A Neural Network for Transportation Safety Modeling

Abstract Accidents serve as an operational measure of marine safety, and specifically the safety of vessels, crews, and cargoes. The ability to accurately predict the type of vessel accident with such input variables as time, location, weather, river stage, and traffic could significantly reduce marine casualties by alerting port authorities and navigation groups as to the likelihood of a specific kind of casualty. In this paper, three models were developed to predict vessel accidents on the lower Mississippi River. These models are a neural network, multiple discriminant analysis and logistic regression. The predictive capability for vessel accidents of a neural network is compared with multiple discriminant analysis and logistic regression. The percent of “grouped” cases correctly classified is 80% (36 of the 45 cases in the testing set) for the neural network, if nonclassified cases are treated as incorrectly classified by neural network. The percent of “grouped” cases correctly classified by this network is 90% (36 of 40 cases) if nonclassified cases are excluded from the calculation. Discriminant analysis and logistic regression were able to correctly classify only 53% and 56% respectively, of accident cases into three casualty groups: collisions, rammings, or groundings.