Delivered on August 16, 2024, the complete pavement dataset gave city leaders a comprehensive, network-wide view they could explain in plain terms to residents and the council.
Wyoming, MN faced heavy freeze–thaw cycles and steady commuter traffic that accelerated pavement distress across neighborhood streets and key corridors. Without current, consistent condition data, the city didn’t know which roads to fix or when and struggled to build accurate paving budgets. Public works staff were stuck reacting to complaints and performing manual windshield and foot surveys that were slow, unsafe, and outdated by the time reports were compiled, making it hard to justify plans in town meetings.
Choosing a faster, data-driven path, the City of Wyoming engaged Cyvl to rapidly survey 68 roadway miles using vehicle-mounted LiDAR and sensors, capturing lane-level cracking, rutting, and surface defects. Within weeks, Cyvl’s Infrastructure Intelligence platform used AI to convert the scans into detailed pavement condition scores, block-by-block repair recommendations, and exportable, defensible work plans and budget scenarios. The city now had trustworthy, up-to-date pavement data for every mile surveyed, along with clear analytics to prioritize work, communicate tradeoffs, and move from analysis to action much faster.
Delivered on August 16, 2024, the complete pavement dataset gave city leaders a comprehensive, network-wide view they could explain in plain terms to residents and the council. Instead of months of manual surveys and rework, Wyoming received a validated condition model and ready-to-use plans in weeks, shortening the time between identifying issues and putting crews on the street. That speed translated directly to resident benefits—safer travel, quicker attention to the highest-need corridors, and transparent, defensible decisions supported by clear visuals and numbers.