Stochastic smoothing of point processes for wildlife-vehicle collisions on road networks

Abstract

Wildlife-vehicle collisions on road networks represent a natural problem between human populations and the environment, that affects wildlife management and raise a risk to the life and safety of car drivers. We propose a statistically principled method for kernel smoothing of point pattern data on a linear network when the first-order intensity depends on covariates. In particular, we present a consistent kernel estimator for the first-order intensity function that uses a convenient relationship between the intensity and the density of events location over the network, which also exploits the theoretical relationship between the original point process on the network and its transformed process through the covariate. We derive the asymptotic bias and variance of the estimator, and adapt some data-driven bandwidth selectors to estimate the optimal bandwidth. The performance of the estimator is analysed through a simulation study under inhomogeneous scenarios. We present a real data analysis on wildlife-vehicle collisions in a region of North-East of Spain.

Modeling Road Traffic Safety Based on Point Patterns of Wildlife-Vehicle Collisions

“…Consequently, distinct road sections will have distinct probabilities of having a WVC in terms of the covariates surrounding this road area (vegetation density, for instance), the traffic volume of this road section and its speed limit. Borrajo et al (2021) model the point intensity of roadkills based on several landscape covariates. This suggests the possibility of considering weighted graphs to model the risk of WVC, and to define distinct path configurations to optimize this risk.…”