How US Cities Are Using AI to Revolutionize Road Maintenance and Repair Priorities

Pothole panic? Not anymore. Cities across the US are deploying AI-powered systems to scan roads, predict failures, and slash repair costs—while taxpayers wonder why it took this long.

The Algorithm That Spots Cracks Before They Become Craters

Machine vision cameras mounted on municipal vehicles now detect early-stage pavement damage with 94% accuracy—before human crews even notice. No more reactive patching; cities fix problems before they escalate.

Budget Black Hole? Not This Time

By prioritizing the worst roads first, AI cuts wasteful spending on politically motivated 'nice-to-have' projects. Finally—a government system that actually optimizes for ROI instead of reelection campaigns.

The Cynical Take

Wall Street bankers weep into their lattes—all that municipal bond volatility just got disrupted by... pavement algorithms. Guess they'll have to find another way to short infrastructure.

TLDRs:

• US cities increasingly deploy AI to detect potholes and hazardous driving conditions.
• San Jose reports 97% AI accuracy, while Hawaii distributes 1,000 smart dashcams.
• GovAI Coalition facilitates data sharing and open standards across local governments.
• Independent audits remain scarce, leaving long-term infrastructure impact uncertain.

As urban roads in the United States age and deteriorate, municipalities are turning to artificial intelligence (AI) to monitor infrastructure and prioritize repairs.

Cities and states are deploying advanced tools, from AI-enabled dashcams to machine learning algorithms, in an effort to identify hazards more quickly and manage limited maintenance budgets efficiently.

Hawaii recently began distributing 1,000 AI-equipped dashcams to volunteer drivers, aiming to collect real-time road data. Similarly, San Jose, California, has expanded its AI systems to map potholes and other road defects, boasting an impressive 97% detection accuracy in local tests. Meanwhile, Texas has Leveraged AI analytics alongside cellphone and dashcam data to assess road signs and evaluate risky driving behavior, showing the versatility of these tools in urban infrastructure management.

Massachusetts-based Cambridge Mobile Telematics is supporting several states in these efforts, focusing on identifying hazardous driving patterns and aligning repair efforts with real-world risks. According to officials, AI enables faster hazard detection, which could translate into more timely interventions and, potentially, safer roads as traffic fatalities rise in some regions.

AI Tools Track Road Conditions

While the initial results are promising, experts caution that independent evaluations of AI’s effectiveness in road monitoring are limited.

San Jose’s reported 97% accuracy has yet to be confirmed by third-party audits measuring broader impacts such as crash reductions, repair speed, or long-term maintenance costs. A 2025 study highlighted even higher accuracy, 99.9%, for road roughness classification using Random Forest machine learning, yet challenges remain in varying lighting conditions and categorizing adjacent pavement issues.

The shift from traditional manual surveys to semi-automated and fully automated assessments has accelerated over the last two decades, but without standardized metrics and audits, distinguishing genuine infrastructure improvements from the enthusiasm surrounding AI adoption is difficult.

Dashcams and Data Drive Insights

Data collected from dashcams, smartphones, and other vehicle sensors feed into AI models that can predict which road segments require attention.

By integrating Geographic Information Systems (GIS) with machine learning, agencies can create predictive maintenance schedules, reduce redundant inspections, and allocate resources more strategically. These systems also allow municipalities to track performance over time and refine budgeting forecasts.

GovAI Coalition Promotes Best Practices

The GovAI Coalition, launched publicly in 2024, is a collaboration among local governments to share data, insights, and open-source tools.

The coalition encourages privacy-first platforms that can ingest multi-vendor telematics data, handle redaction and retention, and support cross-agency collaboration.

By aligning with coalition standards, municipalities can avoid duplicated efforts and negotiate more efficiently with private vendors, fostering greater transparency and interoperability.

Experts Urge Independent Evaluations

Despite growing adoption, AI road monitoring still faces scrutiny. Experts emphasize that rigorous, independent audits are necessary to ensure that these technologies deliver tangible infrastructure improvements rather than simply fulfilling contractual or pilot program goals.

As AI becomes more integrated into urban planning and safety initiatives, standardized evaluation metrics will be crucial in verifying that the technology truly enhances road conditions and public safety.