Home Journals Articles Updates About
CN
image
收藏切换
Prediction of airport arrival delay level based on spatiotemporal association rules and LSTM
收藏切换
PDF
Shanmei LI1, Duanyang WANG1, Rui TANG2, Yanwei LI3, Jinhui LI1, Yahong JI1
China Safety Science Journal | 2025, 35(4) : 59 - 66
Less
收藏切换
China Safety Science Journal | 2025, 35(4): 59-66
• Safety engineering technology •
Prediction of airport arrival delay level based on spatiotemporal association rules and LSTM
Full
Shanmei LI1, Duanyang WANG1, Rui TANG2, Yanwei LI3, Jinhui LI1, Yahong JI1
Affiliations
  • 1 College of Air Traffic Management，Civil Aviation University of China，Tianjin 300300，China
  • 2 Operation Supervisory Centre，Civil Aviation Administration of China，Beijing 100710，China
  • 3 College of Economics and Management，Civil Aviation University of China，Tianjin 300300，China
Published: 2025-04-28 doi: 10.16265/j.cnki.issn1003-3033.2025.04.0476
Outline
收藏切换

To improve the safety of air traffic operations，a delay level prediction method based on the combination of spatiotemporal association rule mining and deep learning was proposed. Firstly，the average flight delay time and delay rate were selected as airport delay metrics，and their spatial-temporal correlation characteristics were analyzed. Secondly，the airport delay levels were identified based on Fuzzy-C Means (FCM)clustering algorithm，and the spatiotemporal association rules of airport delay were mined based on (FP(Frequent Pattern)Growth) algorithm. Thirdly，sample data was constructed based on association rules and delay time series，which was put into LSTM model to predict the future airport delay levels. At the same time，attention mechanism was introduced into the prediction model to learn the influence of different rules on prediction. Finally，the actual US flight data were collected for example analysis. The results show that the average prediction accuracy of overall delay levels reaches 0.91 and the prediction accuracy of different periods is all larger than 80%. The connection weight of the attention layer network reflects the influence of each rule on the prediction，which can be used to explain the prediction results.

spatiotemporal association rules  /  long short term memory (LSTM)  /  airport arrival  /  delay level  /  delay prediction  /  air traffic management
Shanmei LI, Duanyang WANG, Rui TANG, Yanwei LI, Jinhui LI, Yahong JI. Prediction of airport arrival delay level based on spatiotemporal association rules and LSTM[J]. China Safety Science Journal, 2025 , 35 (4) : 59 -66 . DOI: 10.16265/j.cnki.issn1003-3033.2025.04.0476
Appendix
Less
Year 2025 volume 35 Issue 4
PDF
402
159
Cite this Article
BibTeX
Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.04.0476
  • Receive Date：2024-12-15
  • Online Date：2025-07-05
  • Published：2025-04-28
Article Data
Affiliations
History
  • Received：2024-12-15
  • Revised：2025-02-16
Funding
Affiliations
    1 College of Air Traffic Management，Civil Aviation University of China，Tianjin 300300，China
    2 Operation Supervisory Centre，Civil Aviation Administration of China，Beijing 100710，China
    3 College of Economics and Management，Civil Aviation University of China，Tianjin 300300，China
References
Share
https://castjournals.cast.org.cn/joweb/zgaqkxxb/EN/10.16265/j.cnki.issn1003-3033.2025.04.0476
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House