Dartmouth, MA

Dartmouth, MA Infrastructure Assessment

Summary

• 203 roadway miles scanned with LiDAR to deliver current, defensible street conditions that speed safer repairs for residents
• AI-powered condition scores and prioritized repair lists delivered in weeks by January 14, 2025, enabling proactive work scheduling
• Clear reports and maps boosted transparency, reducing 311 complaints and making town meetings smoother

Problem

Dartmouth’s road program relied on outdated spreadsheets and drive-by assessments, making it hard to know which streets to fix or when and leaving paving budgets built on guesswork. Town leaders were routinely pressed in meetings with "Why not my road?" and fielded constant 311 emails, but they lacked a clear, defensible way to explain priorities. Without consistent, current pavement condition data, projects stayed reactive and residents waited longer for visible improvements.

Solution

To change course, Dartmouth selected Cyvl to rapidly survey the full network using vehicle-mounted LiDAR and sensors, scanning 203 roadway miles in weeks. Cyvl’s Infrastructure Intelligence platform used AI to transform the scans into detailed, actionable pavement condition data for every mile, including segment-level condition scores, mapped distresses, and prioritized repair lists with shareable reports. Delivered by January 14, 2025, the city received defensible plans and GIS layers that empowered staff to build a comprehensive, multi-year paving program and mobilize work faster.

Impact

With a complete, current view of its streets, Dartmouth moved from reactive patching to data-driven planning that residents could understand and trust. Comprehensive pavement condition scores for all 203 miles, delivered in weeks by January 14, 2025, gave crews a head start on spring paving and accelerated pothole response. Clear selection criteria and easy-to-read maps improved transparency, helping officials explain choices and address the most critical corridors first.

• 203 roadway miles scanned and scored, enabling block-by-block repair prioritization residents can see on a map
• Plans delivered by January 14, 2025, so crews could line up spring paving sooner and fix potholes faster
• Data-driven budget proposals showed efficient use of taxpayer dollars and helped win approvals for paving and maintenance
• Fewer 311 complaints as timelines and selections were communicated with objective data
• Work scheduling, contractor coordination, and public communication became faster and easier with shared, trusted data
• Town meetings ran smoother with clear answers to "Why not my road?" grounded in defensible analysis