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.

翻译：我们考虑了在杂波存在下线性网络点过程的特征检测问题。我们将先前研究中发展的分类方法扩展至这一更为复杂的几何背景，其中点过程的经典性质发生变化且数据可视化不具备直观性。为此，我们采用线性网络中第K近邻体积分布进行方法构建。研究结果表明，该方法适用于分析由特征与杂波作为两个叠加泊松过程构成的线性网络点模式。为阐明该方法，我们展示了模拟实验，并以哥伦比亚两个城市中导致伤亡的道路交通事故数据为例进行实证分析。