North Andover, MA

North Andover, MA Infrastructure Assessment

Summary

• Data-driven paving plan delivered in weeks, not months, for North Andover’s 151 roadway miles—faster fixes residents can see.
• Clear, defensible priorities reduce 311 complaints and make town meetings smoother and more transparent.
• Accurate budgets and targeted work improve safety while maximizing the value of every taxpayer dollar.

Problem

North Andover’s roads face heavy wear from New England freeze–thaw cycles, but the city lacked current, consistent pavement condition data and had no accurate paving budgets to guide investment. Public works staff were stuck playing defense—fielding 311 complaints and town‑meeting questions like "Why not my road?" without a clear, defensible prioritization method. Manual windshield surveys were slow and outdated by the time reports were ready, delaying projects and making it hard to demonstrate efficient use of taxpayer dollars.

Solution

North Andover chose Cyvl to rapidly survey its streets with mobile LiDAR and high‑resolution sensors, capturing objective pavement distresses across 151 roadway miles. Within weeks, Cyvl’s Infrastructure Intelligence platform used AI to turn raw measurements into detailed condition scores, interactive maps, and prioritized repair and preservation lists aligned to service goals. The city received defensible capital and maintenance plans, exportable reports for council and residents, and a clear path to schedule work, set budgets, and act faster with trustworthy data.

Impact

By August 14, 2025, North Andover had a complete, street‑level pavement condition dataset for 151 miles, delivered in weeks rather than months. This speed translated into immediate scheduling of high‑priority repairs and preservation, shrinking the time between data collection and visible improvements residents can feel in daily travel. With clear scores, maps, and cost scenarios, leaders communicated tradeoffs, defended budgets, and coordinated crews with confidence, improving safety and customer service across neighborhoods.

• 151 roadway miles scanned with LiDAR and AI‑scored to produce block‑by‑block condition ratings and a prioritized work list residents can follow.
• Weeks‑not‑months turnaround enabled faster pothole repairs and curb‑to‑curb paving on the highest‑priority segments, lowering repeat complaints.
• Transparent maps and reports made town meetings smoother by showing why each street was prioritized and what is scheduled next.
• Noticeable drop in 311 calls as crews addressed top‑need corridors sooner and communicated timelines clearly.
• Budget allocation, public communication, and work scheduling are now far simpler with reliable data informing every decision.
• Stronger justification for funding requests by linking dollars to quantified pavement needs and expected outcomes, delivering more value for taxpayers.