Highway and Specialized Testing

ATCC Survey — Automatic Traffic Counter Classifier (Video-AI)

AI video-based vehicle counting and classification per IRC SP 19 — accepted by NHAI

NABL TC-14144 · ISO/IEC 17025:2017 · NHAI / PWD / BRO Approved · 24-72h Mobilisation
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IRC SP 19 IRC SP 84 IRC 37:2018
An ATCC (Automatic Traffic Counter Classifier) survey records and classifies every vehicle passing a highway section continuously for 7 days × 24 hours, sorted into the 17-class IRC SP 19 vehicle scheme. NKMPV runs ATCC surveys using video-based AI classification — faster and more accurate than manual counters or pneumatic tubes — and is NABL-accredited (TC-14144 under ISO/IEC 17025:2017) for ATCC reporting accepted by NHAI, BoT/HAM concessionaires, and state PWDs across India.

What Is an ATCC Survey?

Reviewed by NKMPV Technical Editorial Team, NABL Assessor, IS/IRC-Code Certified Engineers  ·  Last reviewed:

An ATCC survey provides classified, continuous, 24×7 traffic data that becomes the primary input for highway pavement design, capacity analysis, toll viability studies, and BoT/HAM concession monitoring. The classification scheme prescribed by IRC SP 19 (Manual for Survey, Investigation and Preparation of Road Projects) splits traffic into 17 categories — two-wheeler, three-wheeler, car/jeep/van, mini-bus, standard bus, LCV (passenger), LCV (goods), 2-axle truck, 3-axle truck, 4-axle MAV, 5-axle MAV, 6-axle MAV, 7+ axle MAV, tractor with one trailer, tractor with two trailers, animal-drawn vehicle, and bicycle — with separate counting for cycle-rickshaw and hand-cart in urban contexts. NKMPV's primary ATCC method is video-based AI classification. High-resolution roadside cameras record continuously for the full 7-day window; deep-learning models trained on Indian-fleet imagery process the footage to classify every vehicle into the 17 IRC SP 19 categories with timestamp, direction, lane, and speed. Classification accuracy benchmarked against manual ground-truth counts is consistently above 95% even at night and under monsoon conditions — substantially better than human counters (who typically achieve 80-88% over a fatigued 24-hour shift) and better than pneumatic-tube counters (which struggle with classification beyond axle count). The video record also serves as a permanent evidence trail, useful in arbitration disputes and in toll-revenue audits where the count itself can be contested. The processed data outputs Average Daily Traffic (ADT), Annual Average Daily Traffic (AADT) using IRC 37 seasonal correction factors, directional split, peak-hour factors, and — combined with our axle load survey data — Vehicle Damage Factor (VDF) per class and design traffic in cumulative Million Standard Axles (MSA) for the specified design life. These outputs feed directly into IRC 37 flexible pavement design, IRC 58 rigid pavement design, and the traffic-input section of every NHAI Detailed Project Report (DPR). NKMPV deploys ATCC surveys on national highways, state highways, expressways, urban arterials, and rural ODR/MDR roads across Haryana, Punjab, Himachal Pradesh, J&K, Uttarakhand, Rajasthan and beyond. We complement ATCC with Network Survey Vehicle (NSV) condition surveys and FWD structural testing to deliver a complete pavement evaluation package for highway DPRs, overlay design, and concession monitoring.

Survey Parameters & Output Data

The following parameters are recorded and computed from a 7-day continuous ATCC video-AI survey. Outputs are formatted to comply with IRC SP 19, IRC SP 84 (Manual for Concessioning), IRC 37, and NHAI DPR requirements.

