Potholes are a persistent challenge for cities. They impact safety, damage vehicles, and drive up maintenance costs. Yet despite their impact, detection has traditionally relied on manual inspections, citizen reports, or specialized measurement vehicles. These methods are time-consuming, resource-intensive, and limited in coverage.
The core issue is timing. Inspections are periodic, meaning road deterioration is often identified only after it has progressed. By then, what started as a minor defect may already have developed into a larger, more costly problem.
Connected vehicle data introduces a fundamentally different approach. It replaces periodic snapshots with continuous, real-world measurements of road condition.
As vehicles move through the road network, onboard sensors register how the road surface behaves beneath them. Variations in vertical acceleration, wheel movement, and suspension response reflect the interaction between vehicle and road. These signals can be used to assess surface condition indirectly at scale.
Individually, each observation is limited. When aggregated across large vehicle fleets, the data becomes statistically robust and reveals patterns in road roughness, surface irregularities, and anomalies across the entire network.
Because the data is collected through normal traffic, coverage is both extensive and continuously updated. This enables near real-time awareness of road conditions without deploying dedicated inspection vehicles or conducting manual surveys.
Within continuously collected connected vehicle data, potholes are identified as measurable changes in road condition.
As vehicles repeatedly travel the same road segments, they establish a baseline of surface performance. When a pothole begins to form, it appears as a sudden increase in roughness or a localized surface anomaly compared to previous measurements.
Because data is collected continuously, these changes are not only detected, they are also confirmed and refined through repeated measurements.
In practice, this enables:
Rather than relying on isolated observations, cities gain a continuously updated and objective view of road condition. This makes it possible to identify where potholes are emerging and how they develop over time.
Connected vehicle data changes how potholes are detected. The real value lies in turning measurements into decisions.
With continuous road condition data, cities can move from raw signals to actionable insights. This is where solutions like NIRA’s Road Health play an important role.
Road Health by NIRA transforms connected vehicle data into a network-wide, continuously updated view of road conditions, where potholes and surface anomalies are automatically detected. Instead of relying on periodic inspections, cities can monitor deterioration as it happens and act earlier.
The result is a more proactive and data-driven approach to road maintenance. Costs are reduced as resources can be used more efficiently, and maintenance can be planned based on actual road conditions rather than assumptions.
See how Road Health enables continuous road condition monitoring: Road Health by NIRA
Early detection of potholes has a direct impact on how maintenance is planned and executed.
Instead of reacting to visible damage or public complaints, cities can act on early indicators of deterioration. Minor defects can be addressed before they evolve into severe potholes, reducing repair costs and minimizing disruption.
At the same time, maintenance decisions become more precise. Resources can be prioritized based on measured road condition data, and interventions can be tracked and evaluated over time.
Over the long term, this supports a shift toward predictive maintenance, where historical trends and continuous measurements are used to anticipate future deterioration.
Connected vehicle data transforms the road network into a continuously monitored system, where real-world traffic provides a constant feedback loop. The result is not just faster detection, but a smarter, more resilient, and more sustainable approach to road maintenance.