VDOT Shoulder Rumble Strip Planning

VDOT has prioritized shoulder rumble strips to help address roadway departure crashes, but many rural paved roads lack the lane and shoulder width to accommodate them. Deciding which roads are eligible takes accurate geometric measurements.

Contractors used to collect these measurements by hand, with measuring wheels, at intervals of several hundred feet. The process was:

• Labor-intensive: manual field measurements.
• Costly: fixed mobilization and extended field time.
• Potentially unsafe: crews working in active travel lanes on narrow, winding rural roads.
• Limited in detail: measurements several hundred feet apart gave only a rough picture of changing roadway conditions.

VDOT's Salem District turned to RDV Systems for a LiDAR-based approach to roadway measurement. Using RSA 3D, mobile LiDAR data was turned into detailed lane and paved-shoulder measurements at 10-foot intervals across the study corridors.

The digital workflow produced a consistent, high-resolution geometric dataset, with no manual measurements taken in live traffic.

RSA 3D replaced widely spaced manual measurements with consistent roadway geometry measured every 10 feet, giving VDOT a more detailed dataset for deciding where shoulder rumble strips fit.

• Improved accuracy. Measurements every 10 feet gave a more detailed picture of roadway width.
• Reduced cost and schedule. Digital analysis cut the labor, mobilization and time of traditional field measurements.
• Enhanced safety. Field crews no longer needed to collect measurements within active travel lanes.
• Reusable data. The result is a planning-level geometric dataset that can support future safety analysis, design evaluation and corridor planning.
• Faster, smarter decisions. VDOT could rapidly assess rumble strip eligibility, prioritize feasible locations and make better use of limited safety funding.

The VDOT project shows how LiDAR can replace labor-intensive field measurements with a repeatable digital workflow. By extracting roadway geometry at consistent intervals, RSA 3D gives agencies detailed information for safety planning while reducing personnel exposure and manual data collection.

It also showed the potential to extend automated analysis beyond individual corridors to whole roadway networks. Applying consistent criteria across large datasets, agencies can identify potential deficiencies, focus engineering resources and prioritize locations for further investigation. The same dataset supports:

• Safety analysis: roadway safety evaluations.
• Design evaluation: design and improvement projects.
• Corridor planning: long-term network planning.

RSA 3D turns LiDAR and roadway data into detailed, engineering-ready geometric measurements without traditional field measurement. By automating roadway analysis within a repeatable 3D environment, it helps agencies improve data consistency, reduce field exposure, accelerate planning and make better-informed roadway safety decisions.

For VDOT, it turned existing LiDAR into detailed, reusable roadway intelligence, with less manual field measurement and safer, more efficient transportation planning.