Within weeks of kickoff, Temecula had a complete, defensible picture of its streets, delivered by March 10, 2025.
As Temecula’s street network aged at different rates across neighborhoods, the city relied on inconsistent, outdated assessments that obscured true needs. Without trusted inventory and condition data, staff faced “Don’t know which roads to fix or when” and “No accurate paving budgets,” leaving plans exposed to second-guessing. Public meetings often turned defensive—residents asked “Why not my road?” and leaders lacked consistent evidence to justify actions and answer questions with confidence.
Temecula chose Cyvl to rapidly survey 84 roadway miles using vehicle-mounted LiDAR and sensors, capturing lane-level pavement conditions and high-definition imagery across the entire network. Cyvl’s Infrastructure Intelligence platform applied AI to transform raw scans into block-by-block condition scores, distress mapping, prioritized repair lists, and defensible, scenario-based paving plans with cost and timeline options. Delivered on March 10, 2025, the city received interactive reports and exports that turned data into faster decisions, transparent communications, and shovel-ready work plans.
Within weeks of kickoff, Temecula had a complete, defensible picture of its streets, delivered by March 10, 2025. With 84 miles quantified and scored, staff scheduled work earlier in the season and coordinated crews with confidence. The shift to timely, trustworthy data shortened the gap between assessment and action, so residents felt the improvements sooner.