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A probabilistic risk approach for the collision detection of multi-ships under spatiotemporal movement uncertainty

Xin, X, Liu, K, Yang, Z, Zhang, J and Wu, X (2021) A probabilistic risk approach for the collision detection of multi-ships under spatiotemporal movement uncertainty. Reliability Engineering and System Safety, 215. ISSN 0951-8320

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Abstract

It is vital to analyse ship collision risk for preventing collisions and improving safety at sea. The state-of-the-art of ship collision risk analysis focuses on encountering conflict between ship pairs, subject to a strong assumption of the ships having no/little spatiotemporal motion uncertainty. This paper proposes a probabilistic conflict detection approach to estimate potential collision risk of various multi-vessel encounters, in which the spatiotemporal-dependent patterns of ship motions are newly taken into account through quantifying the trajectory uncertainty distributions using AIS data. The estimation accuracy and efficiency are assured by employing a two-stage Monte Carlo simulation algorithm, which provides the quantitative bounds on the approximation accuracy and allows for a fast estimation of conflict criticality. Several real experiments are conducted using the AIS-based trajectory data in Ningbo-Zhoushan Port to demonstrate the feasibility and superiority of the proposed new approach. The results show that it enables the effective detection of collision risk timely and reliably in a complicated dynamic situation. They therefore provide valuable insights on ship collision risk prediction as well as the formulation of risk mitigation measures.

Item Type: Article
Uncontrolled Keywords: 01 Mathematical Sciences, 09 Engineering, 15 Commerce, Management, Tourism and Services
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
H Social Sciences > HE Transportation and Communications
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Engineering
Publisher: Elsevier
Related URLs:
Date Deposited: 13 Jan 2022 09:47
Last Modified: 13 Jan 2022 10:00
DOI or Identification number: 10.1016/j.ress.2021.107772
URI: https://researchonline.ljmu.ac.uk/id/eprint/16046

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