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