With 135 miles objectively assessed and scored, Winchendon moved from reactive repairs to proactive, transparent planning that residents can see and feel.
Winchendon managed a large local road network with outdated and inconsistent data, making it hard to know which streets to fix or when and leaving no accurate paving budgets. Without a clear prioritization method and current asset inventory, the team struggled to build defensible work plans and often had to react to complaints instead of executing a strategic program. At public meetings, leaders were forced to play defense to “Why not my road?” questions, and the lack of transparent, current information made budget discussions difficult to win.
The Town chose Cyvl to rapidly survey 135 roadway miles using vehicle-mounted LiDAR and sensors, capturing lane-level pavement distresses and geo-referenced imagery. Within weeks, Cyvl’s Infrastructure Intelligence platform used AI to generate pavement condition scores, prioritized repair lists, and budget scenarios—turning raw measurements into actionable plans and reports. Winchendon received defensible maps, dashboards, and exportable documentation that made decisions faster, scheduling clearer, and public communication straightforward.
With 135 miles objectively assessed and scored, Winchendon moved from reactive repairs to proactive, transparent planning that residents can see and feel. The Town leveraged detailed, actionable pavement condition data to sequence treatments, coordinate with utilities, and align budgets to needs—accelerating the path from data collection to construction. Residents benefit from quicker fixes, safer streets, and fewer surprises because leaders can now explain what’s being done, when, and why.