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We consider the feature detection problem in the presence of clutter in pointprocesses on linear networks. We extend the classification method developed inprevious studies to this more complex geometric context, where the classicalproperties of a point process change and data visualization are not intuitive.We use the K-th nearest neighbour volumes distribution in linear networks forthis approach. As a result, our method is suitable for analysing point patternsconsisting of features and clutter as two superimposed Poisson processes on thesame linear network. To illustrate the method, we present simulations andexamples of road traffic accidents that resulted in injuries or deaths in twocities in Colombia.

Feature detection in point processes on linear networks using nearest neighbour volumes