Within weeks—not months—Randolph received a complete condition map and plan for all 111 miles, in time to align the paving season, utility coordination, and contractor schedules.
Randolph’s public works team had to make paving calls without current, citywide pavement data, leaving them unsure which streets to fix and when. With residents asking "Why not my road?" at council meetings, staff struggled to justify decisions and defend budgets as complaints piled up. Manual windshield surveys were slow and inconsistent, and by the time reports were compiled, the information was already outdated.
To change that, Randolph selected Cyvl to rapidly survey the entire roadway network using vehicle-mounted LiDAR, cameras, and advanced sensors, scanning 111 miles end to end. Cyvl’s Infrastructure Intelligence platform used AI to convert the raw data into detailed, actionable pavement condition data—block-by-block condition scores, distress identification, prioritized repair lists, and defensible multi-year plans—delivered on April 12, 2024. With a single source of truth, city leaders could immediately build clear work programs, test budget scenarios, and communicate decisions with confidence.
Within weeks—not months—Randolph received a complete condition map and plan for all 111 miles, in time to align the paving season, utility coordination, and contractor schedules. The data gave Public Works the confidence to schedule crews and fast-track high-need corridors, so potholes and safety hazards are addressed sooner for residents. At public meetings, leaders now bring defensible reports and clear visuals, reducing 311 volume and accelerating budget approvals by showing efficient use of taxpayer dollars.