Parameter Value / Range Unit Standard
Vehicle Classification Scheme 17-class per IRC SP 19 (2W, 3W, car/jeep/van, mini-bus, bus, LCV-pax, LCV-goods, 2-axle truck, 3-axle, 4/5/6/7+-axle MAV, tractor with 1/2 trailers, ADV, bicycle) categories IRC SP 19 Cl. 4.3 / Annexure 4-1
Survey Duration 7 consecutive days × 24 hours continuous (per IRC SP 19 / SP 84 minimum) days IRC SP 19 Cl. 4.2
Classification Accuracy (video-AI vs ground-truth) ≥ 95% (validated against 10% manual ground-truth sample per station) % Internal QA per IRC SP 19 validation protocol
Time Resolution (raw data) Per-vehicle event log with timestamp, direction, lane, speed; aggregated to 15-min and hourly bins minutes IRC SP 19 Cl. 4.4
Average Daily Traffic (ADT) Computed from 7-day total / 7 vehicles/day IRC SP 19 / IRC 37 Cl. 5.2
Annual Average Daily Traffic (AADT) ADT × seasonal correction factor (per IRC region) × day-of-week factor vehicles/day IRC 37 Cl. 5.2 / IRC 9
Vehicle Damage Factor (VDF) Computed per class from paired axle load survey, fourth-power law dimensionless IRC 37 Table 2 / IRC 81
Design Traffic (Cumulative MSA) 10/15/20/30-year cumulative standard axles, per IRC 37 formula msa IRC 37 Cl. 5.3
Directional Distribution Factor Computed per direction from 7-day data dimensionless IRC 37 Cl. 5.1
Peak Hour Factor AM peak / PM peak / overall peak (volume-to-capacity ratio per IRC 106) dimensionless IRC 106
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Applicable Indian Standards

IRC SP 19

Manual for Survey, Investigation and Preparation of Road Projects — defines the 17-class vehicle classification and ATCC survey methodology

IRC SP 84

Manual of Specifications and Standards for Six-Laning of National Highways — traffic data requirements for BoT/HAM concession projects

IRC 37:2018

Guidelines for the Design of Flexible Pavements — design traffic MSA computation methodology

IRC 9

Traffic Census on Non-Urban Roads — establishes seasonal correction factors and AADT estimation

IRC 106

Guidelines for Capacity of Urban Roads in Plain Areas — peak-hour volume and capacity analysis

MoRTH RW/NH-33044

MoRTH circulars on traffic data acceptance for NHAI DPRs and BoT/HAM concession monitoring

Equipment Used

Video-AI ATCC System (Primary)

High-resolution roadside camera + edge-compute classifier

Continuous 7-day recording at 30 fps; on-edge AI classifies into IRC SP 19 17 categories with timestamp, direction, lane, and speed; ≥ 95% accuracy benchmarked against manual ground-truth

Calibrated

AI Classification Software (NKMPV in-house)

Deep-learning vehicle classifier trained on Indian fleet imagery (auto-rickshaws, MAVs, tractor-trailers, ADVs)

Per-vehicle event log; 17-class scheme; handles night vision, monsoon, and partial-occlusion scenarios; validated against IRC SP 19 visual classifier reference set

Validated

IR Night-Vision Camera Module

Active IR illuminator with CMOS sensor

Maintains classification accuracy in zero-ambient-light conditions across the 18:00-06:00 window when classification is hardest for human counters

Calibrated

Pneumatic Tube ATCC (Backup)

Heavy-duty rubber tube + multi-channel data logger

Backup axle-count and speed measurement deployed when video sight-lines are obstructed; classification limited to axle-based scheme (5-6 categories) per IRC SP 19 Annexure

Calibrated

Manual Classified Count Team (Validation)

Trained surveyors with handheld tally counters

Conducts a minimum 10% ground-truth sample (typically 16 hours per 7-day station, distributed across day/night/weekend) for independent validation of AI classification accuracy per IRC SP 19 QA protocol

Trained per IRC SP 19

Power Supply & Data Storage

Solar + battery backup with 4G/LTE upload capability

Zero-power-failure 7-day continuous recording; raw video archived locally and to cloud; client receives full unedited footage for independent verification

Tested

Survey Process

1

Site Reconnaissance & Station Selection

1-2 days

The team visits the project corridor to identify ATCC station locations per IRC SP 19 spacing requirements (typically every 25-50 km on long corridors, plus traffic-influence-area stations near intersections, settlements, and agricultural belts). Each station is selected on a straight, level mid-block section away from intersections, bus stops, and toll plazas. GPS coordinates and lane configuration are recorded.

2

Camera Mounting & Sight-Line Validation

Half day

Cameras are mounted on roadside poles or temporary masts at the height and angle that gives unobstructed view of all lanes in both directions. The AI system runs a 30-minute calibration sweep — sample frames are reviewed for plate visibility, occlusion patterns, sun-glare windows, and headlight bloom. If sight-lines are compromised, a backup pneumatic-tube ATCC is laid as a redundant counter.

