The city received decision-ready maps, scores, and segment-level details in weeks instead of months, shrinking the time between data collection and project kickoff.
Woodinville’s street network was being managed with outdated and inconsistent data, making it hard to know which roads to fix or when. Without a clear, defensible prioritization method, the city often reacted to complaints and spent time answering “Why not my road?” at council and neighborhood meetings. Leaders needed timely, trustworthy information to plan work, align budgets, and show residents how choices were made.
Woodinville chose Cyvl to rapidly survey 59 miles of roadway using vehicle-mounted LiDAR and sensors, creating an objective record of pavement conditions across the entire network. Within weeks, and by May 17, 2024, Cyvl’s Infrastructure Intelligence platform used AI to turn raw data into condition scores, prioritized repair lists, and ready-to-use reports that align maintenance with budgets and timelines. With detailed, actionable pavement condition data for every segment, the city assembled a defensible, comprehensive plan and moved from reacting to implementing.
The city received decision-ready maps, scores, and segment-level details in weeks instead of months, shrinking the time between data collection and project kickoff. Public works and finance staff used these outputs to coordinate treatments, schedule crews, and plan funding with clarity residents could see and understand. With simple rollups and visual reports, leaders communicated tradeoffs in public meetings, reducing confusion and building trust.