  The identification of accident hot spots is a central task of road safety
management. Bayesian count data models have emerged as the workhorse method for
producing probabilistic rankings of hazardous sites in road networks.
Typically, these methods assume simple linear link function specifications,
which, however, limit the predictive power of a model. Furthermore, extensive
specification searches are precluded by complex model structures arising from
the need to account for unobserved heterogeneity and spatial correlations.
Modern machine learning (ML) methods offer ways to automate the specification
of the link function. However, these methods do not capture estimation
uncertainty, and it is also difficult to incorporate spatial correlations. In
light of these gaps in the literature, this paper proposes a new spatial
negative binomial model, which uses Bayesian additive regression trees to
endogenously select the specification of the link function. Posterior inference
in the proposed model is made feasible with the help of the Polya-Gamma data
augmentation technique. We test the performance of this new model on a crash
count data set from a metropolitan highway network. The empirical results show
that the proposed model performs at least as well as a baseline spatial count
data model with random parameters in terms of goodness of fit and site ranking
ability.
