Sequence-to-sequence transfer transformer network for automatic flight plan generation
文献类型：期刊论文
| 作者 | Yang, Yang1,2; Qian, Shengsheng3 ; Zhang, Minghua2,4; Cai, Kaiquan2,4
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| 刊名 | IET INTELLIGENT TRANSPORT SYSTEMS
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| 出版日期 | 2023-12-21 |
| 页码 | 12 |
| ISSN号 | 1751-956X |
| 关键词 | air transportation artificial intelligence data mining |
| DOI | 10.1049/itr2.12478 |
| 通讯作者 | Cai, Kaiquan(caikq@buaa.edu.cn) |
| 英文摘要 | In this work, a machine translation framework is proposed to tackle the flight plan generation in the air transport field. Diverging from the traditional human expert-based way, a novel sequence-to-sequence transfer transformer network to automatic flight plan generation with enhanced operational acceptability is presented. It allows the user to translate the departure and arrival airport pairs denoted as test sentences, into the flyable waypoint sequences denoted as the corresponding source sentences. The approach leverages deep neural networks to autonomously learn air transport specialized knowledge and human expert insights from industry legacy data. Moreover, a multi-head attention mechanism is adopted to model the complex correlation between airport pairs. Besides, we introduce an innovative waypoint embedding layer to learn effective embeddings for waypoint sequences. Additionally, an extensive flight plan dataset is constructed utilizing real-world data in China spanning from July to September 2019. Employing the proposed model, rigorous training and testing procedures are conducted on this dataset, yielding remarkably favourable outcomes based on automatic evaluation metrics that are BLEU and METEOR, which outperform other popular approaches. More importantly, the proposed approach achieves high performance in the operational validation and visualization, showing its application potential for real-world air traffic operation. In this work, a machine translation framework is proposed to tackle the flight plan generation in the air transport field. Diverging from the traditional human expert-based way, a novel sequence-to-sequence transfer transformer network is presented to automatic flight plan generation with enhanced operational acceptability. It allows the user to translate the departure and arrival airport pairs denoted as test sentences, into the flyable waypoint sequences denoted as the corresponding source sentences.image |
| 资助项目 | National Key Research and Development Program of China ; Funds of National Natural Science Foundation of China ; [2022YFB2602402] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| 出版者 | WILEY |
| WOS记录号 | WOS:001128745300001 |
| 资助机构 | National Key Research and Development Program of China ; Funds of National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54949]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Cai, Kaiquan |
| 作者单位 | 1.Beihang Univ, Res Inst Frontier Sci, Beijing, Peoples R China 2.State Key Lab CNS ATM, Beijing, Peoples R China 3.Chinese Acad Sci, Univ Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 4.Beihang Univ, Sch Elect & Informat Engn, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Yang,Qian, Shengsheng,Zhang, Minghua,et al. Sequence-to-sequence transfer transformer network for automatic flight plan generation[J]. IET INTELLIGENT TRANSPORT SYSTEMS,2023:12. |
| APA | Yang, Yang,Qian, Shengsheng,Zhang, Minghua,&Cai, Kaiquan.(2023).Sequence-to-sequence transfer transformer network for automatic flight plan generation.IET INTELLIGENT TRANSPORT SYSTEMS,12. |
| MLA | Yang, Yang,et al."Sequence-to-sequence transfer transformer network for automatic flight plan generation".IET INTELLIGENT TRANSPORT SYSTEMS (2023):12. |
入库方式： OAI收割
来源：自动化研究所
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