Armed with trustworthy, current pavement condition data, Newton shifted from reactive to proactive program management.
Newton manages a dense, mixed-use street network where school traffic, bus routes, and freeze-thaw cycles stress pavement differently across neighborhoods. Outdated or inconsistent data left leaders unsure which roads to fix and when, making accurate paving budgets hard to defend. Crews were often reactive to complaints without a clear prioritization method, leading to potholes, flat tires, and frustrated residents asking why their street was not selected.
Newton selected Cyvl to rapidly survey the entire network using vehicle-mounted LiDAR and sensors, capturing lane-level pavement condition across 282 roadway miles. Within weeks, Cyvl’s Infrastructure Intelligence platform applied AI to produce objective condition scores, block-by-block analytics, and interactive maps, delivered on September 24, 2024. The city received prioritized repair lists, scenario-based paving plans, and exportable, defensible reports that enable faster decisions and field action.
Armed with trustworthy, current pavement condition data, Newton shifted from reactive to proactive program management. By replacing months-long manual assessments with a weeks-long data pipeline, the city scheduled paving and repairs sooner, shrinking the gap between data collection and construction. Residents benefit from faster fixes, clearer timelines, and transparent explanations of how taxpayer dollars are allocated.