Multi-ship collision risk assessment based on DBSCAN algorithm

Xusheng Wang; Longhui Gang; Liujiang Zheng; Tong Liu; Sen Qiao

doi:10.1117/12.3054628

20 December 2024 Multi-ship collision risk assessment based on DBSCAN algorithm

Xusheng Wang, Longhui Gang, Liujiang Zheng, Tong Liu, Sen Qiao

Author Affiliations +

Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 134211W (2024) https://doi.org/10.1117/12.3054628
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

The evaluation of collision risks in maritime traffic is essential for guiding strategic evasive actions and decreasing the incidence of collision-related events. In this scholarly work, we introduce a maritime collision risk assessment model that employs the DBSCAN clustering methodology. This model is capable of assessing the potential for collisions in situations where numerous vessels encounter. In this model, the DBSCAN algorithm is used to obtain the collision cluster of ships, and a quantitative index based on collision avoidance rules and ship domain - danger sector is used to represent the collision risk of each ship in the case of multi-ship encounters. AIS data was applied to validate the proposed risk assessment method, and the findings demonstrated that the proposed model is proficient in discerning the likelihood of multi-vessel collisions, thereby offering a foundational theoretical framework for the surveillance of navigational safety in intricate maritime environments.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xusheng Wang, Longhui Gang, Liujiang Zheng, Tong Liu, and Sen Qiao "Multi-ship collision risk assessment based on DBSCAN algorithm", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 134211W (20 December 2024); https://doi.org/10.1117/12.3054628

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE