail/3042273304]

* [ ]

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Scipediacontent: Scipediacontent moved page Draft Content 908703762 to Wang et al 2018b

Scipediacontent: Created page with " == Abstract == This paper is an extension of work originally reported in SESAR Innovation Days 2018 : Zhengyi Wang, Man Liang, Daniel Delahaye. Automated Data-Driven Predict..."