Abstract
Ship handling simulator is a virtual ship navigating system with three dimensional screen system and simulation programs. FTS simulation can produce theoretically infinite experiment tests without time constraint, but which results in collecting determinstic observations. RTS simulation can collect statistical observations but has disadvantage of spending at least 30 minutes for a single experiment. The previous studies suggested that the number of experiment conditions to be tested could be reduced to obtain random data with RTS simulation by focusing on highly difficult experiment condition for ship handling. It has the limitation of not estimating the distribution of ship handling difficulty for the route. In this paper, similarity and clustering analysis are suggested for reduction methodology of experiment conditions. Similarity of experiment conditions are measured as follows: euclidean distance of ship handling difficulty index and correlation matrix of distance differences from the designed route. Clustering analysis and multi-dimensional scaling are applied to classify experiment conditions with measured similarity into reducing the number of RTS simulation conditions. An empirical result on Dangin harbor is shown and discussed.
