Currently Vessel Traffic Service (VTS) does not have enough technical capability to monitor a crowded surveillance area to maintain safety. Without an efficient alerting system, many marine accidents have occurred due to operator oversight. In this article, a new fuzzy logic method is proposed to add vessel collision avoidance capability to VTS/AIS systems for all potential collision ships in the surveillance area. Starting from the VTS standpoint and integrating AIS data into the Marine Geographic Information System (MGIS) as a platform, the calculations of ship domain and ship inertial force are utilized to generate models of a guarding ring and danger index. By this means, a precise prediction of collision time and position can be achieved using a marine GIS spatial analyst module. The proposed method is able to enhance the VTS operator's decision-making abilities by providing a collision avoidance alerting system.

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

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Maritime transport is a major mode of transportation. Over 80% of international freight is carried by this mode. A port is a hub of ships and freight in maritime transport. Because of growing environmental concerns, how to effectively monitor, control, and improve ship emissions in a port has become a challenge for port administrations. This study combines automatic identification systems (AIS), ship emission estimation model (SEEM), geographic information system (GIS) mapping, and a scenario simulation technique to create a ship emission scenario simulation model (SESSM) for mapping and assessing current ship emissions alongside various “what-if” improvement options in a port area. A case study of the Port of Keelung in Taiwan is used to illustrate and verify the proposed model. In this case, the distribution and density of ship carbon emissions are mapped, with the ship berthing status being identified as the primary source of ship emissions. Meanwhile, nine “what-if” scenarios based on various combinations of speed policies and shore power supplies are simulated and analyzed. The results show that the proposed scenario simulation model is an effective tool to assess various “what-if” emission improvement options and to identify key factors for emission reduction. The effect of shore power supply on carbon emission reduction is significantly greater than speed policies. If investment costs are an issue, a balanced emission improvement option is suggested by combining a new speed policy and 50% shore power supply.