With objective condition scores and a geo-referenced map of every segment, Swampscott quickly shifted from reactive decisions to a clear, comprehensive plan that residents could see and understand.
Swampscott’s pavement decisions were hampered by outdated and inconsistent information, making it hard to know which roads to fix and when, and leading to uncertainty in annual paving budgets. Without current, objective data, staff often found themselves playing defense to resident complaints—answering constant “Why not my road?” questions and struggling to justify actions in public meetings. The result was a reactive, worst-first approach that consumed resources and delayed the strategic, community-wide improvements residents expected.
Swampscott chose a data-driven approach with Cyvl, using vehicle-mounted LiDAR and sensors to rapidly scan 44 roadway miles and capture precise surface and roadway condition data. Cyvl’s Infrastructure Intelligence platform used AI to transform that raw data into segment-level condition scores, prioritized repair lists, and defensible multi-year paving plans—complete with dashboards, maps, and exportable reports. Detailed, actionable pavement condition data for all 44 miles was delivered in weeks, with final deliverables on December 7, 2023, giving leaders immediate insight to plan the next construction season with confidence.
With objective condition scores and a geo-referenced map of every segment, Swampscott quickly shifted from reactive decisions to a clear, comprehensive plan that residents could see and understand. The city reduced planning time from months to weeks, aligning budgets, treatments, and schedules so crews could move faster and deliver visible results sooner. Transparency improved public trust, while better coordination stretched taxpayer dollars further and focused work where it would help the most people.