Animal–vehicle collisions (AVCs) can result in devastating injuries to both
humans and animals. Despite significant advances in crash prediction models,
there is still a significant gap when it comes to injury severity prediction
models in AVCs, especially concerning small animals. It is no secret that large
mammals can pose a significant threat to road safety; however, researchers tend
to overlook the impact of domestic and small animals wandering along the roads.
In this study, STATS19 road safety data was used containing any type of live
animal, and a radial basis function (RBF) model was used to predict different
severities of injury regardless of whether the animal was hit, or not. As a
means of better understanding the factors contributing to severities, regression
trees were used to identify and retain only the most useful predictors, removing
the less useful ones. A comparison was made between the performance of the trees
across a range of severity classes, and the model-fitting results were
discussed. Initially, the study was unable to generate satisfactory predictions,
but the optimization of the key predictors and the combination of severity
classes significantly improved their accuracy. Research findings revealed
factors contributing to the severities, which were discussed accordingly.
Particular attention was drawn to the pressing safety issue posed by animals
crossing A-class single carriageways in rural site clusters. Animals being
present on those carriageways without direct vehicular contact significantly
contributed to the severity of the injuries sustained. Although the majority of
contributing factors were related to human behavior, no evidence of road safety
education, training, or publicity interventions specifically targeting AVCs was
found in the literature.
