Ship Collision Risk Assessment based on Collision Detection Algorithm

Ningbo Zhoushan port handled 1.08 billion tons cargoes in 2018 which is considered as the one of biggest ports in the world. There are more than 1 000 ships enter or depart the port per day. Therefore, it is of importance to assess the collision risk for ships passing through the harbor area. In thi...

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Bibliographic Details
Published in:IEEE access Vol. 8; p. 1
Main Authors: Liu, Dongdong, Shi, Guoyou
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Anomalies
Artificial intelligence
collision detection
Context modeling
Data models
Harbors
Marine vehicles
Navigation
Ports
Risk assessment
Risk management
Safety
ship domain
Ship domains
Ships
Waterway transportation
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Ningbo Zhoushan port handled 1.08 billion tons cargoes in 2018 which is considered as the one of biggest ports in the world. There are more than 1 000 ships enter or depart the port per day. Therefore, it is of importance to assess the collision risk for ships passing through the harbor area. In this paper, a novel approach is initially proposed to assess ship collision risk in the harbor area based on collision detection technology of ship domain using automatic identified system (AIS) data. This study aims to build a unified framework of collision risk assessment which does not need to build different models in accordance with the ship domain we selected. To clean the historical motion data of ships, a method for anomaly detection of ship static information based on autoencoder (AE) is proposed. Based on the above proposed method, the ship collision frequency can be estimated, besides, the risk area can also be determined. The results obtained from the method could provide a reference on furthering enhance the navigational safety for the Maritime and Port Authority.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3013957