Selection of Streets from a Network Using Self-Organizing Maps

We propose a novel approach to selection of important streets from a network, based on the technique of a self-organizing map (SOM), an artificial neural network algorithm for data clustering and visualization. Using the SOM training process, the approach derives a set of neurons by considering multiple attributes including topological, geometric and semantic properties of streets. The set of neurons constitutes a SOM, with which each neuron corresponds to a set of streets with similar properties. Our approach creates an exploratory linkage between the SOM and a street network, thus providing a visual tool to cluster streets interactively. The approach is validated with a case study applied to the street network in Munich, Germany.