Abstract
The automatic identification of multiship encounter is a vital criterion for ship collision avoidance and intelligent maritime safety surveillance. However, the parameters of ship encounter identification in the existing studies are fixed, and the methods are weak to give an automatic and visual performance in the multiship encounter identification. In order to fix the existed gap, this paper proposed a novel adaptive visual analytics framework for automatic multiship encounter identification based on density-based spatial clustering of applications with noise (DBSCAN) and visual analytics by adjusting the parameters of ship encounter adaptively. The DBSCAN clustering method was applied to detect the clusters of encounter ships and filter out the nonencounter ship, and the distribution and density of the encounter ship had been visualized on the nautical chart to give a better perception of ships’ behavior with a potentially high navigational risk. The framework had been designed and developed using DBSCAN and visual analytics, and the effectiveness was evaluated and validated by adjusting different parameters of multiship encounter within the Southwest waters of Zhoushan Island, China. The results showed that the proposed framework had a good performance in the visual identification of multiship encounter within confined waters, which could assist the ship collision avoidance and intelligent maritime surveillance system.
Highlights
Maritime transport constitutes the arteries of global trade; more than 90% of the world trade volume is transported by sea [1]. e maritime transportation plays a significant role in the development of international society; it is urgent to ensure the maritime safety and environment protection [2,3,4,5]. e huge volume of ships challenges the safety and security within the confined and coastal waters [6]. ere are still ship-ship collisions or grounding accidents occurring inevitably every year, and the collision accidents bring loss of life, property, and pollution to the marine environment [7]
In order to decrease the probability of accidents, optimize the traffic flow, and coordinate ships’ berthing sequence and protect the environment, some maritime traffic aids and surveillance systems have been installed. e surveillance systems are usually composed of radar, automatic identification system (AIS), port security system (PSS), vessel traffic service system (VTS), and video surveillance [8, 9]
E multiship encounter identification is a key problem that faces great difficulty and challenge; the main innovative contribution of this paper is that a novel adaptive visual analytics framework of the multiship encounter identification has been proposed, and the newly developed framework can contribute to the intelligent maritime traffic surveillance under number of ships sailing within a port in the visual and automatic way
Summary
Maritime transport constitutes the arteries of global trade; more than 90% of the world trade volume is transported by sea [1]. e maritime transportation plays a significant role in the development of international society; it is urgent to ensure the maritime safety and environment protection [2,3,4,5]. e huge volume of ships challenges the safety and security within the confined and coastal waters [6]. ere are still ship-ship collisions or grounding accidents occurring inevitably every year, and the collision accidents bring loss of life, property, and pollution to the marine environment [7]. E multiship encounter identification is a key problem that faces great difficulty and challenge; the main innovative contribution of this paper is that a novel adaptive visual analytics framework of the multiship encounter identification has been proposed, and the newly developed framework can contribute to the intelligent maritime traffic surveillance under number of ships sailing within a port in the visual and automatic way. Different from the previous work, this paper designed a novel framework that can adjust the ship encounter radius and the minimum number of sailing ships by introducing visual analytics and get flexible identification results of multiship encounter. E proposed framework is composed of AIS data processing and encounter ship detection by DBSCAN and adaptive visual analytics model for clusters of encounter ships under different specifications, which could make full use of the excellent performance of information processing by computer and adaptive visualization and perception from operators by adjusting the encounter radius and minimum number of ships in an adaptive way.
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