With trusted, current data across 2 roadway miles and a complete inventory of 313 assets, Westfield moved from uncertainty to confident, proactive action in weeks, not months.
Westfield’s street program relied on outdated and inconsistent data, leaving staff unsure which roads to fix and when. Without a clear, defensible prioritization method, work plans often shifted in response to complaints, and crews were stuck reacting instead of planning. Budget conversations were hard to defend because estimates lacked objective condition scores and a current asset inventory.
Westfield selected Cyvl to rapidly survey its streets using vehicle-mounted LiDAR and sensors, scanning 2 roadway miles and capturing high-resolution imagery for analysis. Within weeks, Cyvl’s Infrastructure Intelligence platform used AI to generate segment-level pavement condition scores, prioritized repair lists, and defensible work plans, delivered on February 28, 2025. The team also produced a precise asset inventory—65 signs and 313 total assets (139 trees, 69 trash bins, 35 manhole covers, 33 sidewalk segments, 17 curbs, 16 hydrants, and 4 ramps)—linking each asset to location-based work orders and budget scenarios.
With trusted, current data across 2 roadway miles and a complete inventory of 313 assets, Westfield moved from uncertainty to confident, proactive action in weeks, not months. The city now has detailed, actionable pavement condition data and AI-backed plans that turn data into scheduled work residents can see. Clear maps and reports make it simple to explain priorities at public meetings, defend budgets, and sequence repairs to reduce disruption.