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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.12244 |
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| _version_ | 1866914803789856768 |
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| author | Liu, Ke Ding, Kaijing Dai, Lu Hansen, Mark Chan, Kennis Schade, John |
| author_facet | Liu, Ke Ding, Kaijing Dai, Lu Hansen, Mark Chan, Kennis Schade, John |
| contents | In this paper, we employ the long-short-term memory model (LSTM) to predict the real-time go-around probability as an arrival flight is approaching JFK airport and within 10 nm of the landing runway threshold. We further develop methods to examine the causes to go-around occurrences both from a global view and an individual flight perspective. According to our results, in-trail spacing, and simultaneous runway operation appear to be the top factors that contribute to overall go-around occurrences. We then integrate these pre-trained models and analyses with real-time data streaming, and finally develop a demo web-based user interface that integrates the different components designed previously into a real-time tool that can eventually be used by flight crews and other line personnel to identify situations in which there is a high risk of a go-around. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12244 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-Time Go-Around Prediction: A case study of JFK airport Liu, Ke Ding, Kaijing Dai, Lu Hansen, Mark Chan, Kennis Schade, John Physics and Society Machine Learning In this paper, we employ the long-short-term memory model (LSTM) to predict the real-time go-around probability as an arrival flight is approaching JFK airport and within 10 nm of the landing runway threshold. We further develop methods to examine the causes to go-around occurrences both from a global view and an individual flight perspective. According to our results, in-trail spacing, and simultaneous runway operation appear to be the top factors that contribute to overall go-around occurrences. We then integrate these pre-trained models and analyses with real-time data streaming, and finally develop a demo web-based user interface that integrates the different components designed previously into a real-time tool that can eventually be used by flight crews and other line personnel to identify situations in which there is a high risk of a go-around. |
| title | Real-Time Go-Around Prediction: A case study of JFK airport |
| topic | Physics and Society Machine Learning |
| url | https://arxiv.org/abs/2405.12244 |