With comprehensive street-level data delivered by July 8, 2024, Mamaroneck shifted from reactive maintenance to a confident, proactive program.
Mamaroneck’s paving decisions were driven by outdated and inconsistent road assessments, making it hard to know which streets to fix or when and leaving budgets built on guesswork. Without a clear prioritization method and citywide data, the team stayed reactive to complaints and faced “Why not my road?” at public meetings. Manual spot checks were slow and unsafe, and the data was already stale by the time reports were ready, limiting the city’s ability to act quickly for residents.
Mamaroneck chose Cyvl to rapidly survey the network using vehicle-mounted LiDAR and sensors, capturing objective pavement conditions across 42 roadway miles. Within weeks, Cyvl’s Infrastructure Intelligence platform used AI to transform the survey into detailed, actionable pavement condition data, producing condition scores for every street and GIS-ready layers tied to clear recommendations. The city received prioritized repair lists and defensible paving plans by July 8, 2024, giving leaders the reports and visuals they needed to make better decisions and move projects to construction faster.
With comprehensive street-level data delivered by July 8, 2024, Mamaroneck shifted from reactive maintenance to a confident, proactive program. Department leaders now schedule work based on objective condition scores, aligning budgets and timelines to resident needs. Most importantly, speed translated to faster infrastructure improvements in neighborhoods, with less time between data collection and on-the-ground repairs.