Deer-vehicle collisions are a significant risk to public safety on Minnesota roads—causing injuries and death for humans and animals, and property damage. Minnesota ranks among the top 10 riskiest states for deer-vehicle collisions in the nation.
In a new research project, investigators will improve the understanding of factors (such as geography, road type, land use, deer, and traffic volume) that lead to higher deer-vehicle collision risk in Minnesota. To prioritize funding for counter-measures, the team will identify areas that would benefit from action to reduce deer-vehicle collisions.
“Minnesota has a relatively high number of deer-vehicle collisions,” said Christopher Smith, MnDOT wildlife ecologist. “We are excited about the project’s use of innovative analytical methods to identify variables that increase deer-vehicle collision risk and help prioritize locations to deploy countermeasures and improve driver safety.”
- Literature Review: Conduct a literature review of deer-vehicle collisions in Minnesota.
- Data: Identify, obtain, and develop GIS-based data sets to use in the analysis.
- Field Project: Locate deer-vehicle collisions over 12 months to estimate the number of unreported deer-vehicle collisions.
- Machine Learning Based Model: Develop a data-driven machine learning model for deer-vehicle collision likelihood by identifying risk factors from previous collision locations.
- Hotspot Mapping: Plot hotspots (geographic areas that correlate to high deer-vehicle collision rates) on an interactive map.
- Estimated Start Date: 05/13/2021
- Estimated Completion Date: 11/30/2023
- Funding: MnDOT
- Principal Investigators: Raphael Stern, Ron Moen
- Technical Liaison: Christopher Smith
Details of the research study work plan and timeline are subject to change.
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