In weeks—not months—Middletown shifted from guesswork to a citywide, data‑driven plan delivered on April 22, 2024.
Middletown’s road program relied on outdated and inconsistent information, making it difficult to know which streets to fix and when, and preventing accurate paving budgets. Without a clear prioritization method, staff stayed reactive to complaints and faced constant “Why not my road?” questions at town meetings. Manual windshield surveys were slow and unsafe, and by the time reports were assembled, the data was stale—making it hard to defend spending and schedule work with confidence.
To move from reactive to proactive, Middletown chose Cyvl to capture a complete, objective view of its streets and signs. Using vehicle‑mounted LiDAR and high‑resolution sensors, Cyvl rapidly surveyed 206 roadway miles and inventoried 5,286 signs, then processed the data in the Infrastructure Intelligence platform, which uses AI to turn raw scans into decisions. The city received detailed pavement condition scores, segment‑level analyses, prioritized repair lists, and defensible work plans and reports—so leaders could act faster and communicate clearly with residents.
In weeks—not months—Middletown shifted from guesswork to a citywide, data‑driven plan delivered on April 22, 2024. With network‑wide pavement condition data and a complete sign inventory, the city scheduled repairs sooner, coordinated crews more efficiently, and reduced disruption to neighborhoods. Residents saw quicker fixes, clearer explanations of what comes next, and stronger budget requests tied to transparent engineering data.