The fast delivery gave Southborough a head start on the next construction season, shrinking the time between data collection and project implementation.
Outdated and inconsistent pavement data left Southborough unsure which roads to fix and when, and made paving budgets difficult to forecast or defend. Public works often played defense to 311 complaints and town-meeting questions of “Why not my road?”, which made prioritization appear biased rather than objective. This reactive cycle slowed projects, increased resident frustration, and kept crews chasing issues instead of executing a citywide plan.
To change that, Southborough chose Cyvl to rapidly survey all 71 roadway miles using vehicle-mounted LiDAR and sensors, capturing high-resolution geometry and surface condition across the network. Within weeks, Cyvl’s Infrastructure Intelligence platform applied AI to produce segment-level condition scores, objective prioritization, budget scenarios, and ready-to-use maps and reports delivered on December 20, 2022. Armed with defensible data, the city immediately generated prioritized repair lists and a clear, explainable plan that linked investments to resident outcomes and faster action.
The fast delivery gave Southborough a head start on the next construction season, shrinking the time between data collection and project implementation. Public works could schedule crews and procure paving with confidence, and explain decisions with transparent metrics in council and neighborhood meetings. Most importantly, residents saw quicker fixes on the most critical segments, fewer repeat potholes, and a clearer understanding of when their street would be addressed.