Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-12T21:59:23.088Z Has data issue: false hasContentIssue false
  • English
  • Français

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

Published online by Cambridge University Press:  18 June 2021

Lei Jinyu ,
Liu Lei ,
Chu Xiumin ,
He Wei ,
Liu Xinglong  and
Liu Cong
Lei Jinyu
Affiliation:
National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, China Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation, Minjiang University, Fuzhou, China
Liu Lei*
Affiliation:
School of Transportation, Southeast University, Nanjing, China
Chu Xiumin
Affiliation:
National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, China Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation, Minjiang University, Fuzhou, China
He Wei
Affiliation:
National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, China Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation, Minjiang University, Fuzhou, China
Liu Xinglong
Affiliation:
National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, China Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation, Minjiang University, Fuzhou, China
Liu Cong
Affiliation:
Department of Mechanical Engineering, Aalto University, Espoo, Finland
*
*Corresponding author. E-mail: lei1992@seu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

Keywords

ship domainautomatic identification systembridge-watersship collision
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 6 , November 2021 , pp. 1284 - 1304
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Azevedo, C. L., Ciuffo, B., Cardoso, J. L. and Ben-Akiva, M. E. (2015). Dealing with uncertainty in detailed calibration of traffic simulation models for safety assessment. Transportation Research Part C: Emerging Technologies, 58, 395–412.CrossRefGoogle Scholar
Chen, H. Z. and Guo, G. P. (2008). Research of ship domain and the traffic capacity in paratactic bridge water area. Ship & Ocean Engineering, 37, 113–116.Google Scholar
Chen, Z., Chen, D., Zhang, Y., Cheng, X., Zhang, M. and Wu, C. (2020). Deep learning for autonomous ship-oriented small ship detection. Safety Science, 130, 104812.CrossRefGoogle Scholar
Coldwell, T. (1983). Marine traffic behavior in restricted waters. The Journal of Navigation, 36, 430–444.CrossRefGoogle Scholar
Fan, X. H., Zhang, Q. N., Zhou, F., Tan, Z. R. and Wang, M. Q. (2013). Model of ship domain in river water. Journal of Dalian Maritime University, 39, 46–48.Google Scholar
Felski, A. and Jaskólski, K. (2013). The integrity of information received by means of AIS during anti-collision manoeuvring. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 7 (1).Google Scholar
Fujii, Y. and Tanaka, K. (1971). Traffic capacity. The Journal of Navigation, 24, 543–552.CrossRefGoogle Scholar
ITU. (2010). Recommendation M.1371-4, Technical characteristics for an automatic identification system using time-division multiple access in the VHF maritime mobile band. Geneva: International Telecommunication Union.Google Scholar
Goodwin, E. M. (1975). A statistical study of ship domains. The Journal of Navigation, 28, 328–344.CrossRefGoogle Scholar
Jia, C. Y. (1989). Ship domain in congested water area. Journal of Dalian Marine College, 15, 15–19.Google Scholar
Liu, S., Wang, N. and Wu, Z. (2011). Review of research on ship domain. Journal of Dalian Maritime University, 37, 51–54.Google Scholar
Montewka, J., Hinz, T., Kujala, P. and Matusiak, J. (2010). Probability modelling of vessel collisions. Reliability Engineering & System Safety, 95, 573–589.CrossRefGoogle Scholar
Pietrzykowski, Z. (2008). Ship's fuzzy domain – a criterion for navigational safety in narrow fairways. The Journal of Navigation, 61, 499–514.CrossRefGoogle Scholar
Pietrzykowski, Z. and Magaj, J. (2017). Ship domain as a safety criterion in a precautionary area of traffic separation scheme. Transnav International Journal on Marine Navigation & Safety of Sea Transportation, 11(1), 93–98.CrossRefGoogle Scholar
