  Animal-vehicle collisions (AVCs) are common around the world and result in
considerable loss of animal and human life, as well as significant property
damage and regular insurance claims. Understanding their occurrence in relation
to various contributing factors and being able to identify locations of high
risk are valuable to AVC prevention, yielding economic, social and
environmental cost savings. However, many challenges exist in the study of AVC
datasets. These include seasonality of animal activity, unknown exposure (i.e.,
the number of animal crossings), very low AVC counts across most sections of
extensive roadway networks, and computational burdens that come with discrete
response analysis using large datasets. To overcome these challenges, a
Bayesian hierarchical model is proposed where the exposure is modeled with
nonparametric Dirichlet process, and the number of segment-level AVCs is
assumed to follow a Binomial distribution. A P\'olya-Gamma augmented Gibbs
sampler is derived to estimate the proposed model. By using the AVC data of
multiple years across about 100,000 segments of state-controlled highways in
Texas, U.S., it is demonstrated that the model is scalable to large datasets,
with a preponderance of zeros and clear monthly seasonality in counts, while
identifying high-risk locations (for application of design treatments, like
separated animal crossings with fencing) and key explanatory factors based on
segment-specific factors (such as changes in speed limit) can be done within
the modelling framework, which provide useful information for policy-making
purposes.
