With high-confidence data in hand, Newington moved from reactive fixes to a proactive, transparent program that aligns work with resident needs, safety, and high-use corridors.
Newington struggled to see true pavement conditions across its network because staff relied on outdated and inconsistent data that did not match what crews saw on the street. Without current, accurate information, the team found it hard to build defensible paving plans and often played defense to resident complaints, with people asking, "Why not my road?" in public meetings. The lack of a clear prioritization method made budgets hard to justify, slowed work scheduling, and kept the city reactive instead of proactive.
Newington chose Cyvl to rapidly survey the entire network using vehicle-mounted LiDAR and sensors, scanning 105 roadway miles in days. Cyvl’s Infrastructure Intelligence platform used AI to transform raw scans into detailed, actionable pavement condition data, network and block-level condition scores, prioritized repair lists, and clear reports that leaders could share. Delivered on June 16, 2025, the analysis provided defensible plans that helped the city make fast, confident decisions and move projects into construction sooner.
With high-confidence data in hand, Newington moved from reactive fixes to a proactive, transparent program that aligns work with resident needs, safety, and high-use corridors. The city assembled a multi-year paving plan in weeks instead of months, shortening the time between data collection and project implementation so residents see improvements faster. Clear condition scores and cost scenarios made it easier to secure support, schedule crews efficiently, and explain decisions to the community.