3

7-Day × 24-Hour Continuous Recording

7 days

Cameras record continuously for 7 consecutive days × 24 hours per IRC SP 19 minimum survey duration. The AI classifier runs in real-time on the edge device and uploads per-vehicle event logs to the cloud every 15 minutes. The field team conducts daily QA visits to verify camera integrity, IR illumination, power supply, and to download raw footage.

4

Manual Ground-Truth Validation Sample

Concurrent with Step 3

A trained classification team conducts manual counts for a stratified 10% sample (typically four 4-hour windows distributed across day/night/peak/weekend). AI-classified output is compared event-by-event against manual records; any class with < 95% match triggers re-training of the AI model on that class's frames before final classification is locked.

5

AI Classification Refinement & Final Output

2-3 days

After the 7-day recording closes, the full footage is re-processed in batch mode with the validated model. Any frames flagged low-confidence are visually verified by the analyst team. Output is the per-vehicle event log: timestamp, direction, lane, IRC SP 19 class, speed, and frame reference for audit trail.

6

Traffic Analysis & MSA Computation

2-3 days

ADT is computed from the 7-day total. AADT is derived using IRC 37 / IRC 9 seasonal correction factors. Combined with paired axle load survey data, Vehicle Damage Factor (VDF) is calculated per class using the fourth-power law. Design traffic in cumulative MSA is computed for the specified design life using N = 365 × A × D × F × [(1+r)^n − 1] / r per IRC 37 Cl. 5.3. Hourly distribution charts, peak-hour factors, and class-distribution pies are generated.

7

Report Compilation & Delivery

3-5 days

The final ATCC report includes: (1) classified hourly and daily volume tables for all 17 IRC classes, (2) ADT/AADT computation worksheets, (3) directional distribution and peak-hour analysis, (4) VDF and design-traffic MSA worksheets, (5) per-station class-distribution charts, (6) sample video frames per class as classification evidence, (7) raw per-vehicle event log in CSV, and (8) an executive summary formatted to NHAI DPR / BoT concession requirements. Reports are delivered with NABL accreditation reference and signed by NABL-recognised assessors.

Where ATCC Surveys Are Used

ATCC survey data is the foundation input for highway pavement design, traffic forecasting, toll-revenue projection, and BoT/HAM concession monitoring. NHAI requires ATCC surveys per IRC SP 19 and SP 84 for every Detailed Project Report (DPR). The data feeds directly into IRC 37 flexible pavement design and IRC 58 rigid pavement design as the design-traffic MSA input. For existing roads, ATCC combined with FWD structural deflection testing enables overlay design under IRC 115. Concessionaires running BoT/HAM toll roads use periodic ATCC surveys to demonstrate minimum revenue compliance and to support toll-rate revision applications. Urban planners use turning-movement counts derived from ATCC video footage for junction design, signal timing, and capacity analysis under IRC 106. NKMPV's video-AI ATCC method delivers three differentiators that matter in practice. First, accuracy: the AI classifier consistently exceeds 95% per-class accuracy against manual ground-truth, vs. 80-88% typical for fatigued human counters over a 24-hour shift. Second, evidence trail: every classified vehicle is linked to a video frame, so disputes during arbitration or toll-audit can be resolved by showing the actual footage. Third, scalability: AI processing handles parallel deployment of 5-10 stations across a corridor without proportionally increasing field staff, reducing total mobilisation time and cost vs. traditional manual or pneumatic deployments.

Further reading: ATCC vs Manual Traffic Count — Why Video-AI Wins on Accuracy, Speed and Evidence — head-to-head comparison of video-AI, pneumatic-tube and manual classified-count methods on Indian highway projects.

NHAI Detailed Project Report (DPR) preparation per IRC SP 19 / SP 84 Flexible and rigid pavement design traffic input per IRC 37 / IRC 58 BoT and HAM concession agreement traffic compliance monitoring Toll-rate revision applications under NH Fee Rules Overlay design for existing pavements (combined with FWD) Traffic Impact Assessment for urban development and SEZ projects Junction and intersection design (turning movement counts from video) Highway feasibility studies and capacity analysis Annual traffic monitoring for state PWD asset management Toll-revenue audit and arbitration evidence trail

Detailed Information

NKMPV provides video-AI ATCC (Automatic Traffic Counter Classifier) survey services across India for highway, expressway, and urban infrastructure projects. Our surveys deliver classified, continuous, 7-day × 24-hour traffic data per IRC SP 19, accepted by NHAI, NHIDCL, state PWDs, and BoT/HAM concessionaires.

