Home Journals Articles Updates About
CN
image
收藏切换
Research on inland waterway congestion prediction method based on traffic wave theory
收藏切换
PDF
Qing YU1, 4, Weixin LAI1, Desheng CAO2, Chengpeng WAN2, 3, Xinyi SHEN1
Navigation of China | 2025, 48(1) : 50 - 59
Less
收藏切换
Navigation of China | 2025, 48(1): 50-59
• Marine Traffic Safety •
Research on inland waterway congestion prediction method based on traffic wave theory
Full
Qing YU1, 4, Weixin LAI1, Desheng CAO2, Chengpeng WAN2, 3, Xinyi SHEN1
Affiliations
  • 1.Navigation College, Jimei University, Xiamen 361021, China
  • 2.Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430070, China
  • 3.State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430070, China
  • 4.Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
Published: 2025-03-25 doi: 10.3969/j.issn.1000-4653.2025.01.007
Outline
收藏切换

Existing studies indicate that the longer the queue length of vessels, the greater the channel saturation. To predict channel congestion, this study proposes a congestion prediction method that considers the maximum queue length based on the fundamental principles of traffic wave theory. The model utilizes Automatic Identification System (AIS) data to extract traffic flow characteristic parameters and, considering the differences in navigation behavior among ships in different waters, proposes a method for dividing the channel into characteristic areas. The queue length in traffic wave theory is selected as the evaluation index for congestion, and a method for predicting the maximum queue length based on Gaussian process regression is proposed to achieve the prediction of waterway congestion levels. A case study is conducted in the Yuxi River section of the Yangtze River Basin. The results show that the theoretical value of the maximum queue length in this section in July 2020 is 0.98 km, and the Adjusted R2 index of the established regression model is 0.88, predicting a maximum queue length of 1.34 km with an error of 0.37 km compared to the theoretical value. The research results demonstrate that the proposed model has a high degree of interpretability and can effectively predict the maximum queue length, thereby enabling the prediction of channel congestion. This study provides a theoretical basis for improving the level of maritime supervision services.

DBSCAN clustering  /  traffic wave theory  /  maximum queue length  /  Gaussian process regression  /  traffic flow saturation
Qing YU, Weixin LAI, Desheng CAO, Chengpeng WAN, Xinyi SHEN. Research on inland waterway congestion prediction method based on traffic wave theory[J]. Navigation of China, 2025 , 48 (1) : 50 -59 . DOI: 10.3969/j.issn.1000-4653.2025.01.007
Appendix
Less
Year 2025 volume 48 Issue 1
PDF
58
16
Cite this Article
BibTeX
Article Info
doi: 10.3969/j.issn.1000-4653.2025.01.007
  • Receive Date：2023-12-18
  • Online Date：2026-03-17
  • Published：2025-03-25
Article Data
Affiliations
History
  • Received：2023-12-18
Funding
Affiliations
    1.Navigation College, Jimei University, Xiamen 361021, China
    2.Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430070, China
    3.State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430070, China
    4.Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
References
Share
https://castjournals.cast.org.cn/joweb/zghh/EN/10.3969/j.issn.1000-4653.2025.01.007
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