Spatial Analysis and Prediction of Animal Vehicle Collisions (AVCs) in Iowa Using Environmental and Anthropogenic Factors

Animal–vehicle collisions (AVCs) present serious safety, economic, and ecological concerns, particularly in areas where wildlife habitats intersect with expanding road networks. While previous studies have examined land cover or traffic volume in relation to AVCs, many relied on coarse spatial scales, treated species as a single group, and omitted key roadway or operational variables. This study addresses these gaps by investigating the spatial determinants of terrestrial AVCs across Iowa using an integrated ecological and infrastructural modeling framework. Crash data from 2020 to 2024 were analyzed using a natural language processing (NLP) model to extract animal-related incidents and infer species types from narrative reports. Land cover, road characteristics (e.g., classification, AADT, speed limits), operational measures (e.g., incident clearance time), and population data were aggregated to Iowa’s 896 census tracts (2020 boundaries). A Negative Binomial (NB) regression model identified forest coverage, population density, speed limits, and road network complexity (e.g., length of Interstates and arterials) as significant predictors of higher AVC risk, while urban land cover was negatively associated. Stepwise AIC selection and 10-fold cross-validation enhanced model performance. Spatial clustering in residuals (local Moran’s I = 0.42, p < 0.01) led to a Spatial Error Model (SEM), which further improved fit and reduced spatial bias. This tract-level framework offers a scalable, data-driven approach for transportation and wildlife agencies to identify high-risk areas and implement localized mitigation strategies, such as fencing, signage, or speed control, tailored to both ecological and infrastructural conditions.