V článku je představena metoda pro identifikaci a hodnocení klastrů dopravních nehod podle jejich významnosti. Pomocí rozšířené kernelové hustoty s testováním statistické významnosti lze odhalit nejrizikovější místa.
Abstract:
This paper proposes a procedure which evaluates clusters of traffic accident and organizes them according to their significance. The standard kernel density estimation was extended by statistical significance testing of the resulting clusters of the traffic accidents. This allowed us to identify the most important clusters within each section. They represent places where the kernel density function exceeds the significance level corresponding to the 95th percentile level, which is estimated using the Monte Carlo simulations. To show only the most important clusters within a set of sections, we introduced the cluster strength and cluster stability evaluation procedures. The method was applied in the Southern Moravia Region of the Czech Republic.
http://www.sciencedirect.com/science/article/pii/S0001457513000912