4.7 Article

A comprehensive spatiotemporal metric for ship collision risk assessment

Journal

OCEAN ENGINEERING
Volume 265, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.oceaneng.2022.112446

Keywords

Collision probability; Collision risk assessment; Spatiotemporal trajectory; Spatiotemporal ship domain; SVM

Funding

  1. National Natural Science Foundation of China
  2. Fundamental Research Funds for the Central Universities
  3. [52071047]
  4. [3132021102]

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This paper investigates ship collision risk assessment from a new perspective and proposes a comprehensive and flexible assessment algorithm by developing a spatiotemporal ship domain model and introducing a spatiotemporal metric. The effectiveness of the algorithm is validated through simulation studies.
Collision risk assessment is an effective way to determine the potential collision and reserve time for the action taken to avoid the possible collision. This paper investigates the ship collision risk assessment problem from a new perspective that considers both the spatial and temporal factors related to the ship's navigational safety. A spatiotemporal ship domain (SD) model is developed to take both factors into account so that any traditional 2 -dimensional SD model can be generalized into a 3-dimensional spatiotemporal SD model. Based on the new SD model, a comprehensive spatiotemporal metric is introduced to predict the ongoing ship collision risk, which considers not only the collision probability but also the assumed consequence. Moreover, the new assessment algorithm is continuous, and the quantitative collision risk can be obtained. Compared to the previous SD methods, the advantage of the new algorithm is that it can provide a more comprehensive and flexible risk assessment when facing different encounter situations. The effectiveness of the new risk assessment algorithm is examined by simulation studies which assesses the collision risk for a real ship collision accident and show advantages in comparison with the previous methods.

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