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  5. A Novel Method for the Evaluation of Ship Berthing Risk Using Ais Data

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Preprint
English
2023

A Novel Method for the Evaluation of Ship Berthing Risk Using Ais Data

0 Datasets

0 Files

English
2023
DOI: 10.2139/ssrn.4620503

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Mao Zheng
Mao Zheng

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Bowen Lin
Mao Zheng
Xiumin Chu
+3 more

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 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.

How to cite this publication

Bowen Lin, Mao Zheng, Xiumin Chu, Mingyang Zhang, Wengang Mao, Da Wu (2023). A Novel Method for the Evaluation of Ship Berthing Risk Using Ais Data. , DOI: 10.2139/ssrn.4620503.

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Publication Details

Type

Preprint

Year

2023

Authors

6

Datasets

0

Total Files

0

Language

English

DOI

10.2139/ssrn.4620503

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