NKMPV's ATCC method uses high-resolution roadside cameras with edge-deployed deep-learning classifiers — faster than manual counts and substantially more accurate than pneumatic-tube counters. Every classified vehicle is linked to a video frame for permanent evidence trail.

Why Video-AI ATCC Beats Manual and Pneumatic-Tube Methods

Accuracy. Our AI classifier consistently delivers ≥ 95% per-class accuracy against manual ground-truth, validated on a 10% stratified sample per station. Human counters typically drop to 80-88% accuracy over a 24-hour fatigue cycle. Pneumatic-tube ATCCs can count axles reliably but cannot visually distinguish vehicle types (e.g., 2-axle truck vs. mini-bus vs. LCV-goods).

Speed. AI processes the entire 7-day record in 2-3 days. Manual transcription of an equivalent volume takes weeks. Total mobilisation-to-report turnaround for a 5-station corridor: 14-18 days.

Coverage. AI handles parallel deployment of 5-10 stations across a corridor without proportionally increasing field staff cost.

Evidence trail. Every classified vehicle is linked to a video frame timestamp. Clients receive full unedited 7-day footage. Useful in arbitration, toll-revenue audit, and independent re-verification.

Night accuracy. IR night-vision modules maintain classification accuracy through 18:00-06:00, when human counters and even pneumatic tubes degrade most.

IRC SP 19 17-Class Vehicle Classification

NKMPV classifies every vehicle into the full 17-class scheme prescribed by IRC SP 19, not the simplified 5-7 axle-based bins typical of pneumatic-tube ATCC. The classes:

  • 1. Two-wheeler (motorcycle, scooter, moped)
  • 2. Three-wheeler / auto-rickshaw
  • 3. Car / jeep / van
  • 4. Mini-bus
  • 5. Standard bus
  • 6. LCV — passenger
  • 7. LCV — goods
  • 8. 2-axle truck
  • 9. 3-axle truck
  • 10. 4-axle MAV (multi-axle vehicle)
  • 11. 5-axle MAV
  • 12. 6-axle MAV
  • 13. 7-or-more-axle MAV
  • 14. Tractor with one trailer
  • 15. Tractor with two trailers
  • 16. Animal-drawn vehicle (ADV)
  • 17. Bicycle

What You Get — ATCC Survey Deliverables

Per-vehicle event log (CSV) with timestamp, direction, lane, IRC SP 19 class, speed, and frame reference for audit trail.

Hourly classified volume tables for all 17 IRC classes across the full 7-day window.

ADT and AADT worksheets with IRC 37 / IRC 9 seasonal correction factor application.

Vehicle Damage Factor (VDF) and cumulative MSA worksheets per IRC 37 Cl. 5.3 — directly usable as IRC 37 / IRC 58 design-traffic input.

Class-distribution charts (pie + hourly histogram) and peak-hour factor analysis per IRC 106.

Sample frame archive showing one classified video frame per IRC class as classification-evidence.

Full 7-day unedited video footage on hand-over media.

NABL-accredited summary report formatted to NHAI DPR or BoT/HAM concession requirements, signed by NABL-recognised assessors.

Industries and Clients We Serve

  • National Highways Authority of India (NHAI)
  • NHIDCL (border-area highway projects)
  • State Public Works Departments
  • BoT and HAM concession companies
  • DPR consultants and engineering houses
  • Road safety auditors
  • Urban development authorities and SEZ planners
  • World Bank / ADB-funded highway programmes
  • Toll plaza operators (toll-rate revision and revenue audit)

Get Started with Video-AI ATCC

Contact NKMPV today for an ATCC survey quote:

We respond to ATCC quote requests within one working day with a station-by-station scope, indicative cost, and IRC SP 19 deliverable list.

Why Choose NKMPV for ATCC Surveys?

Video-AI Classification — Faster and More Accurate Than Human Counts

Our in-house AI classifier consistently delivers ≥ 95% per-class accuracy against manual ground-truth, even at night and in monsoon conditions. Human counters typically drop to 80-88% accuracy over a 24-hour fatigue cycle. AI processes the entire 7-day record in 2-3 days vs. weeks for manual transcription, with no shift-change classification drift.

