When an officer of the watch (OOW) faces complicated marine traffic, a suitable decision support tool could be employed in support of collision avoidance decisions, to reduce the burden and greatly improve the safety of marine traffic. Decisions on routes to avoid collisions could also consider economy as well as safety. Through simulating the biological evolution model, this research adopts the genetic algorithm used in artificial intelligence to find a theoretically safety-critical recommendation for the shortest route of collision avoidance from an economic viewpoint, combining the international regulations for preventing collisions at sea (COLREGS) and the safety domain of a ship. Based on this recommendation, an optimal safe avoidance turning angle, navigation restoration time and navigational restoration angle will also be provided. A Geographic Information System (GIS) will be used as the platform for display and operation. In order to achieve advance notice of alerts and due preparation for collision avoidance, a Vessel Traffic Services (VTS) operator and the OOW can use this system as a reference to assess collision avoidance at present location.
K E Y W O R D S1. genetic algorithm.2. collision avoidance. 3. decision support system. 4. GIS.
I N T R O D U C T I O N.With the continued development of the shipping industry, ships have grown larger, become more specialized and capable of operating at faster speeds. The marine traffic environment has become more complicated and the density of shipping traffic has become greater. The navigable areas in channels and ports have become relatively narrow, so that the navigation problems are more challenging and collisions or stranding accidents are increasing in frequency, even though auxiliary ship collision avoidance equipment is widely used at present. These accidents not only cause major human injury and huge property loss, but also constitute a serious threat to the marine environment. In an investigation into reasons for collision accidents it was found that over 80% are caused by human factors (Li et al., 2006). There are two ways to solve the problem of these human factors : Firstly, to strengthen the technical training and management of crews, to improve the quality
The widespread use of the Automatic Identification System (AIS) has had a significant impact on maritime technology. AIS enables the Vessel Traffic Service (VTS) not only to offer commonly known functions such as identification, tracking and monitoring of vessels, but also to provide rich real-time information that is useful for marine traffic investigation, statistical analysis and theoretical research. However, due to the rapid accumulation of AIS observation data, the VTS platform is often unable quickly and effectively to absorb and analyze it. Traditional observation and analysis methods are becoming less suitable for the modern AIS generation of VTS. In view of this, we applied the same data mining technique used for business intelligence discovery (in Customer Relation Management (CRM) business marketing) to the analysis of AIS observation data. This recasts the marine traffic problem as a business-marketing problem and integrates technologies such as Geographic Information Systems (GIS), database management systems, data warehousing and data mining to facilitate the discovery of hidden and valuable information in a huge amount of observation data. Consequently, this provides the marine traffic managers with a useful strategic planning resource.
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