Prediction of Vehicle-Live Animal Crashes in Britain: Contact and Noncontact Incidents

Abstract

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.