With 190 miles objectively scored and mapped, Bedford moved from reactive to proactive, translating data into faster, visible improvements for residents.
Bedford’s pavement program relied on sporadic, manual assessments and outdated spreadsheets, creating inconsistent data and uncertainty about which roads to fix and when. Staff struggled to build defensible paving plans and often had to play defense at town meetings, repeatedly fielding “Why not my road?” from residents. Without trusted condition data, budgets were hard to defend and work plans drifted toward worst-first fixes instead of a strategic, network-level approach.
Bedford’s public works leaders selected Cyvl to rapidly survey the network using vehicle-mounted LiDAR and sensors, capturing the city’s roadway conditions at high speed and high fidelity. Cyvl’s Infrastructure Intelligence platform used AI to convert those raw scans into detailed, actionable pavement condition data and clear, defensible reports. Within weeks—by August 1, 2023—Bedford had network-wide condition scores, prioritized repair lists, and a data-backed plan the team could explain and execute with confidence.
With 190 miles objectively scored and mapped, Bedford moved from reactive to proactive, translating data into faster, visible improvements for residents. The city could schedule work with precision, communicate priorities clearly, and deploy crews where they would deliver the greatest benefit soonest. Faster delivery of trustworthy data shortened the time between collection and construction, improved budget credibility, and reduced friction in public conversations.