How to use RoadAI for planned and reactive road maintenance

Maintaining roads efficiently is a complex task. Road maintenance organizations are looking for more efficient, cost-effective, and safe ways to maintain their networks. This means optimization across the board: network inspections, maintenance programming, and inventory management.

Many organizations still use antiquated methods and processes to assess roads, collect data, and plan for the future. But what if there was a more efficient way?

A quick introduction to road asset management

80% of all journeys take place on the road. Over the last 50 years, road design and maintenance have improved to ensure that we can have safe and smooth journeys. But roads are designed to wear out. Road asset management is the practice used to enable engineers to understand where the roads are wearing out and target their resources on keeping the roads in good condition.

The process of road asset management breaks down into three broad areas:

• Reactive maintenance process: The day-to-day monitoring of network condition, ensuring there are no immediate safety hazards on the network
• Planned maintenance process: The measurement of a road network’s condition and planning where to fix different areas.
• Network inventory / curbside asset management: This includes things like signs, safety barriers, bollards, manhole covers, etc.

The challenge for engineers is that they're trying to manage this complex and dynamic environment that covers, in many cases, thousands of miles.

RoadAI: Fewer resources, less time, and faster data

One of several challenges for organizations that use traditional manual road condition surveys to gain knowledge of their road networks is not having any video associated with the survey data. Enter RoadAI: the best of both worlds. With this method, organizations can collect data using cell phone video footage, analyze video data, and also automatically produce distress ratings. Setup and installation are simple, and video files are uploaded automatically to the cloud.

For planned and reactive maintenance, the benefits of RoadAI abound. RoadAI offers an overall simplified process, using fewer resources and less time when compared to traditional data collection methods:

• Faster, simpler surveys: Reduce the need to produce ongoing road network surveys. With RoadAI, organizations likely need to complete only one cycle profile and one data collection cycle with RoadAI. And all of these surveys can be produced using less time and resources.
• Increased inspection speed: Drive at normal speeds, versus with restrictions, and without having to think about looking for defects or doing road condition assessments. Collect the video and the data is generated automatically.
• Ease of data collection: Data is always available at users’ fingertips. Not only is an abundance of data generated, but it is also quick and simple to sort. Users can export data into different formats — and even load it into other GIS or third-party asset management systems.
• Quick visual overview: View data as a heat map or download using a process called computer vision to process HD video and analyze each frame. The software then tags objects and features in the environment like road markings, different defect types, and repairs (like sealing, repatching, etc.)
• Detailed clustered defect data: Human recording processes tend to cluster defects into broader categories. With RoadAI, the software classifies every individual different defect type and cluster those together to simulate a human report.
• Consistent results: Even with a limited number of defect types, the process is subjective. What one person sees may differ from another, which means organizations need to train and maintain a standard. Machine-based data provides consistent identification.
• Machine-based versus using personnel: Data collection takes half the resources it normally would. Collect a whole network-wide data and have it available immediately once the process is finished, allowing for network maintenance planning — in near real-time.

Fix road issues before they start

The most critical factor of RoadAI is that organizations can start to implement early intervention asset management — and pick up defects early in the deterioration phase.

With frequent network surveying, users can pick up a minor defect, like a beginning crack, and plan to make a lower of cost treatment intervention. Sealing up that crack up — before any water or ice gets into that section of road — prevents it from breaking up further. Having access to detailed, timely data consistently enables teams to drive down the total cost of maintenance, making maintenance processes much more efficient. Does RoadAI sound like a methodology you’re interested in? Watch the webinar in its entirety — or visit our website for even more resources.