Pattern development for vessel accidents: a comparison of statistical and neural computing techniques

Abstract This paper describes a sample of over 900 vessel accidents that occurred on the lower Mississippi River. Two different techniques, one statistical and the other based on a neural network model, were used to build logical groups of accidents. The objective in building the groups was to maximize between-group variation and minimize within-group variation. The result was groups whose records were as homogenous as possible. A clustering algorithm (i.e., a non-inferential statistical technique) generated sets of three, four and five groups. A Kohenen neural network model (i.e., a self-organizing map) also generated sets of three, four and five groups. The two sets of parallel groups were radically different as to the relative number of records in each group. In other words, when the two sets of groups were constructed by the respective techniques, the membership of each comparable group within the two different sets was substantially different. Not only was the respective record count in each group substantially different, so were the descriptive statistics describing each comparable set of groups. These results have significant implications for marine policy makers. Important policy variables include safety factors such as weather, speed of current, time of operation, and location of accidents, but mandatory utilization of a voluntary vessel tracking service may be subject to debate.