With detailed, actionable pavement condition data covering all 47 miles, Maynard accelerated project planning and put crews to work sooner.
Maynard’s public works team had fragmented, outdated road condition notes and limited visibility across the network, making it hard to know which streets to fix and when. Without accurate, consistent data, paving budgets were difficult to defend, and leaders faced regular questions at town meetings about why some streets moved ahead of others. Crews often had to respond to complaints and short-term issues instead of executing a clear, transparent, and strategic work plan for the entire community.
Maynard chose Cyvl to rapidly survey all 47 roadway miles using vehicle-mounted LiDAR and advanced sensors, capturing precise pavement condition at traffic speed. Within weeks, Cyvl delivered results on November 3, 2023 through the Infrastructure Intelligence platform, where AI transformed raw data into street-level condition scores, maps, and reports. The city gained prioritized repair lists and defensible multi-year plans grounded in objective data, enabling faster decisions and immediate action on high-priority needs.
With detailed, actionable pavement condition data covering all 47 miles, Maynard accelerated project planning and put crews to work sooner. Leaders used clear visuals and metrics to communicate tradeoffs, streamline budget approvals, and set expectations with residents. The shorter timeline from survey to plan meant less time living with deteriorated pavement and quicker relief for drivers, cyclists, and emergency services.