A novel method for the evaluation of ship berthing risk using AIS data

The assessment of ship berthing risk, encompassing the potential for berthing collisions and unforeseen events, holds paramount importance in the realm of waterway traffic management and maritime surveillance. However, existing methods for analyzing ship berthing risk suffer from limitations in terms of timeliness, comprehensiveness and data accessibility. Therefore, this paper presents a novel approach to ship berthing risk assessment. The proposed method relies on Automatic Identification System (AIS) data and takes into consideration information related to the ship, berth, and environmental factors. It calculates crucial parameters, including the vertical distance between the ship and the berth, berthing speed, berthing angle and real-time distance between the ship and the berth, utilizing the AIS data and the berth location. Furthermore, environmental disturbance data pertaining to the ship's berthing environment is integrated with AIS data. Subsequently, the authors introduce the Improved Bossel Model considering Catastrophe (IBM-CC) to evaluate ship berthing risk in real-time. Finally, the proposed method was validated using actual ship berthing data and various simulation scenarios. The results demonstrate that the authors' proposed method accurately assesses real-time ship berthing risk under diverse scenarios, offering a novel approach for the real-time and precise quantitative assessment of ship berthing risk.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2023.116595
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801823029797
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2023 Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Lin, Bowen
  • Zheng, Mao
  • Chu, Xiumin
  • Zhang, Mingyang
  • Mao, Wengang
  • Wu, Da
  • 0000-0002-4898-5203
• Publication Date: 2024-2-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 116595
• Serial:
  • Ocean Engineering
  • Volume: 293
  • 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: Automatic vehicle detection and identification systems; Data analysis; Docking; Risk assessment; Ships
• Subject Areas: Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01904844
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 18 2024 11:37AM