A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance. (15th November 2017)

• Record Type: Journal Article
• Authors: Zhen, Rong; Riveiro, Maria; Jin, Yongxing
• Is Part Of: Ocean engineering. Volume 145(2017)
• Page Start: 492
• Page End: 501
• Publication Date: 2017-11-15
• Subjects: Maritime transportation -- Vessel traffic -- AIS -- Collision risk index -- Maritime surveillance; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2017.09.015
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Multi-vessel collision risk assessment for maritime traffic surveillance is a key technique to ensure the safety and security of maritime traffic and transportation. This paper proposes a framework of real-time multi-vessel collision assessment that combines a spatial clustering process (DBSCAN) for detecting clusters of encounter vessels and a multi-vessel collision risk index model for encounter vessels within each cluster from the large amounts of monitored vessels in a surveyed sea area. First, the vessels monitored are clustered using DBSCAN to obtain the clusters of encounter vessels, filtering out the relatively safe vessels. Then, the dynamic motion relation between encounter vessels within each cluster is modeled to obtain DCPA and TCPA. The semantic and mathematical relationship of vessel collision risk index for each cluster of encounter vessels with DCPA and TCAP is constructed using a negative exponential function. To illustrate the effectiveness of the framework proposed, an experimental case study has been carried out within the west coastal waters of Sweden. The results show that our framework is effective and efficient at detecting and ranking collision risk indexes between encounter vessels within each cluster, which allows an automatic risk prioritization of encounter vessels for further investigation by operators. Hence, this framework can improve the safety and security of vessel traffic transportation and reduce the loss of lives and property. Highlights: This paper proposes a framework for real-time multi-vessel collision assessment for maritime traffic surveillance. The framework combines DBSCAN for detecting clusters of encounter vessels and a multi-vessel collision risk index model. The vessel collision risk index for each cluster of encounter vessels has been developed using a negative exponential function correlating DCPA and TCAP. An experimental case study has been carried out within the west coastal waters of Sweden to illustrate the validity of the framework. Our framework is effective and efficient at detecting and ranking collision risk indexes between encounter vessels within each clusterfor further investigation by operators.