India’s transportation networks are evolving rapidly, and modern cities require precise, data-rich systems to manage traffic growth efficiently. While IRC 65 provides the foundational framework for traffic engineering in India, today’s mobility challenges demand far more accurate and continuous insights than traditional manual surveys can deliver.

With the rise of AI-based traffic surveys, cities and road authorities are now equipped to gather real-time, high-resolution traffic data using automated digital tools. Solutions such as road asset management India platforms, computer vision–powered data extraction, and continuous cloud-based analytics are redefining how traffic flow standards are applied in real-world conditions.

From the very beginning, modern AI ecosystems like digital traffic management, AI-based road safety audit, automated road inventory inspection and pavement monitoring tools help authorities collect holistic, actionable traffic information that aligns seamlessly with IRC design and operational guidelines.

This blog offers a detailed look at how AI supports, strengthens, and modernizes IRC 65 Traffic Engineering Standards for India’s next generation of roads.

Understanding the Role of IRC:65 in India’s Traffic Engineering Framework

IRC:65 forms the backbone of India’s traffic capacity estimation methodologies. It lays down:

1. Definitions of traffic flow, vehicle categories, and speed–flow relationships.
2. PCU (Passenger Car Unit) values for converting heterogeneous traffic into a standardised measure.
3. Capacity values for different road classes, including single-lane, two-lane, multi-lane, and divided highways.
4. Guidelines for road design, width recommendations, level-of-service criteria, and operating capacity under varying environmental and roadway conditions.
5. Methods for conducting traffic volume counts, classified counts, speed and delay studies, and peak-hour factor calculations.

While IRC:65 provides the structure, traditional data collection methodologies—mostly manual—are slow, inconsistent, and error-prone, especially when applied in high-density Indian traffic.

This is where modern AI-based traffic surveys become transformative.

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How AI-Based Traffic Surveys Modernize and Strengthen IRC:65 Standards?

AI-powered traffic data collection aligns with IRC:65 requirements while dramatically improving the speed, accuracy, granularity, and reliability of engineering inputs.

Below are the major improvements, explained in rich detail.

1. Fully Automated Traffic Volume Counts with High Accuracy

IRC:65 mandates precise classified traffic counts. Traditional manual enumeration has several constraints:

1. Short-duration sampling leads to inaccurate projections.
2. Human observers struggle in mixed, high-speed, or nighttime conditions.
3. Vehicle misclassification is extremely common.
4. Data consistency varies across teams and regions.

AI solves these challenges by using camera-based or mobile-based detection systems that operate continuously and classify vehicles with high precision, improving compliance with traffic volume count methodologies recommended by IRC.

Platforms like automated traffic surveys offer:

1. Automatic classification across all IRC-prescribed categories (cars, buses, autos, 2Ws, LCVs, HCVs etc.).
2. Continuous data capture across hours, days, or weeks to remove sampling bias.
3. High-speed recognition even in heavy or mixed traffic scenarios.
4. Real-time datasets that instantly feed into design inputs.

This increases the reliability of traffic demand estimation dramatically.

2. Converting Real-Time Traffic into Precise PCU Values

PCU conversion is one of the most critical components of IRC:65 because India’s traffic is heterogeneous.

AI enhances PCU estimation because:

1. It records vehicle dimensions and behaviour in real time.
2. It can adjust PCU factors dynamically based on peak-hour congestion.
3. It produces time-resolved PCU profiles instead of one static PCU multiplier.
4. It creates directional PCU distribution critical for design and widening proposals.

This gives engineers a more accurate representation of actual roadway behaviour.

3. AI Enhances Speed–Flow Analysis for IRC:65 Capacity Standards

Speed–flow curves defined in IRC:65 depend on observed speeds under different flow conditions. AI systems provide:

1. Continuous speed extraction from video feeds.
2. Speed distribution curves for each vehicle category.
3. Identification of congestion buildup thresholds.
4. Real-time operating capacity estimation.

The result is a significantly improved ability to calibrate IRC-based speed–flow relationships using real urban conditions.

4. Predictive Traffic Congestion Modelling for Better Urban Mobility Planning

IRC:65 defines capacity thresholds but does not offer predictive modelling.

AI tools fill this gap by forecasting congestion based on:

1. Historical traffic patterns.
2. Weather and seasonal variables.
3. Peak-hour demand variations.
4. Road geometry, bottlenecks, and signal delays.
5. Pedestrian and multi-modal interactions.

This allows city authorities to anticipate failures instead of simply reacting to them.

5. Integration with Road Condition Databases and Digital Road Inventories

Traffic engineering does not exist in isolation.

AI systems integrate with:

• road inventory inspection
• pavement condition surveys
• road safety audits

to create a holistic view of road performance.

With integrated datasets, engineers can identify:

1. Roads where capacity is limited due to geometry deficiencies.
2. Areas with poor pavement performance reducing effective speed.
3. Sections needing redesign, widening, or operational improvements.
4. Conflict zones needing turn pockets, median openings, or channelisation.

This transforms IRC:65 guidelines from static values into dynamic, actionable insights.

6. High-Fidelity Traffic Visualisation for Engineering Decision Making

AI tools generate visual dashboards showing:

1. Classified traffic flow trends.
2. Peak-hour movement patterns.
3. Congestion hotspots with heat maps.
4. Turning movement counts at intersections.
5. Queue lengths and saturation flow measurements.

Such visual outputs help engineers directly apply IRC:65 recommendations in the field more confidently.

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Why India Needs AI Traffic Surveys to Keep IRC:65 Relevant for the Future?

India’s transport ecosystem is becoming more complex. Rapid urbanisation, increased motorisation, multimodal traffic, and infrastructure expansion require smarter tools.

AI traffic surveys help modernise IRC:65 norms by:

1. Increasing the accuracy of all foundational datasets.
2. Supporting real-time mobility planning.
3. Reducing survey cost and manpower dependency.
4. Ensuring standardisation across states and cities.
5. Enhancing long-term forecasting and policy planning.
6. Making capacity assessment more reflective of modern traffic patterns.

As a result, AI aligns IRC standards with global traffic engineering practices.

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Conclusion

AI-based traffic surveys represent a major leap forward for implementing and modernising IRC:65 Traffic Engineering Standards. Combining continuous data monitoring, automated classification, advanced analytics, and predictive capacity modelling, AI enables engineers to design safer, smarter, and more efficient road systems aligned with real-world Indian traffic behaviour.

RoadVision AI is transforming infrastructure development and maintenance by harnessing AI in roads to enhance safety and streamline road management. Using advanced roads AI technology, the platform enables early detection of potholes, cracks, and surface defects through precise pavement surveys, ensuring timely maintenance and optimal road conditions. Committed to building smarter, safer, and more sustainable roads, RoadVision AI aligns with IRC Codes, empowering engineers and stakeholders with data-driven insights that cut costs, reduce risks, and enhance the overall transportation experience.

To explore how AI can strengthen your traffic engineering practices or help align operations with IRC:65, you can book a demo with us.

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FAQs

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Q1. How does AI improve the accuracy of traffic volume counts under IRC:65?

AI uses automated camera-based vehicle detection to count and classify vehicles with high precision, eliminating human error and improving compliance with IRC guidelines.

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Q2. Can AI completely replace manual traffic surveys?

AI can replace most manual surveys while increasing frequency and accuracy. However, hybrid validation checks may still be used in certain locations.

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Q3. Does AI help in multi-modal traffic flow studies required by IRC?

Yes. AI detects pedestrians, cyclists, commercial vehicles, public transport, and mixed-flow conditions, giving richer insights for capacity and design.

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