Pietrzykowski, Z. and Uriasz, J. (2004). The Ship Domain in a Deep-Sea Area. Proceeding of the 3rd International Conference on Computer and IT Applications in the Maritime Industries, Siguenza, Spain.Google Scholar
Pietrzykowski, Z. and Uriasz, J. (2006). Ship Domain in Navigational Situation Assessment in an Open Sea Area. Proceeding of the 5th International Conference on Computer and IT Applications in the Maritime Industries, Oegstgeest, Netherlands, pp. 135–141.Google Scholar
Pietrzykowski, Z. and Uriasz, J. (2009). The ship domain – a criterion of navigational safety assessment in an open sea area. The Journal of Navigation, 62, 93.CrossRefGoogle Scholar
Rawson, A. and Brito, M. (2020). A critique of the use of domain analysis for spatial collision risk assessment. Ocean Engineering, 219, 108259.Google Scholar
Rawson, A. and Brito, M. (2021). Developing contextually aware ship domains using machine learning. Journal of Navigation, 74(3), 515–532.CrossRefGoogle Scholar
Szlapczynski, R. and Szlapczynska, J. (2017). Review of ship safety domains: Models and applications. Ocean Engineering, 145C, 277–289.CrossRefGoogle Scholar
Wang, N. (2010). An intelligent spatial collision risk based on the quaternion ship domain. Journal of Navigation, 63, 733–749.CrossRefGoogle Scholar
Wang, Y., Zhang, J., Chen, X., Chu, X. and Yan, X. (2013). A spatial–temporal forensic analysis for inland-water ship collisions using AIS data. Safety Science, 57, 187–202.CrossRefGoogle Scholar
Wang, T. F., Yan, X. P., Wang, Y. and Wu, Q. (2017). Ship domain model for multi-ship collision avoidance decision-making with COLREGs based on artificial potential field. Transnav. International Journal on Marine Navigation & Safety of Sea Transportation, 11(1), 85–92.CrossRefGoogle Scholar
Wen, Y., Li, T., Zheng, H., Huang, L., Zhou, C. and Xiao, C (2018). Characteristics of ship domain in typical inland waters. Navigation of China, 41, 43–47.Google Scholar
Weng, J., Meng, Q. and Qu, X. (2012). Vessel collision frequency estimation in the Singapore Strait. The Journal of Navigation, 65, 207–221.CrossRefGoogle Scholar
Willems, N., Scheepens, R., van de Wetering, H. and van Wijk, J. J. (2013). Visualization of vessel traffic. In van de Laar, P., Tretmans, J., and Borth, M. (eds.). Situation Awareness with Systems of Systems, New York: Springer, 73–87.CrossRefGoogle Scholar
Xiang, Z., Hu, Q., Shi, Z. and Yang, C. (2015). Computation method of ship domains in restricted waters based on AIS data. Journal of Traffic and Transportation Engineering, 15, 110–117.Google Scholar
Xiao, F., Ligteringen, H., Gulijk, C. V. and Ale, B. (2015). Comparison study on AIS data of ship traffic behavior. Ocean Engineering, 95(feb.1), 84–93.CrossRefGoogle Scholar
Zhang, L. and Meng, Q. (2019). Probabilistic ship domain with applications to ship collision risk assessment. Ocean Engineering, 186, 106130-.CrossRefGoogle Scholar
Zhang, W., Goerlandt, F., Montewka, J. and Kujala, P. (2015). A method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 107, 60–69.CrossRefGoogle Scholar
Zhang, M., Zhang, D., Fu, S., Yan, X. and Goncharov, V. (2017). Safety distance modeling for ship escort operations in Arctic ice-covered waters. Ocean Engineering, 146, 202–216.CrossRefGoogle Scholar
Zhang, L., Meng, Q., Xiao, Z. and Fu, X. (2018). A novel ship trajectory reconstruction approach using AIS data. Ocean Engineering, 159, 165–174.CrossRefGoogle Scholar
Zhang, M., Zhang, D., Yao, H. and Zhang, K. (2020). A probabilistic model of human error assessment for autonomous cargo ships focusing on human–autonomy collaboration. Safety Science, 130, 104838.CrossRefGoogle Scholar
Zhang, M., Montewka, J., Manderbacka, T., Kujala, P. and Hirdaris, S. (2021). A big data analytics method for evaluation of ship-ship collision risk reflecting hydrometeorological conditions. Reliability Engineering & System Safety, 213, 107674.CrossRefGoogle Scholar
Zhu, X., Xu, H. and Lin, J. (2001). Domain and its model based on neural networks. The Journal of Navigation, 54, 97.CrossRefGoogle Scholar