Local councils and highway authorities are under pressure to keep the road network safe and smooth, but the very tools they rely on often fail to give a clear picture. Traditional road surveys and consumer smartphone apps can leave big gaps in road monitoring coverage, data quality, and even inspector safety. In practice, this means small defects go unnoticed until they become big problems, and engineers end up racing to catch up.
National polls show why this matters: 59% of people report dissatisfaction with road repairs, and residents consistently name potholes as a top local concern. Yet official statistics tell a rosier story (“broadly stable” road conditions) that conflicts with what drivers see every day.
Across England’s road network, conditions continue to worsen despite record investment. The Asphalt Industry Alliance’s 2024 survey warns that “over half of the local road network has 15 years or less of structural life remaining,” and a record £16.3 billion is now needed to tackle the backlog of repairs.
In this article, we unpack why legacy road monitoring methods — from specialist vehicles to smartphone checks — struggle to keep pace with today’s demands, and what a more effective, network-wide approach looks like in practice.
Traditional Surveys: Slow, Costly, and Incomplete
Many councils still rely on infrequent, specialist surveys to measure road condition. In the UK, this often meant the SCANNER van scanning classified roads or engineers doing foot patrols. These methods can be accurate on the ground, but they cannot cover every street regularly.
In fact, coverage has already declined: one industry report notes that use of mandated SCANNER surveys on A-roads dropped from about 99% in 2018 to only 81% in 2024. Meanwhile, minor and unclassified roads are largely unsurveyed unless flagged by complaints or accident reports. In practical terms, this means many stretches of highway see no formal inspection between budget cycles or citizen complaints.
Even when these surveys occur, they are typically sporadic. An annual or biennial SCANNER run creates just a single data “snapshot” each year. Any pavement damage emerging after that can go undetected for months. And because a special vehicle is needed, surveys are expensive (each run involves machinery, trained staff, and sometimes lane closures). The result is data that is often stale and sparse. Highway managers receive condition scores every year or two, but have little visibility in between. New cracks, developing potholes or skid-resistance issues may only be discovered when a resident spots them, by which time the defects have grown. In short, coverage and frequency fall short of the continuous monitoring that busy road networks demand.
On-the-ground inspections also carry practical constraints:
--> An inspector walking or driving slowly down a busy A-road must use cones or traffic control – adding time and, importantly, risk.
--> Foot patrols on busy carriageways pose safety hazards: engineers can end up standing by live traffic to note defects.
--> One study of an AI-based monitoring trial found that replacing manual site inspections with digital scanning halved the time needed per inspection and...
“...improved safety for inspectors by reducing their time near moving traffic.”
In contrast, traditional methods force either road closures or roadside stops, which slow down data collection and divert resources to traffic management instead of repairs.
Smartphone Based Monitoring: Indicative, but Limited at Scale?
Smartphones have increasingly been explored as a lower cost way to capture road condition data. In theory, using devices already carried by inspectors (or even the public) seems appealing, and some smartphone based systems can deliver indicative results. PAS 2161 recognises this, allowing mobile phones to be used under certain conditions. In practice, however, smartphone led monitoring comes with limitations when applied across a full highway network.
While powerful, smartphones are consumer devices, not infrastructure grade sensors. Their IMUs (Inertial Measurement Units) and cameras vary significantly by model and manufacturer, and results are highly sensitive to how and where the phone is mounted. A device fixed to a windscreen, placed in a cradle or resting in a cup holder will likely record the same road very differently. Vehicle type, suspension, speed and driving style can also affect the reliability of the data captured, making consistency difficult to guarantee.
Cameras present similar constraints. While smartphone cameras are sophisticated they are not purposefully designed for defect detection at speed: mounting angles are inconsistent, depth perception is limited, and image quality can degrade quickly in low light or poor weather; an important consideration when it comes to data quality. While defects may be captured, the data is unlikely to be consistently uniform enough for reliable comparison over time.
Operationally, phone based approaches also introduce friction. Why? Data capture is not automatic. Inspectors typically need to mount the device, start the app, and later upload the data manually. In busy operational environments, this reduces consistency and coverage. How? If the phone is not switched on or correctly set up, that journey produces no usable data.
