Transforming Asset Management At Honda And The Ohio Department Of Transportation — Ohio, United States

State transportation agencies are under increasing pressure to maintain aging infrastructure more efficiently while improving safety and responsiveness. The Ohio Department of Transportation (ODOT) manages nearly 49,000 lane miles of roadway using inspection processes. These processes have historically depended on frequent, manual field reviews. As expectations rise alongside advancements in connected and automated vehicle technologies, ODOT sought a more scalable, data-driven approach to transportation asset management.

We partnered with the Ohio Department of Transportation, Honda of America Manufacturing, and the University of Cincinnati to demonstrate how connected vehicle data could be operationalized to support proactive maintenance decisions. Specifically, the pilot explored whether production-vehicle sensor data, when fused with advanced analytics and integrated into existing agency systems, could reliably detect roadway conditions and automate maintenance workflows.

AI-Driven Detection, Built For Real-World Roads

As systems integrator, Parsons, in collaboration with the University of Cincinnati, designed and delivered the end-to-end framework that transformed vehicle-generated data into maintenance-ready intelligence. Production Honda vehicles were equipped with after-market vision sensors and LiDAR. These sensors collected roadway condition data during normal driving operations.

Parsons ingested and normalized this data within its iNET® Asset Guardian platform, where detections were geospatially visualized and prioritized through intuitive dashboards. The platform streamlined the transition from detection to action by allowing maintenance needs to be grouped by proximity and severity. It also automatically generated maintenance work orders with supporting imagery, with the click of a button. We worked closely with ODOT field and leadership teams to incorporate continuous feedback and refine system accuracy, workflows, and usability. To accelerate innovation without operational risk, the solution was deployed outside of ODOT’s production environment. This approach enabled rapid iteration and learning.

Turning Insight Into Savings

During a four-month pilot across two ODOT districts, Parsons’ platform analyzed nearly 2,000 roadway miles and identified thousands of roadway and roadside asset deficiencies. Detection accuracy exceeded 99 percent for traffic signs. It reached 98 percent for lane markings, 93 percent for guardrails, and up to 84 percent for potholes — demonstrating that connected vehicle data can meet DOT accuracy and reliability expectations when operationalized through a robust platform.

The pilot delivered measurable operational and financial benefits while establishing a scalable model for modern asset management:

• Reduced reliance on frequent manual inspections, improving field safety and lowering inspection burden
• Faster, more consistent maintenance planning through prioritized, data-driven workflows
• Improved visibility into roadway conditions across both urban and rural environments
• Estimated $4.53M in annual labor savings from reduced inspection and planning effort
• Additional annual savings from streamlining maintenance cycles and shifting to an as-needed approach.

Beyond immediate outcomes, the project demonstrated our ability to integrate emerging vehicle technologies, advanced analytics, and operational workflows into scalable, repeatable solutions. This helps transportation agencies move from reactive inspections to proactive, intelligence-driven infrastructure management.

Read More About This Project

• Honda News | Honda and DriveOhio Complete Pilot of Nation’s First Proactive Roadway Maintenance System Aimed at Creating Safer Roads
• Car And Driver | Honda Has Invented an AI Heads-Up About Potholes and Road Hazards
• WKYC Studios | Honda, ODOT complete pilot of first proactive roadway maintenance system in US