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Neural Network Models for Assessing Road Suitability for Dangerous Goods Transport

机译：评估道路危险货物运输适宜性的神经网络模型

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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.
机译：本文介绍了一种方法，用于评估使一段较短的道路适合危险品运输（DGT）所需的补救措施的程度。该方法基于对一组与DGT风险有关的变量的评估。由于涉及大量变量，因此有必要采用基于专家标准的监督方法。结果是一个知识库，可用于估计新的道路巷道的DGT风险，并且无需依赖专家即可确定风险来源。测试了许多用于模型构建的多元统计分析技术，即线性判别分析，其尺寸预先减小，多层感知器和支持向量机。从测试样本获得的结果表明，支持向量机最可靠地代表了专家知识。研究过的一段巷道的风险指数的图形表示可得出该巷道的DGT风险水平的地图。

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