Sequence-to-sequence transfer transformer network for automatic flight plan generation

doi:10.1049/itr2.12478

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Sequence-to-sequence transfer transformer network for automatic flight plan generation
	Yang, Yang 1,2; Qian, Shengsheng 3; Zhang, Minghua 1,4; Cai, Kaiquan 1,4
刊名	IET INTELLIGENT TRANSPORT SYSTEMS
	2023-12-21
页码	12
关键词	air transportation artificial intelligence data mining
ISSN号	1751-956X
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
内容类型	期刊论文
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.State Key Lab CNS ATM, Beijing, Peoples R China 2.Beihang Univ, Res Inst Frontier Sci, 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.