For decades, road authorities have relied on the same fundamental approach to understanding the condition of their road networks: send a specialist vehicle out, measure what it finds, and use that data to plan maintenance. The methods have evolved — from manual walkthroughs to laser profilometers — but the underlying logic has remained unchanged. Monitoring happens periodically, covers a fraction of the network at any one time, and produces data that is already ageing by the time it informs a decision.
Connected vehicle technology is changing that logic entirely. By drawing on the sensors already embedded in millions of passenger cars, road authorities now have access to continuous, network-wide road condition data that would have been economically impossible to collect through traditional means. Understanding where traditional methods fall short — and where connected vehicle data fills those gaps — is essential for any organisation serious about modernising its road asset management strategy.
Traditional road condition monitoring encompasses several well-established methods, each with genuine strengths and well-documented limitations.
Manual visual inspection is the oldest and most widely used approach. Trained inspectors travel roads on foot or by vehicle, recording distress types, severities, and extents according to standardised protocols. Visual inspection is flexible, relatively inexpensive per kilometre, and can capture a wide range of surface distress types — cracking patterns, surface texture loss, edge deterioration — that automated sensors may miss. Its weakness is consistency: human raters introduce variance, and the subjectivity of visual assessment makes it difficult to compare data collected by different inspectors or across different seasons. More fundamentally, it does not scale. Inspecting an entire road network manually, at meaningful frequency, is simply not economically feasible for most road authorities.
High-speed inertial profilers address the objectivity problem by replacing human judgement with laser and accelerometer measurements. A profiler vehicle travels at normal traffic speed, measuring the longitudinal road profile and computing the International Roughness Index (IRI) — a standardised metric expressing vertical displacement per unit of distance, typically in metres per kilometre or inches per mile. IRI is widely used in pavement management systems and provides a consistent, comparable measure of ride quality. The limitation is cost and frequency: profiler surveys are expensive to operate, require specialist vehicles and operators, and are typically conducted once or twice per year on major roads. Many secondary and local roads are surveyed even less frequently, if at all. A road that begins deteriorating in January may not be flagged until the following year's survey cycle.
Ground-penetrating radar and other structural assessment tools provide deeper insight into subsurface conditions — identifying voids, delamination, and base layer deterioration that surface measurements cannot detect. These methods are valuable for structural assessment but are even more resource-intensive than surface profiling, limiting their use to targeted investigations rather than network-wide monitoring.
The common thread across all traditional methods is the fundamental tension between coverage and frequency. Increasing one typically means reducing the other. A road authority that surveys its entire network annually is making a trade-off: it knows the condition of every road once a year, but it knows the condition of no road on any given day.
Connected vehicles — passenger cars equipped with onboard sensors that transmit anonymised driving data — offer a fundamentally different model. Rather than deploying specialist measurement vehicles on a schedule, road condition data is collected continuously by the vehicles already travelling the roads as part of normal traffic.
Road Health product is built on this principle. Using high-frequency signals from vehicle suspension systems, inertial measurement units (IMUs), wheel speed sensors, and GPS, NIRA's in-vehicle software — embedded directly in vehicles from partner manufacturers — computes road roughness, friction, pothole events, and surface anomalies at 25-metre segment resolution. Data is aggregated across multiple vehicle passes, updated daily, and delivered as network-wide road condition intelligence.
The scale of this approach is what makes it transformative. A recent study from Purdue University analysing connected vehicle IRI data across Marion County, Indiana — a network of over 8,300 miles of roadway — found that connected vehicle coverage was nearly complete for arterial and collector roads, reaching 93–100% of the network within a single month of data collection. For a road authority that previously surveyed its network annually with a single profiler vehicle, this represents a step change in both coverage and timeliness.
The continuous nature of the data changes what is possible in maintenance planning. Traditional survey data tells you what a road looked like on the day the survey vehicle passed. Connected vehicle data tells you what is happening to the road now — and, critically, how it is changing over time. A road that shows stable IRI readings for six months and then begins deteriorating rapidly is a very different maintenance priority from a road with consistently high IRI. That distinction is invisible to annual survey data but immediately apparent in a daily time series.
It is worth being precise about what connected vehicle data does not replace. Manual inspection and profiler surveys capture information that vehicle sensors cannot — visible cracking patterns, edge deterioration, surface texture loss, and subsurface structural conditions. PCI and IRI measure different dimensions of pavement condition, and research consistently shows that the correlation between the two metrics is moderate rather than strong, particularly across different road types and deterioration stages. Connected vehicle IRI data is best understood as a complementary layer that dramatically improves the frequency and coverage of condition monitoring, not as a wholesale replacement for periodic structural assessment.
The practical implications of shifting from periodic surveys to continuous connected vehicle monitoring are significant across the full maintenance workflow.
Early detection becomes the norm. With daily IRI updates, a sudden increase in roughness — caused by a new pothole, frost damage, or accelerating structural deterioration — is detectable within days rather than months. Road authorities that previously learned about pothole clusters through public complaints or annual surveys can now identify emerging problems before they become visible to road users or cause vehicle damage.
Maintenance prioritisation becomes data-driven. Rather than relying on inspection schedules and engineer judgement to decide which roads to treat first, maintenance teams can rank roads by current condition, deterioration rate, and remaining pavement life — all derived from continuously measured data. Roads with accelerating deterioration receive attention before they reach the point of costly structural failure; roads with stable condition can be deferred without risk.
Repair verification becomes objective. One of the persistent challenges in road maintenance contracting is verifying that repairs have actually improved road condition. Connected vehicle data provides an objective before-and-after comparison: IRI readings in the weeks following a repair confirm whether roughness has returned to baseline, providing an independent performance record that supports both contractor accountability and budget justification.
Budget conversations change. Road authorities increasingly need to demonstrate the value of maintenance investment to elected officials and the public. Continuous, objective road condition data — showing deterioration trends, repair outcomes, and network-wide condition scores — provides the evidence base for those conversations in a way that periodic survey data, by its nature, cannot.
None of this means that inertial profilers or visual inspection are obsolete. The most effective road asset management strategies will combine the continuous coverage and timeliness of connected vehicle data with the structural depth of periodic surveys. Connected vehicle IRI data is well suited to identifying where conditions are changing, prioritising which roads need closer inspection, and verifying repair outcomes. Profiler surveys and visual inspection remain the appropriate tools for structural assessment, distress classification, and the kind of detailed pavement condition evaluation that informs major rehabilitation decisions.
The shift is one of emphasis. Traditional methods move from being the primary source of network condition data to being targeted, high-value investigations triggered by the patterns that continuous monitoring reveals. The result is a more efficient use of specialist survey resources — deployed where the data says they are needed, rather than on a fixed schedule that treats every road as equally uncertain.