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Ship domain model for ships with restricted manoeuvrability in busy waters

Published online by Cambridge University Press:  22 January 2021

Wei Pan ,
Xin-lian Xie ,
Tian-tian Bao  and
Meng Li
Wei Pan
Affiliation:
Integrated Transport Institute, Dalian Maritime University, Dalian116026, China.
Xin-lian Xie*
Affiliation:
Integrated Transport Institute, Dalian Maritime University, Dalian116026, China.
Tian-tian Bao
Affiliation:
Faculty of Maritime and Transportation, Ningbo University, Ningbo315211, China
Meng Li
Affiliation:
Integrated Transport Institute, Dalian Maritime University, Dalian116026, China.
*
*Corresponding author. E-mail: [email protected]
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Abstract

Ship domain is an important theory in ship collision avoidance and an effective collision detection method. First, several classical ship domain models are used in experiments. The results show that the alarm rate is too high in busy waters, leading to greatly reduced practicality of the model. Potential collision risk cannot be detected effectively, especially for a ship with restricted manoeuvrability, which is usually regarded as an overtaken ship due to its navigation characteristics. Therefore, it is necessary to fully consider the interference of other ships to ships with limited manoeuvrability in an encounter situation. A novel ship domain model for ships with restricted manoeuvrability in busy waters is proposed. Considering the navigation characteristics of a ship with restricted manoeuvrability and the influence of the ship–ship effect, an algorithm to determine the boundary of the ship domain model is given by force and moment equations. AIS trajectory data of the North Channel of the Yangtze River Estuary are used to perform a comparative experiment, and four classical ship domain models are employed to perform comparative experiments. The results show that the alarm rates of the novel ship domain model are 7⋅608%, 15⋅131%, 55⋅785% and 7⋅608% lower than those of the other four classical models, and this outcome can effectively reduce the high false alarm rate produced by other models in this environment.

Keywords

ship domainship collisionAISsafety
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 3 , May 2021 , pp. 673 - 697
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Copyright
Copyright © The Royal Institute of Navigation 2021