As a result, smartphone data often reflects where inspections are already taking place, rather than delivering continuous, network-wide visibility. Streets not routinely visited can still remain unmonitored, and repeat coverage can be uneven or even unreliable.
For highway managers, this has practical consequences. Indicative data can support local insight, but it is harder to rely on for prioritisation, budgeting or audit-ready decision making. As networks age and expectations around evidence increase, the limits of manual, phone-based monitoring become more apparent.
The PAS 2161 Imperative: Raising the Bar on Data
Aware of these issues, the UK government has shifted to enforce higher data standards. In January 2024, the BSI introduced PAS 2161:2024, the new road-condition monitoring specification for England. It is explicitly designed to fix longstanding blind spots by requiring more complete, validated data. Notably, PAS 2161 is technology-neutral: it does not prescribe a single vehicle or device for data collection. This differs from the historical processes (where the SCANNER van was effectively mandated). Instead, PAS 2161 sets performance and coverage rules. It defines how much of each road class must be surveyed, how often, and in what standardised format. It also enforces rigorous quality checks, metadata requirements, and data schemas.
For highway managers, this means the choice is now about meeting these targets, not sticking with a legacy vendor. Councils can choose any method that meets PAS 2161’s benchmarks. In effect, the government is forcing the issue: partial snapshots and unverified smartphone scans will no longer cut it. Early trials of PAS 2161-compliant systems have shown that consistent data collection saves money by avoiding duplicate surveys and enabling risk-based maintenance. And crucially, having standard condition categories (“red/amber/green” and 1–5 scales) means datasets from different councils become comparable.
PAS 2161 incentivises continuous, data-rich approaches. To keep pace with the new rules, highways teams will need automated tools that produce reliable, audited data — and do so with minimal human intervention.
AI and Fleet-Based Monitoring: A New Approach
Fortunately, new technologies are stepping into that role. AI-driven camera systems mounted on everyday vehicles are emerging as a practical, robust and reliable solution. Instead of one-off surveys, these systems run continuously: a camera rig and processing unit (often using computer-vision software) are installed in vehicles such as highway patrol cars, bin trucks, or gritter lorries. As those vehicles go about normal routes, the device quietly scans all lanes of road. It captures high-definition imagery and on-board analysis flags defects in real time.
This fleet-based model has several advantages...
1. First, every mile becomes an inspection.
A routine patrol or waste-collection run can capture data for the roads it travels – vastly increasing coverage compared to a few specialist runs. Councils need not schedule separate survey trips; the system piggybacks on existing routes. In fact, National Highways has noted that such video-analytics devices can be fitted to any vehicle to automatically detect hazards. In effect, a local road that hadn’t been formally surveyed in years can now be scanned whenever a camera-equipped vehicle passes.
2. Second, these AI systems harvest rich data beyond simple condition grades.
Modern solutions detect various asset types (potholes, cracks, faded markings, street signs, barriers, etc.) and precisely geo-tag them. For example, if the camera spots a pothole, the system records its size, exact location (often to centimetre accuracy using GPS), and an image. This raw evidence feeds into a database continuously. It is far more detailed than a “good/bad” rating. Over time, engineers can track how a particular defect is growing, or compare before/after repair images. Importantly, all this data is timestamped and traceable, which means decisions can be audited later.
3. Third, embedding the technology into vehicles cuts exposure to risk.
Because the camera does the observing, staff no longer need to step onto live roads to log defects. One road manager reported that when such a system was trialed, “inspectors no longer need to stop in traffic or step onto the road to log issues” – making the process much safer and letting crews focus on repairs instead. In real terms, one digital monitoring trial saved almost 50% of inspection time while simultaneously reducing inspector time near moving traffic. This frees up hundreds of hours annually, time that can be spent on maintenance rather than on the roadsides.
4. Finally, these AI-camera platforms are designed for integration.
The output is typically in standard GIS or asset-management formats, so it can flow into existing work management systems. When a defect is detected, an alert can automatically create a work order in the council’s maintenance system. This closes the loop end-to-end: from detection to response. In contrast to siloed spreadsheets or paper notes, managers get a unified, up to date, view of network health. Some early adopters report being able to answer citizen queries quickly by simply pulling up the latest street image showing a reported defect.
In short, AI-enabled monitoring turns the road network into its own continuous sensor. Instead of human surveys or sporadic apps, the council vehicle fleet itself becomes the data collection backbone – scanning every street autonomously it drives.
