, Assistant Professor, Civil, Environmental and Geo-Engineering
Melissa Duhn, Former Lab Manager, MTO, Civil, Environmental and Geo-Engineering
Ron Moen, Associate Professor, UMD-NRRI
Deer-Vehicle Collisions (DVCs) are a significant risk to public safety on Minnesota roads, causing property damage, human injuries and deaths, and also killing deer. In 2019, there were 1,573 DVCs reported to the Minnesota Department of Public Safety (MnDPS), or roughly 4.5 DVCs per day.
Reducing the number of DVCs in a cost-efficient manner would require an analysis of DVCs, using geographic, road type, land use, deer, traffic volume, and other data to identify locations where safety measures or warnings would be most beneficial. In this project we will analyze about 10,000 DVCs to identify factors which increase the risk of a DVC in Minnesota.
Many DVCs without significant vehicle damage or passenger injury go unreported to MnDPS. We will collect roadkill data to estimate the number of unreported DVCs in 2021-2022 in the Duluth area, with possibility of applying this method more broadly across Minnesota. This data is critical to establish that DVCs that have caused significant property damage or human injury are not different from DVCs that are unreported.
We will use the results of the DVC analysis to construct a data-driven machine-learning-based model to predict hotspots for DVCs. This model will identify road and habitat features that are associated with higher DVC rates.
The products that will be available from this proposed project include:
1. Literature review on DVCs.
2. An interactive map that identifies DVC hotspots.
3. Recommendations for locations where DVC risk could be reduced with highest return on investment for improvements.
4. Pilot project to estimate unreported DVCs in and near Duluth, MN, and development of a protocol to apply across Minnesota.
These products will meet the goal of this proposed project: Identify ways to reduce the impact of DVCs on drivers in Minnesota.
- Project number: 2022001
- Start date: 05/2021
- Project status: Active
- Research area: Transportation Safety and Traffic Flow
Data and modeling, Safety