By September 5, 2024, Duxbury had a complete, trusted picture of every lane mile and traffic control sign needed to guide near-term paving and maintenance.
Duxbury’s coastal roadway network faces steady wear from freeze-thaw cycles and seasonal traffic, stressing pavement and roadside assets. The town relied on outdated or inconsistent information, making it difficult to know which roads to fix, when to schedule work, or how much to budget. Without clear, current data, leaders often played defense to resident complaints and answered “Why not my road?” at town meetings without strong evidence to justify choices.
Duxbury selected Cyvl to run a rapid, vehicle-based LiDAR and sensor survey across 110 roadway miles, capturing pavement distresses and inventorying 2,154 signs with high accuracy. Within weeks, the Infrastructure Intelligence platform used AI to convert raw field data into detailed pavement condition scores, sign compliance insights, and GIS-ready layers the town could use immediately. The result was prioritized repair lists and defensible, budget-aligned plans that let staff make faster, confident decisions and communicate them clearly to residents.
By September 5, 2024, Duxbury had a complete, trusted picture of every lane mile and traffic control sign needed to guide near-term paving and maintenance. With reliable condition scores and clear priorities, the town shortened planning timelines from months to weeks, accelerating construction schedules and contractor mobilization. Residents saw quicker responses on high-need corridors, fewer surprises, and clear explanations of what gets fixed and when.