Risk Communications: Around the World Neural Network Models for Assessing Road Suitability for Dangerous Goods Transport

This article describes a methodology for assessing the degree of remedial action required to make short stretches of a roadway suitable for dangerous goods transport (DGT). The methodology is based on the evaluation of a set of variables that have a bearing on DGT risk. The large number of variables involved made it necessary to apply a supervised approach based on expert criteria. The result was a knowledge base that can be used both to estimate DGT risk for new stretches of roadway and to determine sources of risk without having to rely on an expert. A number of multivariate statistical analysis techniques were tested for the construction of the model, namely linear discriminant analysis with a prior reduction in dimensionality, multilayer perceptrons, and support vector machines. The results obtained from a test sample show that the support vector machines represented expert knowledge most reliably. A graphic representation of the risk index for a studied stretch of roadway results in a map of the level of DGT risk for that roadway.