Annual Local Authority Road Surveys (ALARS) and How AI Can Transform Reporting

Introduction

The Annual Local Authority Road Surveys (ALARS) form a vital part of road asset management in the UK. Commissioned by the Asphalt Industry Alliance and adopted by local highway teams, ALARS provides authoritative insights into carriageway condition, backlog, structural resilience, and maintenance funding.

These surveys play a critical role in shaping council road condition reports, budgeting for maintenance, and ensuring compliance with Department for Transport (DfT) highways standards.

However, manual inspections—relying on spot checks and visual assessments—are time-consuming, subjective, and often inconsistent. The emergence of digital highway audits using AI inspections platforms like RoadVision AI can revolutionise the ALARS process, bringing unprecedented accuracy, cost savings, and operational efficiency.

Understanding ALARS and the DfT Framework

What is ALARS?

ALARS is rooted in the ALARM survey, which captures data from around 80% of local authorities across England and Wales annually. It reveals urgent challenges such as:

• A £16.3 billion backlog in local carriageway repair — a 30-year high
• Average resurfacing intervals exceeding 90 years
• Structural life of over half the network under 15 years

The DfT uses ALARS data and official road condition statistics to shape maintenance policy and funding decisions.

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Why Councils Must Improve ALARS Reporting?

These findings highlight the need for proactive, asset-based maintenance over reactive patching. Councils under budget constraints must deliver accurate and timely council road condition reports to:

• Secure central funding
• Ensure public accountability
• Manage legal and safety risks
• Justify strategic investments

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Challenges of Current Manual ALARS Procedures

Manual methods typically suffer from:

• Limited coverage, missing large portions of the network
• Inconsistent data due to observer bias and subjectivity
• Delayed reporting cycles caused by data processing lags
• Weak audit trails that struggle under compliance scrutiny

These limitations compromise data quality submitted to the DfT, risking underfunding or delayed interventions.

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Transforming ALARS with AI-Powered Digital Highway Audits

RoadVision AI provides an advanced alternative that automates and enhances ALARS reporting across the entire road network.

How It Works

• AI-equipped vehicles collect continuous high-resolution video and sensor data
• Computer vision identifies cracks, potholes, skid resistance, and surface degradation
• Reports are generated with visual, geo-tagged, and timestamped evidence
• Data is structured directly in DfT-compliant formats

This creates a comprehensive, auditable, and standardised approach to ALARS submissions.

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Key Benefits for Councils and DfT

Comprehensive Coverage
Full-network scanning eliminates the gaps left by sample-based surveys.

Objective, Repeatable Data
AI models ensure consistent and error-free assessments for better reliability.

Rapid Turnaround
Automated reporting shortens the time from inspection to insight.

Cost Efficiency
The platform works with existing municipal vehicles, eliminating the need for expensive manual teams.

Audit-Ready Submissions
Every defect is backed by visual logs, simplifying audits and strengthening data credibility.

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Integrated Road Asset Management Platform

ALARS is just one part of the wider digital toolkit offered by RoadVision AI, including:

• Pavement Condition Surveys – Assess structural integrity and degradation patterns
• Road Safety Audits – Align with GG 119 and national safety frameworks
• Road Inventory Inspections – Log signs, furniture, lighting, and markings
• Traffic Surveys – Capture vehicle counts, usage patterns, and congestion points

These modules work together on a RoadVision AI dashboard for unified AI-based road asset management.

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Driving Sustainability and Smarter Roads

Digitising ALARS contributes to predictive maintenance and net-zero goals. Councils reduce over-repair and emissions while extending pavement life. This supports the UK’s long-term environmental and infrastructure resilience strategies.
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Case Studies in Action

Explore how forward-thinking councils across the UK are leveraging RoadVision AI to digitise ALARS, reduce costs, and improve road network performance. Visit our blog to read more.

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Conclusion

The shift from manual to AI-powered Local Authority Road Surveys is a transformational leap for UK road infrastructure. Councils adopting RoadVision AI for ALARS gain deeper insights, faster delivery, and greater audit confidence—ensuring roads remain safe, well-funded, and future-ready.

RoadVision AI is leading innovation in AI in road maintenance, providing a smart, automated solution for managing road networks. It conducts detailed traffic surveys and generates high-quality road data for early detection of issues such as surface cracks and the need for potholes repair. This technology-driven platform brings the power of AI in road planning and monitoring to enhance road safety. Fully compliant with IRC Codes and aligned with UK Highways Agency standards, RoadVision AI supports infrastructure planning that meets the needs of modern UK road networks.

Book a demo with RoadVision AI today and discover how your council can optimise DfT reporting, funding, and safety.

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FAQs

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Q1. What is ALARS, and how does it impact road maintenance?

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ALARS gathers local road condition data used to inform DfT funding and shape council-level highway maintenance strategies.

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Q2. Can AI-driven surveys replace SCANNER and visual inspections?

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Yes. RoadVision AI provides detailed, consistent, and network-wide coverage that matches or exceeds traditional SCANNER methods.

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Q3. How does this help councils meet audit requirements?

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Each inspection is logged with time, location, and image-based proof, streamlining audits and strengthening data credibility.

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