Crash data observed on a road network often exhibit spatial correlation due to unobserved effects with inherent spatial correlation following the structure of the road network. We introduce a network process convolution (NPC) model, wherein the spatial correlation among crash data is captured through a Gaussian Process (GP) approximated through a kernel convolution approach. The GP’s covariance function is based on path distance computed between a limited set of knots and crash data points on the road network. The proposed model offers a straightforward approach for predicting crash frequency at unobserved locations where covariates are available, and for interpolating the GP values anywhere on the network. Inference procedure is performed following the Bayesian paradigm and is implemented in R-INLA, which offers an estimation procedure that is more efficient than a Markov Chain Monte Carlo approach. We fitted our model to synthetic data and to crash data from Ottawa, Canada.