A novel multi-ship collision probability estimation method considering data-driven quantification of trajectory uncertainty

The collision risk prediction is crucial for the safety management of maritime transportation. Previous studies have primarily focused on the near-miss collision risk of two ships, yet the risk due to congestion caused by multiple ships is complex and also very challenging for safe management. In this paper, a novel multi-ship collision probability assessment method is proposed. To consider the uncertainty of ship motion, a deep learning multi-model integration method is proposed to predict the ship motion trajectory to quantify the time-varying stochasticity of ship motion. Then, a method for calculating collision probability is proposed based on Monte Carlo simulation by integrating importance sampling techniques with the cross-entropy method. The model is combined with the ship trajectory estimator to quantify the collision probability. To prove the validity of the developed model, a case study based on Victoria Harbour is illustrated. The results show that this method can provide early warning of multi-ship collision risk. The method can offer an important basis for maritime collision risk monitoring and ship navigation risk assessment for maritime administration departments and shore-based center of maritime autonomous ships.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2023.113825
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801823002093
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wang, Miaomiao
  • 0000-0002-3704-9858
  • Wang, Yanfu
  • Cui, Erhua
  • Fu, Xiuju
  • 0000-0002-6673-1098
• Publication Date: 2023-3-15

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 113825
• Serial:
  • Ocean Engineering
  • Volume: 272
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Crash risk forecasting; Data analysis; Ships; Uncertainty; Vehicle trajectories; Water transportation crashes
• Subject Areas: Data and Information Technology; Marine Transportation; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01873757
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 22 2023 9:53AM