Predictive Maintenance for Road and Highway Assets
Pavement, signs, signals, drainage — AI-driven asset management for road networks is now production-credible. Where the savings are.
DOTs and toll authorities own road networks with hundreds-of-thousands to millions of distinct assets — pavement segments, signs, signals, drainage structures, ITS devices. Predictive maintenance at this scale is an asset-management problem that AI is genuinely good at when paired with credible inspection data.
Where the savings come from.
Pavement condition forecasting#
Pavement Condition Index (PCI), traffic, climate, structural data feed ML models that forecast deterioration. Better than empirical curves alone, especially in mixed-climate regions.
Output: per-segment forecast of when condition will drop below intervention threshold. Combined with treatment cost models, produces an optimized capital plan.
Production-deployed at multiple state DOTs. The savings over rule-of-thumb planning are real and quantifiable.
Treatment selection optimization#
Given current condition, traffic, climate, and budget, which treatment is most cost-effective? Crack seal, overlay, mill-and-fill, full reconstruction. AI tools recommend; engineer reviews.
Earns its place at the network level. Project-level treatment decisions still involve site visits and engineering judgment.
Sign and signal inventory#
Vision models on continuous road imagery (DOT van captures, sometimes drone) detect, classify, and condition-score signs and signals. Auto-update the asset inventory with locations and conditions.
Saves real money compared to manual inventory updates, which most DOTs do on a 5–7 year cycle (meaning the inventory is mostly stale).
Drainage inspection#
CCTV of culverts and drainage structures feeds vision models that detect debris, structural defects, scouring. Continuous improvement vs the annual inspection cycle.
Pothole and crack detection from connected vehicles#
Smartphone accelerometer data from city fleets (and increasingly, consumer apps with permission) detects pavement defects. Aggregated across thousands of vehicle-passes, produces high-confidence defect maps.
Less mature than other use cases; promising for high-density urban networks.
Where AI doesn’t (yet) earn its place#
Replacing the field inspector for high-consequence assets (bridges, retaining walls). See our bridge inspection notes.
Geographic regions without inspection data. AI extrapolates from training data; it doesn’t invent ground truth.
Politically driven capital decisions. AI produces optimization; political reality often drives final choices. Use the AI to inform, not to dictate.
The integration question#
Road asset management runs on AgileAssets, Cartegraph, Lucity, sometimes custom systems plus AASHTOWare. AI tools must integrate; standalone tools produce reports nobody uses.
Our data engineering practice builds these integrations for DOTs and infrastructure owners.
What we ship for DOT and toll programs#
For road-asset management engagements:
- Pavement deterioration forecasting integrated with the firm’s pavement management system
- Treatment optimization with cost-data feedback
- Vision-based asset inventory updating
- Drainage inspection analytics pipeline
- Network-level capital planning analytics
The funding context#
Federal infrastructure funding (IIJA in the US, equivalent programs in EU, AU, NZ) made AI-driven asset management a much higher priority for funded programs. DOTs that build the analytical capacity now will be the ones whose project lists hold up under federal scrutiny.
Road asset AI earns its place at the network and portfolio level. Our team builds the data and analytics platforms for road owners. Tell us about the program.