Predictive modeling of ungulate–vehicle collision in the Republic of Korea

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Context Studies of ungulate–vehicle collision (UVC) may suffer from inadequate or scattered datasets, due to difficulties in acquiring data over vast temporal and spatial scales. Predictive modeling on UVC using a reliable data is useful to reduce the collisions. Objectives This study aims to understand spatial and temporal UVC characteristics by examining various parameters related to habitat, traffic, and seasonality using a UVC dataset that may be regarded as near-complete UVC data covering all road types across the Republic of Korea. Methods A total of 25,755 UVC points were collected between 2019 and 2021 using a standardized method by over 5,000 road menders in the Republic of Korea. Seasonal UVC predictive models of three ungulate species, Capreolus pygargus, Hydropotes inermis, and Sus scrofa, were generated using a machine-learning algorithm software, MaxEnt. Results The results showed that the peak UVC seasons coincided with the most active seasonal behaviors of the studied ungulates. In C. pygargus, habitat variables are most important for models across seasons, and UVC events are most likely to occur in high mountain chains. In H. inermis, habitat and traffic variables are most important for models across seasons. Although the important habitat for the models were different across seasons for S. scrofa, the maximum speed was consistently critical for models across all seasons. Conclusions Factors critical for UVC in the Republic of Korea were different for all three ungulate species and across seasons, indicating that seasonal behavior should be considered along with landscape and traffic features to mitigate UVC.