1. Purpose:

The CRTN method is used to calculate noise levels from road traffic at various points near roadways. It is commonly applied to determine whether new roads or changes to existing roads will result in unacceptable noise impacts on surrounding areas, such as residential zones, schools, or hospitals. The predictions are used to assess compliance with environmental regulations or guidelines regarding noise pollution, and to help in the design of noise mitigation measures, such as noise barriers or road surface changes.

2. Noise Metric: LA10,18h:

CRTN predicts noise levels using the LA10,18h metric, which represents the A-weighted noise level exceeded for 10% of the time over an 18-hour period (from 6:00 AM to midnight).

This metric is commonly used in road traffic noise studies as it captures the higher noise levels associated with peak traffic conditions, giving a good representation of noise impact on communities.

3. Inputs Required for Calculation: CRTN uses various factors to predict road traffic noise, including:

Traffic Flow: The volume of vehicles passing on the road, typically measured as vehicles per hour or vehicles per day.

Vehicle Speeds: The average speed of vehicles on the road, as noise increases with speed.

Road Surface: The type of road surface affects the noise produced, with smoother surfaces generating less noise than rougher surfaces.

Traffic Composition: The percentage of heavy goods vehicles (HGVs) or buses, which produce more noise than smaller vehicles like cars.

Gradient of the Road: The slope of the road affects noise generation, with steeper gradients often causing more noise as vehicles work harder (more acceleration or braking).

Distance from the Road: Noise predictions are calculated at specific distances from the road, usually measured at 1 meter in front of a building facade or at various free-field locations.

Screening: The presence of structures like walls, buildings, or natural features (e.g., hills) that may block or reflect noise and reduce its impact on nearby areas.

4. Application: CRTN is used in several contexts:

Environmental Impact Assessments (EIAs): It helps predict noise levels from new or modified road schemes and assess their impact on the local environment.

Planning and Development: Local authorities and developers use CRTN to determine whether proposed projects near roadways will cause significant noise issues and whether mitigation measures are needed.

Noise Mapping: CRTN data feeds into noise mapping projects, which model road traffic noise across large areas, such as urban regions or entire road networks.

5. Correction Factors: CRTN allows for the inclusion of various correction factors to improve the accuracy of noise predictions:

Ground Absorption: Soft ground (e.g., grass, soil) can absorb noise, reducing its impact, while hard surfaces (e.g., concrete, asphalt) reflect noise, increasing its reach.

Reflection from Buildings: Buildings near the measurement point can reflect sound waves, potentially increasing the perceived noise level.

Distance Attenuation: Noise decreases with distance, so CRTN accounts for this natural reduction in sound intensity over longer distances.

Barrier Attenuation: Noise barriers, embankments, or other obstructions can provide significant reductions in traffic noise, which CRTN calculations account for.

6. Reference Speed:

CRTN uses reference speeds (the assumed average speeds for cars and heavy vehicles) as part of the calculation, which are based on whether the road is in an urban or rural area, and if it is a motorway or local road.

7. Limitations:

While CRTN provides reliable estimates of road traffic noise, it is primarily designed for steady-state traffic conditions and open-air noise propagation. It may not account for complex urban environments, highly variable traffic conditions, or other noise sources (e.g., railways, aircraft). CRTN is also based on the noise behaviour of vehicles and traffic patterns from the 1980s, so while still in use, updates or supplementary methods may be needed to account for changes in vehicle technologies and traffic patterns.