Meeting the Standards: Practical Benefits
What does this look like on the ground? In recent UK trials and deployments, advanced monitoring systems have shown tangible gains:
Broader Coverage and Frequency.
- Because every vehicle turn can collect data, councils can achieve much higher mileage coverage without extra crews. Areas that previously lay fallow between annual surveys are now scanned monthly or even weekly. PAS 2161 requires high coverage levels, and these platforms make compliance practical. National Highways trialed AI-video monitoring devices for exactly this reason.
Actionable Data, Not Just Images
- The raw data comes pre-processed. For example, an Essex County trial found that the AI system rapidly flagged and geolocated dozens of defects in a single run. Each defect record had measured dimensions, GPS coordinates, and a photo. This turned ad-hoc observations into a searchable database. Engineers can then prioritise by severity or location, rather than keeping mental notes or paper lists.
Safety Inspections without the Safety Risk
- These systems handle what might have been done by a kit inspection team on foot. One city that integrated its safety defect inspection into the AI scanning workflow no longer needed inspectors to walk along busy roads at all. High-risk tasks could be handled remotely, reducing the need for traffic management, all while protecting staff.
Real-Time Alerts and Reporting
- Because data is uploaded immediately, councils can react fast. If a camera detects a hazard (i.e. a new deep pothole on a major bus route), the team sees that via a dashboard minutes after driving by. This beats waiting days or weeks for the next scheduled survey.
Tangible Efficiency Gains
- Perhaps most compellingly for leaders watching budgets, AI scanning has demonstrated dramatic efficiency improvements. Using this technology can identify defects four times faster than a manual road inspection. In practice, councils using these systems have been able to complete their monitoring work in a fraction of the time, freeing crews to focus on repairs. This means doing more with the same or less staff time — a crucial advantage when each pound of maintenance budget must stretch as far as possible.
All the above goes directly to the heart of a highway manager’s real concerns and challenges. By filling in the “blind spots” of the network, these technologies provide the comprehensive, up-to-date data that PAS 2161 (and common sense) now require. They replace reactive maintenance (fixing only the very visible problems) with a proactive strategy: small defects get tracked and treated before they become dangerous or costly. And with auto-generated images and metrics, councils can report clearly to stakeholders and even defend decisions in case of insurance claims (because the digital record is timestamped).
Conclusion: Embracing Smarter Monitoring
The bottom line is that the legacy method of road monitoring is no longer sufficient. Traditional surveys and smartphone apps simply can’t keep pace with modern network demands. They either miss things or generate data that isn’t as reliable as it should be. Highway teams must now move to more reliable, frequent, and standardised data collection.
In practice, this means adopting continuous scanning methods. Councils that trialled these systems have seen inspection times cut drastically and staff kept safe off the live carriageway. Ultimately, engineers gain a truly up-to-date map of road defects, allowing precise prioritisation and preventive action.
Route Reports has emerged as a practical answer to many of the challenges facing highway managers today. By moving beyond periodic surveys and phone-based reporting, Route Reports equips everyday council vehicles with high-definition cameras and dedicated AI processors, enabling continuous, automated monitoring across the road network.
As councils who have deployed Route Reports have found, every minute of driving generates fresh condition data – potholes and cracks are detected and geo-tagged instantly. This data is then uploaded live to a cloud dashboard, so managers see issues almost as soon as they appear. Route Reports is proud to deliver defect surveys up to four times faster than manual inspections, while integrating seamlessly with asset-management software.
For UK highway decision makers, tools like this mean a real step-change. Instead of chasing problems that road users spot, councils get ahead of issues with hard data. Roads become safer, public satisfaction can improve (since fixes are faster and better-targeted), and precious funding is used more efficiently.
Work with us
Want to find out more about Route Reports and see it in action?
Contact us today to arrange a one to one with one of our team, or view the live demo now.
Further reading
- Local Government Association: Improving highways maintenance productivity. A councillor's guide to technology and leading practice on road treatments.
- Highways Today: Pioneering the Future of Road Condition Monitoring with PAS 2161:2024
- Department for Transport: Official Statistics: Road conditions in England to March 2024
- House of Commons Public Accounts Committee Condition and maintenance of Local Roads in England
