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A ship domain-based model of collision risk for near-miss detection and Collision Alert Systems

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  • Szlapczynski, Rafal
  • Szlapczynska, Joanna

Abstract

The paper presents a new model of ship collision risk, which utilises a ship domain concept and the related domain-based collision risk parameters. An encounter is here described by five variables representing: degree of domain violation (DDV), relative speed of the two vessels, combination of the vessels’ courses, arena violations and encounter complexity. As for the first three variables, their values can be directly computed based on positions, courses and speeds of two vessels. The last two variables require decomposing a close quarters situation into phases. For this purpose the method utilizes a number of auxiliary parameters derived from the concept of ship domain: time of domain violation (TDV), time of domain exit (TDE), timespan of close quarters situation and vessels’ proximity, which is quantified based on the ship arena. The formulas and algorithms for determining all variables’ values are provided in detail. Once all the values are computed, the final collision risk assessment is made. Possible applications of the presented model include: AIS-based near-miss detection, Collision Alert Systems (CAS) and collision avoidance decision support systems (DSS). Case studies for those applications are provided, including examples of encounter classification and quantification of collision risk.

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  • Szlapczynski, Rafal & Szlapczynska, Joanna, 2021. "A ship domain-based model of collision risk for near-miss detection and Collision Alert Systems," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021002945
    DOI: 10.1016/j.ress.2021.107766
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    References listed on IDEAS

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    6. Dinis, D. & Teixeira, A.P. & Guedes Soares, C., 2020. "Probabilistic approach for characterising the static risk of ships using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
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    Cited by:

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    2. Gao, Dawei & Zhu, Yongsheng & Yan, Ke & Soares, C. Guedes, 2024. "Deep learning–based framework for regional risk assessment in a multi–ship encounter situation based on the transformer network," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Zyczkowski, Marcin & Szlapczynski, Rafal, 2023. "Collision risk-informed weather routing for sailboats," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    4. Mauro, Francesco & Vassalos, Dracos & Paterson, Donald, 2022. "Critical damages identification in a multi-level damage stability assessment framework for passenger ships," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    5. Li, Mengxia & Mou, Junmin & Chen, Pengfei & Rong, Hao & Chen, Linying & van Gelder, P.H.A.J.M., 2022. "Towards real-time ship collision risk analysis: An improved R-TCR model considering target ship motion uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    6. Gao, Dawei & Zhu, Yongsheng & Guedes Soares, C., 2023. "Uncertainty modelling and dynamic risk assessment for long-sequence AIS trajectory based on multivariate Gaussian Process," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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