A novel framework for regional collision risk identification based on AIS data. (August 2019)

• Record Type: Journal Article
• Authors: Liu, Zihao; Wu, Zhaolin; Zheng, Zhongyi
• Is Part Of: Applied ocean research. Volume 89(2019)
• Page Start: 261
• Page End: 272
• Publication Date: 2019-08
• Subjects: ARPA Automatic Radar Plotting Aids -- AIS Automatic Identification System -- ECDIS Electronic Chart Display and Information System -- DCPA Distance at Closest Point of Approach -- TCPA Time to Closest Point of Approach -- CPA Closest Point of Approach -- SDOI ship domain overlapping index -- COLREGs Convention on the International Regulations for Preventing Collisions at Sea -- DBSCAN Density-Based Spatial Clustering of Applications with Noise -- RCR regional collision risk -- CCR cluster collision risk -- MMSI Maritime Mobile Service Identify -- IWRAP International Association of Lighthouse Authorities Waterways Risk Assessment Program -- IALA The International Association of Marine Aids to Navigation and Lighthouse Authorities -- IMO International Maritime Organization; Regional collision risk -- AIS data -- Clustering -- Shapley value -- Navigational Safety.; Ocean engineering -- Periodicals; 620.416205
• DOI: 10.1016/j.apor.2019.05.020
• Languages: English
• ISSNs: 0141-1187

Abstract

Highlights: A novel framework of regional collision risk identification is proposed. SDOI is proposed to represent the collision risk of relative bearing and distance. DCPA, TCPA and SDOI are used to represent collision risk between vessels. DBSCAN is used to simplify the computing process of the proposed framework. An improved Shapley value method is used to estimate contributions. Abstract: Identification of regional collision risk in water area is of significance for the safety of navigation. However, traditional risk identification models are subject to the limitations in accuracy, short-term identification and traffic characteristics. Herein, a framework was put forward to identify regional collision risk instantaneously based on AIS data. The vessels were clustered by using the spatial clustering method. Afterwards, the framework was divided into two steps. Firstly, collision risk of each cluster was obtained by collision risk and contribution of the vessels within the cluster. An analytical method was adopted to identify collision risk of each vessel from the perspective of vessel pairs. Contribution of each vessel was determined by using improved Shapley value method in game theory. Secondly, regional collision risk was obtained by collision risk and contribution of each cluster. Case studies were carried out based on the AIS data of Northern Yellow Sea in China to validate the validity of the proposed framework. The results show that the proposed framework can effectively identify collision risk in water area, presenting the potential for collision risk monitoring and collision risk analysis of water area.