Regional Multi-ship Collision Risk Analysis Based on Velocity Obstacle Method: a Case Study on the Pearl River Estuary
Analysis of regional multi-ship collision risk is essential for enhancing the efficiency of traffic management in maritime transportation. However, traditional collision risk analysis methods only assess the risk of collision from the viewpoint of ship pair encounters. In this research, a novel framework for analyzing regional multi-ship collision risk based on Velocity Obstacle (VO) method is proposed using the AIS (Automatic Identification System) data. Firstly, the ships in specific sea areas are clustered with the density-based spatial clustering of applications with Noise to identify multi-ship encounter situations. Afterward, a new collision risk indicator utilizing VO-based time-varying collision risk measurement method is proposed to calculate the collision risk of the single ship. Secondly, the macro-regional collision risk is quantified by calculating the contribution of each ship and each cluster with the Shapley value in cooperative games. Finally, to verify the effectiveness of the proposed framework, we carried out a case study of the Pearl River Estuary in China using historical AIS data. The results show that the proposed framework for regional multi-ship collision risk analysis can help maritime surveillance operators identify the ships with high risk and gain a better understanding of regional collision risk from both microcosmic and macroscopic perspectives.
Authors
- Linying Chen (ORCID: https://orcid.org/0000-0002-0267-4354)
- Junmin Mou (ORCID: https://orcid.org/0000-0003-1955-4604)
- Pengfei Chen (ORCID: https://orcid.org/0009-0001-1998-6186)
- Qi Liu (ORCID: https://orcid.org/0000-0001-6956-5550)
Institutions
- Wuhan University of Technology (CN)
Publication Details
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1109/icist63249.2024.10805290
- Citations
- 1
- Primary Topic
- Maritime Navigation and Safety
- Type
- article
- Field-Weighted Citation Impact
- 0.34
Funders
- National Natural Science Foundation of China