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

  • Bowen Lin
  • , Mao Zheng*
  • , Xiumin Chu
  • , Mingyang Zhang
  • , Wengang Mao
  • , Da Wu
  • *Corresponding author for this work
  • Wuhan University of Technology
  • Chalmers University of Technology

Research output: Contribution to journalArticleScientificpeer-review

22 Citations (Scopus)
6 Downloads (Pure)

Abstract

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, we 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 our 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.

Original languageEnglish
Article number116595
Number of pages12
JournalOcean Engineering
Volume293
DOIs
Publication statusPublished - 1 Feb 2024
MoE publication typeA1 Journal article-refereed

Funding

The authors acknowledge the financial support from National Key R&D Program of China ( 2022YFB4301405 ), the National Natural Science Foundation of China ( 52001243 ), the National Natural Science Foundation of China ( 52001240 ), and the Guangxi Science and Technology Program ( AB23026132 ).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • AIS data
  • Bossel model
  • Catastrophe theory
  • Safely berthing
  • Ship berthing risk
  • Ship traffic safety

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