IRC SP 19 17-Class Compliance

We classify into the full 17-class scheme prescribed by IRC SP 19, not the simplified 5-7 axle-based bins typical of pneumatic-tube ATCC. This means the data is directly accepted for NHAI DPR submission, BoT/HAM concession reporting, and IRC 37 design-traffic computation without re-classification work.

NABL-Accredited Reports (TC-14144)

Our ATCC reports carry NABL accreditation under ISO/IEC 17025:2017 (TC-14144), making them acceptable to NHAI, state PWDs, NHIDCL, BoT/HAM concessionaires, World Bank-funded projects, and arbitration tribunals without third-party validation.

Permanent Evidence Trail

Every classified vehicle is linked to a video frame timestamp. Clients receive the full unedited 7-day footage along with the classification log, providing a permanent audit trail for toll-revenue disputes, arbitration cases, or independent re-verification.

NHAI Project Experience

Our ATCC team has deployed for NHAI corridor surveys across North India. We understand NHAI report formats, IRC SP 19 station-spacing requirements, and the seasonal-correction-factor application patterns expected by NHAI consultants.

Integrated Traffic + Axle Load + NSV + FWD Package

We bundle ATCC with paired <a href="/services/axle-load-test">axle load surveys</a> for VDF computation, <a href="/services/nsv-testing-service">NSV pavement-condition surveys</a>, and <a href="/services/falling-weight-deflectometer-fwd-test">FWD structural evaluation</a> in a single mobilisation — delivering all four IRC 37 / IRC 115 inputs from one NABL lab, reducing co-ordination cost for DPR consultants.