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References

Argüelles, R. P., Maza, J. A. and Martín, F. M. (2019). Specification and design of safety functions for the prevention of ship-to-ship collisions on the high seas. Journal of Navigation, 72(1), 53–68.10.1017/S0373463318000553CrossRefGoogle Scholar
Coldwell, T. G. (1983). Marine traffic behaviour in restricted waters. The Journal of Navigation, 36(3), 430–444.10.1017/S0373463300039783CrossRefGoogle Scholar
Curtis, R. G. (1980). The probability of close overtaking in fog. Journal of Navigation, 33(03), 329.10.1017/S0373463300040741CrossRefGoogle Scholar
Davis, P. V., Dove, M. J. and Stockel, C. T. (1980). A computer simulation of marine traffic using domains and arenas. Journal of Navigation, 33(02), 215.10.1017/S0373463300035220CrossRefGoogle Scholar
Dinh, G. H. and Im, N. K. (2016). The combination of analytical and statistical method to define polygonal ship domain and reflect human experiences in estimating dangerous area. International Journal of e-Navigation and Maritime Economy, 4, 97–108.10.1016/j.enavi.2016.06.009CrossRefGoogle Scholar
Fiskin, R., Nasiboglu, E. and Yardimci, M. O. (2020). A knowledge-based framework for two-dimensional (2D) asymmetrical polygonal ship domain. Ocean Engineering, 202, 107187.CrossRefGoogle Scholar
Fujii, Y. and Tanaka, K. (1971). Traffic capacity. Journal of Navigation, 24(04), 543.10.1017/S0373463300022384CrossRefGoogle Scholar
Goerlandt, F., Montewka, J., Zhang, W. and Kujala, P. (2016) An analysis of ship escort and convoy operations in ice conditions. Safety Science, 95, 198–209.10.1016/j.ssci.2016.01.004CrossRefGoogle Scholar
Goodwin, E. M. (1975). A statistical study of ship domains. The Journal of Navigation, 28(3), 17.10.1017/S0373463300041230CrossRefGoogle Scholar
Gourlay, T. (2009). Sinkage and trim of two ships passing each other on parallel courses. Ocean Engineering, 36(14), 1119–1127.10.1016/j.oceaneng.2009.06.003CrossRefGoogle Scholar
Hansen, M. G., Jensen, T. K., Lehn-SchiøLer, T., Melchild, K., Rasmussen, F. M. and Ennemark, F. (2013). Empirical ship domain based on AIS data. Journal of Navigation, 66(06), 931–940.CrossRefGoogle Scholar
Hörteborn, A., Ringsberg, J. W., Svanberg, M. and Holm, H. (2018). A revisit of the definition of the ship domain based on AIS analysis. Journal of Navigation, 1–18.Google Scholar
Im, N. and Luong, T. N. (2019). Potential risk ship domain as a danger criterion for realtime ship collision risk evaluation. Ocean Engineering, 194, 106610.10.1016/j.oceaneng.2019.106610CrossRefGoogle Scholar
Inoue, S., Hirano, M. and Kijima, K. (1981). Hydrodynamic derivatives on ship maneuvering. International Shipbuilding Progress, 28(321), 112–125.10.3233/ISP-1981-2832103CrossRefGoogle Scholar
Jingsong, Z., Zhaolin, W. and Fengchen, W. (1993). Comments on ship domains. Journal of Navigation, 46(03), 422.10.1017/S0373463300011875CrossRefGoogle Scholar
Katsoulis, P. S. (1975). Optimizing block coefficient by an exponential formula. Shipping World & Shipbuilder, 168, 217–219.Google Scholar
Lataire, E., Vantorre, M., Delefortrie, G. and Candries, M. (2012). Mathematical modelling of forces acting on ships during lightering operations. Ocean Engineering, 55, 101–115.10.1016/j.oceaneng.2012.07.029CrossRefGoogle Scholar
Nomoto, K., Taguchi, T., Honda, K. and Hirano, S. (1957). On the steering qualities of ships. International Shipbuilding Progress, 4(35), 354–370.10.3233/ISP-1957-43504CrossRefGoogle Scholar
Pietrzykowski, Z. (2008). Ship's fuzzy domain – a criterion for navigational safety in narrow fairways. Journal of Navigation, 61(3), 499–514.CrossRefGoogle Scholar
Pietrzykowski, Z. and Uriasz, J. (2009). The ship domain – a criterion of navigational safety assessment in an open sea area. Journal of Navigation, 62(01), 93.10.1017/S0373463308005018CrossRefGoogle Scholar
Van der Tak, C. and Spaans, J. A. (1977). A model for calculating a maritime risk criterion number. Journal of Navigation, 30(2), 287–295.CrossRefGoogle Scholar
Varyani, K. S. and Krishnankutty, P. (2006). Modification of ship hydrodynamic interaction forces and moment by underwater ship geometry. Ocean Engineering, 33(8), 1090–1104.CrossRefGoogle Scholar
Varyani, K. S., Thavalingam, A. and Krishnankutty, P. (2004). New generic mathematical model to predict hydrodynamic interaction effects for overtaking maneuvers in simulators. Journal of Marine Science & Technology, 9(1), 24–31.CrossRefGoogle Scholar
Wang, N. (2010). An intelligent spatial collision risk based on the quaternion ship domain. Journal of Navigation, 63(04), 733–749.CrossRefGoogle Scholar
Wang, N. (2013). A novel analytical framework for dynamic quaternion ship domains. Journal of Navigation, 66(02), 265–281.CrossRefGoogle Scholar
Wang, Y. and Chin, H. C. (2016). An empirically-calibrated ship domain as a safety criterion for navigation in confined waters. Journal of Navigation, 69(2), 257–276.CrossRefGoogle Scholar
Wang, X., Liu, Z. and Cai, Y. (2017). The ship maneuverability based collision avoidance dynamic support system in close-quarters situation. Ocean Engineering, 146, 486–497.10.1016/j.oceaneng.2017.08.034CrossRefGoogle Scholar
Xiaorui, H. and Changchuan, L. (2011). A preliminary study on targets association algorithm of radar and AIS using BP neural network. Procedia Engineering, 15, 1441–1445.10.1016/j.proeng.2011.08.267CrossRefGoogle Scholar
Yeung, R. W. (1978). On the interactions of slender ships in shallow water. Journal of Fluid Mechanics, 85(01), 143.10.1017/S0022112078000567CrossRefGoogle Scholar
Zhang, X. K. and Li, Y. K. (2009). Prediction of Ship Maneuverability Indices. Navigation of China, 32(1), 96–101.Google Scholar
Zhang, L. and Meng, Q. (2019). Probabilistic ship domain with applications to ship collision risk assessment. Ocean Engineering, 186, 106130.10.1016/j.oceaneng.2019.106130CrossRefGoogle Scholar
Zhang, W., Feng, X., Qi, Y., Shu, F. and Wang, Y. (2019). Towards a model of regional vessel near-miss collision risk assessment for open waters based on AIS data. Journal of Navigation, 72(6), 1–20.10.1017/S037346331900033XCrossRefGoogle Scholar
Zhao-Kun, W., Xin-Lian, X. and Wen-Bo, L. (2020). Self-adaption vessel traffic behaviour recognition algorithm based on multi-attribute trajectory characteristics. Ocean Engineering, 198, 106995.Google Scholar
Zhou, D. and Zheng, Z. (2018). Dynamic fuzzy ship domain considering the factors of own ship and other ships. Journal of Navigation, 72(2), 1–16.Google Scholar
Zhu, X., Xu, H. and Lin, J. (2001). Domain and its model based on neural networks. Journal of Navigation, 54(1), 97–103.CrossRefGoogle Scholar