Maple Grove, MN

In Maple Grove, rapid growth left hundreds of streets aging at the same time, while pavement decisions relied on subjective drive-through surveys. Partnering with WSB and Cyvl gave the city its first full network assessment—an objective baseline that helps stretch every dollar further and keep roads and trails in better condition citywide.

The Challenge: A Growing City Facing Pavement Lifecycle Crunch

Maple Grove is one of Minnesota’s fastest-growing suburbs, with much of its roadway network built during the city’srapid expansion in the 1990s and early 2000s. That meant a large portion of streets were all hitting the 20–30 yearpavement lifecycle at the same time—threatening to overwhelm the city’s rehabilitation and reconstruction budget.

Before Cyvl, pavement decisions relied on fall “drive-through” surveys by engineers, supplemented by ad hoc patchingdata. This process left gaps in consistency, introduced subjectivity, and made long-term financial planning difficult.

The Turning Point: Objective Data to Drive Strategy

To get ahead of the looming wave of pavement needs, Maple Grove engaged WSB and Cyvl to run a full network scan—covering 300–400 miles of roadway and the trail network. Using Cyvl’s AI-powered pavement assessment, the city received:

• Comprehensive PCI ratings across the entire network
• Breakdown of treatment needs (reconstruction, full-depth reclamation, mill & overlay, preventative maintenance)
• Integration into Cartograph (OpenGov) for ongoing asset management

For the first time, the city had a consistent, scientific baseline of its pavement condition. In the past, pavement ratings relied on staff or consultants manually driving or walking streets, introducing variability and inconsistency year to year. With Cyvl’s AI-powered assessments, Maple Grove gained a fast, repeatable, and objective measurement that didn’t depend on “who was out in the field that day.]..[then we can still go out and do some verification manually, but now we’re starting with a reliable baseline.”

The Solution: Stretching Every Dollar Further

With Cyvl’s data in hand, Maple Grove restructured its capital improvement plan:

• From reactive reconstruction to proactive rehabilitation: Instead of budgeting primarily for full recons, the city can now allocate more to mill & overlays, crack sealing, and surface treatments.‍
• Informed treatment mix: The network split almost evenly into thirds: roads needing reconstruction, FDR, or overlays—helping prioritize resources effectively.‍
• Council-ready visuals: Pie charts, bar graphs, and mapped outputs helped city staff clearly communicate funding strategies and pavement lifecycle concepts (like the cost savings of intervening before PCI drops below 60)

The result is a smarter, data-backed program that maximizes every dollar, extends pavement life, and gives Maple Grove a clear, defensible roadmap for maintaining its growing network.

The Impact: Measurable ROI for Maple Grove

1. 4X Budget Efficiency:

• By shifting strategies, they can now treat 20 miles of roadway per year vs. 5 miles previously, without increasing annual spend.

2. Data-Friven Credibility:

• Data from multiple departments was consolidated into a unified, accurate asset inventory, creating a single source of truth as of January 2025.

3. Extended Pavement Life:

• Proactive maintenance slows deterioration and avoids the sharp cost escalation of late-life reconstruction.

4. Multi-Departmental Use:

• Public Works, Engineering, and Parks & Rec are leveraging the same dataset to prioritize both roads and trails.

Results That Matter

Maple Grove’s investment in Cyvl’s platform is already paying off. With a single scan, the city established its first objective pavement baseline, reshaped its capital plan, and unlocked the ability to maintain 4X more roadway each year with the same budget. Just as importantly, staff and council now share a common dataset that builds trust and credibility across departments and with residents.

As City Engineer R.J. Kakach explains, “Using Cyvl was a great way to get the data quickly and take the subjectivity out of it… you’re getting that hard, fast number from the computer.”