Classifying Highways: Hierarchical Grouping versus Kohonen Neural Networks

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Abstract
Neural networks may be useful alternatives for statistical classification techniques when the available data is incomplete. This paper discusses the results obtained from a statistical technique called hierarchical grouping and the Kohonen neural network for classifying traffic patterns. The Kohonen neural network is shown to be a reasonable approximation of the hierarchical-grouping technique. It is suggested that hierarchical grouping of a small sample of typical traffic patterns may be a useful first step in setting up a Kohonen neural network for traffic-pattern classification. The Kohonen neural network can be used for classifying a large number of complete as well as incomplete traffic patterns as they become available. The neural network can also adapt the classification process to the change in the typical traffic patterns over time.

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