How Can AI Optimise Functional Road Classification in Qatar’s Growing Cities?

As Qatar’s urban centres like Doha rapidly expand, road networks are under increasing pressure to handle rising traffic demand, support economic hubs, and ensure public safety. Functional road classification, which groups roads based on their purpose and hierarchy, is a foundational element of Qatar’s Road Planning Guide (RPGQ) and the Qatar Highway Design Manual (QHDM).

However, traditional classification methods often rely on static data, which cannot keep pace with fast-changing urban patterns. This is where AI-powered technologies such as AI-based pavement monitoring, AI traffic modelling Doha, and digital road monitoring systems are transforming how road planners, engineers, and authorities classify and manage roads.

This blog explores how AI can optimise functional classification of Qatar roads, ensuring they are safe, efficient, and resilient while aligning with the country’s long-term vision.

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Understanding Functional Road Classification in Qatar

The Qatar Road Planning Guide emphasises that each road corridor must be designed according to its functional role in the network. Roads are broadly categorised into:

• Freeways and Expressways for high-speed regional movement
• Arterial and Collector roads for connecting major centres
• Local roads for property access within neighbourhoods

Functional classification balances two objectives: mobility (speed and capacity) and access (connectivity to properties and businesses).

As cities grow denser, many corridors in Doha now serve multiple roles simultaneously, carrying commuter traffic, freight, and local access. This complexity makes manual classification challenging and often outdated by the time it is implemented.

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Why Qatar Needs AI for Functional Classification?

Rapid population and economic growth has intensified congestion on roads like Salwa Road, Al Rayyan Road, C-Ring Road, and the Doha Expressway. The RPGQ highlights several challenges that AI can directly address:

• Lack of alternative transport choices
• Unreliable access to key centres
• Low compliance with transportation policies
• Achieving sustainability goals under the Qatar National Vision 2030

By applying AI-based road survey tools, authorities can collect continuous, accurate data on road usage, speeds, and conditions. This helps update classifications dynamically based on real-world performance instead of outdated plans.

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How AI Optimises Road Classification in Practice?

1. Real-time Traffic and Pavement Data Integration

Using AI-based pavement monitoring systems, authorities can measure pavement health, surface distress, and rutting levels. When combined with traffic counts from AI traffic modelling Doha tools, planners can identify which corridors are over-utilised and misclassified.

2. Automated Corridor Function Assessment

Machine learning models can cluster road segments based on average speeds, congestion patterns, and land-use context. These digital road monitoring systems can automatically flag when a collector road is functioning like an arterial and needs reclassification or upgrades.

3. Supporting Multimodal Prioritisation

The QHDM encourages integrating public transport, walking, and cycling into major corridors. AI can simulate the impact of converting general lanes into bus lanes or shared paths, ensuring road function aligns with Qatar’s sustainability objectives.

4. Continuous Compliance Verification

AI systems can perform road safety audits on classified corridors to ensure they meet QHDM safety standards. This creates a feedback loop that maintains safety as traffic patterns evolve.

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Benefits for Road Asset Management in Qatar

Integrating AI into functional classification directly enhances road asset management Qatar strategies:

• Optimised lifecycle planning based on real performance
• Reduced maintenance costs by targeting critical corridors first
• Better coordination between Ashghal, MOTC, and MME
• Enhanced resilience to future urban growth and autonomous vehicle adoption

As the best AI road asset management company in Qatar, RoadVision AI delivers unified platforms that merge traffic analytics, pavement surveys, and safety audits into a single decision-support system for planners and municipalities.

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Future-Ready Classification with AI

AI will enable Qatar to move from static, one-time classification to dynamic network planning. Roads will be classified not just on design intent but on actual performance and predicted future demand. This aligns perfectly with the Transportation Master Plan for Qatar (TMPQ), which aims to create an integrated, sustainable road network by 2050.

Municipalities and planners can use AI-based road survey tools to continuously track corridor performance and automatically adjust classifications as urban growth, land use, and mobility patterns shift.

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Conclusion

Accurate functional road classification is critical to building safe, efficient, and sustainable transport networks in Qatar’s fast-growing cities. With AI, this process becomes data-driven, adaptive, and aligned with the QHDM and TMPQ guidelines.

As Qatar invests in smarter infrastructure, leveraging AI-powered digital road monitoring systems will ensure every corridor is performing its intended role, supporting economic growth and public safety.

With RoadVision AI, the future of road maintenance is smart, automated, and data-driven. Its intelligent platform conducts traffic surveys, generates precise road data, and detects issues such as cracks or the need for pothole repair before they become critical. Backed by IRC compliance and Qatar’s road standards, it powers modern road networks with safety and efficiency at the core.

Book a demo with us to explore how RoadVision AI can transform functional classification and road asset management in Qatar.

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FAQs

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Q1: What is functional road classification in Qatar?

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Functional classification groups roads based on their role in mobility and access, as outlined in the Qatar Highway Design Manual.

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Q2: How does AI help classify roads more accurately?

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AI uses traffic and pavement data to dynamically adjust road classifications and identify misaligned corridors.

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Q3: Can AI-based tools reduce congestion in Doha?

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Yes, by identifying overloaded corridors and rebalancing network functions, AI can reduce congestion and improve road safety.

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