High Friction Surface Treatment is an effective countermeasure for reducing crashes at horizontal curves, yet current site selection practices rely heavily on historical crash data and manual field inspections. These approaches limit agencies’ ability to proactively identify high-risk locations and efficiently allocate limited safety resources. This project addresses these limitations by developing a scalable, data-driven framework for HFST site prioritization at the network level.
The research will create an automated data-processing pipeline that extracts roadway geometry and surface characteristics from mobile LiDAR and video log imagery, including curve radius, superelevation, signage, and surface condition. These features will be integrated with pavement condition and crash data to identify high-risk and constructible HFST locations. The approach will be validated through a case study using MassDOT roadway and crash data. Results will provide transportation agencies with a transferable methodology for proactive HFST deployment, improving safety outcomes and supporting more efficient infrastructure management.