Frequently Asked Questions about ATCC Surveys

An Automatic Traffic Counter Classifier (ATCC) survey records and classifies every vehicle passing a road section continuously for 7 days × 24 hours per day. NHAI and IRC require ATCC data per IRC SP 19 and SP 84 to compute design traffic in cumulative Million Standard Axles (MSA) — the primary input for pavement thickness design under IRC 37. Without an ATCC-verified design-traffic input, no NHAI DPR is acceptable for technical sanction.
IRC SP 19 (Manual for Survey, Investigation and Preparation of Road Projects) prescribes a 17-class scheme: (1) two-wheeler, (2) three-wheeler/auto-rickshaw, (3) car/jeep/van, (4) mini-bus, (5) standard bus, (6) LCV-passenger, (7) LCV-goods, (8) 2-axle truck, (9) 3-axle truck, (10) 4-axle MAV, (11) 5-axle MAV, (12) 6-axle MAV, (13) 7-or-more-axle MAV, (14) tractor with one trailer, (15) tractor with two trailers, (16) animal-drawn vehicle, (17) bicycle. NKMPV's video-AI ATCC classifies into all 17 categories, vs. 5-6 categories typical of pneumatic-tube counters.
Human counters fatigue over a 24-hour shift — observed classification accuracy drops from 90%+ in the first 4 hours to 75-80% in the final 4 hours per published transportation-research studies. Pneumatic tubes can count axles but cannot reliably classify visual vehicle types (e.g., cannot distinguish a mini-bus from an LCV-passenger if both have 2 axles and similar wheelbases). Video-AI processes every frame uniformly without fatigue and uses visual signature (length, height, body shape, axle count, headlight pattern) to classify into the full 17-class scheme. NKMPV's classifier benchmarks at ≥ 95% per-class accuracy against manual ground-truth, validated on a 10% stratified sample per station.
The minimum field duration is 7 consecutive days × 24 hours per IRC SP 19. Including site reconnaissance (1-2 days), camera mounting and sight-line validation (half day), AI processing and validation (2-3 days), and traffic analysis with MSA computation and report preparation (3-5 days), total turnaround from mobilisation to final NABL-accredited report is approximately 14-18 days. Multi-station deployments along a corridor are surveyed simultaneously, so a 5-station 100 km corridor takes the same total elapsed time as a 1-station survey.
ATCC survey cost in India is project-specific and depends on station count, video-AI vs. pneumatic-tube method, classification scheme (5-class axle-based vs. 17-class IRC SP 19), and reporting depth. There is no published MoRTH Schedule of Rates - pricing is competitive and corridor-specific. NKMPV provides specific quotes after reviewing the project scope, station count, and required IRC SP 19 deliverables. For the scope variables that drive ATCC pricing, see our ATCC cost drivers guide. For a project-specific quote, call +91-82953-60108.
In NHAI documentation, ATCC refers to the Automatic Traffic Counter Classifier survey conducted under IRC SP 19 and IRC SP 84 requirements for Detailed Project Reports (DPRs). NHAI requires ATCC data at the DPR-feasibility stage for new construction, at the technical-sanction stage for widening or rehabilitation, and periodically during BoT/HAM concession operation for compliance monitoring. The output is the 7-day classified count, AADT, VDF, and cumulative MSA design traffic that feeds directly into IRC 37 pavement thickness design.
Design traffic in cumulative Million Standard Axles (MSA) is calculated per IRC 37 Clause 5.3 using N = 365 × A × D × F × [(1+r)ⁿ − 1] / r, where A is the AADT of commercial vehicles in the year of completion, D is the lane distribution factor, F is the Vehicle Damage Factor (VDF) computed from paired axle load survey data using the fourth-power law, r is the annual traffic growth rate (typically 5-7.5% for Indian highways), and n is the design life in years (typically 15-20 years for NH and 10-15 years for SH). The ATCC survey provides A, the directional split, and the per-class fleet composition required to weight the VDF correctly.
A manual classified count uses trained surveyors with tally counters at the roadside to classify each passing vehicle and tabulate hourly volumes. It is the IRC reference method but is labour-intensive, fatigue-limited, and impractical for the full 7-day × 24-hour duration. An ATCC (video-AI or pneumatic-tube) automates the same classification continuously without surveyor fatigue. NKMPV's video-AI ATCC delivers manual-level classification accuracy (95%+) at ATCC scale (continuous, multi-station, simultaneous), with manual counts reserved for the 10% ground-truth validation sample.
Yes. We deploy ATCC for periodic BoT/HAM concession monitoring per IRC SP 84 and concession-agreement-specific intervals (typically annual or milestone-driven). Our reports include AADT trend tables vs. baseline DPR projections, classification-shift analysis (e.g., shift in 2-axle vs. MAV ratio over time), and toll-revenue-equivalent calculations to support concessionaire compliance reports and any toll-rate revision applications under NH Fee Rules.
Primary: IRC SP 19 (Manual for Survey, Investigation and Preparation of Road Projects) defines the 17-class vehicle scheme and ATCC survey methodology. IRC SP 84 (Manual of Specifications and Standards for Six-Laning of National Highways) sets the traffic-data requirements for BoT/HAM concession projects. IRC 37 (Flexible Pavement Design) and IRC 58 (Rigid Pavement Design) define how ATCC data converts into MSA. IRC 9 establishes seasonal correction factors for AADT estimation. IRC 106 covers urban capacity analysis. MoRTH circulars under RW/NH-33044 define acceptance protocols for NHAI DPR submission.
Yes. NKMPV provides ATCC services across India. Our headquarters and primary lab are in Pinjore, Haryana, with mobile camera-AI deployment to projects nationwide. We have completed ATCC for NHAI corridors and have run ATCC across Punjab, Haryana, Himachal Pradesh, J&K, Ladakh, Uttarakhand, and Rajasthan. Project mobilisation outside North India is arranged on a per-project basis with logistics included in the quote. Our NABL accreditation (TC-14144) is recognised across all Indian state PWDs and central agencies.
A standard ATCC deliverable package includes: (1) per-vehicle event log in CSV with timestamp, direction, lane, IRC SP 19 class, speed and frame-reference; (2) hourly classified volume tables for all 17 classes; (3) ADT and AADT computation worksheets with seasonal correction; (4) directional distribution and peak-hour analysis; (5) class-distribution pie charts and hourly distribution histograms; (6) Vehicle Damage Factor (VDF) and cumulative MSA design-traffic worksheets per IRC 37; (7) sample-frame archive showing one classified frame per IRC class as evidence; (8) the full unedited 7-day video footage on hand-over media; (9) NABL-accredited summary report formatted to NHAI DPR or BoT/HAM concession requirements.

Related Testing Services

Axle Load Survey for Highways

Vehicle-by-vehicle axle load measurement for VDF and MSA computation per IRC 37 / IRC 81.

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Flexible Pavement Design

Pavement thickness design using CBR, FWD, and ATCC traffic data per IRC 37:2018.

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Network Survey Vehicle (NSV) Survey

Automated road condition survey per IRC SP 16 — bundled with ATCC for full pavement evaluation.

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Falling Weight Deflectometer (FWD) Test

Pavement structural evaluation per IRC 115 / ASTM D4694 for overlay design.

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