As we continue to build, develop, and redevelop our communities, there is a continuous need to monitor and evaluate noise levels of our road networks and urban areas. High noise levels have many documented adverse effects on our health: poor concentration, disrupted sleep, stress-related cardiovascular issues, impaired hearing, and in children, reduced attention span and academic performance. In addition to complying with established laws regarding acceptable noise levels, we're obligated to reduce harmful effects for the well-being of our communities.
Traffic noise impact studies assess the impacts of roadway traffic noise when certain roadway improvements are being proposed. They help urban planners and local governments determine if noise mitigation measures are required. These may include construction noise barriers or earth noise berms, traffic management measures such as visible police presence to slow down traffic, zoning changes and alteration of horizontal and vertical alignments. Studies also provide useful information to government agencies in recommending ways to address high noise levels and provide information to the general public about impacts caused by either proposed new roadway projects or roadway improvement projects that increase traffic capacity.
There are numerous challenges inherent to conducting traffic noise impact studies, such as:
Removing environmental noise. Traffic noise levels need to be evaluated independently from environmental factors that interfere with readings, like wind, temperature, and background noise. Microphone placement and calibration are essential for capturing reliable measurements.
Multiple noise sources. Industrial facilities, construction projects, and nature sounds can obscure vehicle-related noise. Using advanced filtering and strategic monitoring locations can differentiate between various sources.
Manual audio processing. Once the audio is recorded, the process of listening to hours and hours of audio files to distinguish traffic noises is time-consuming and prone to errors.
Sophisticated Improvements to a Complex Process
Artificial intelligence (AI) can be used to improve data accuracy, produce predictive forecasting, and generate high-quality visualizations for traffic impact studies, including:
Collecting & Analyzing Data AI-powered sensors and IoT devices placed at strategic locations (near residential areas, on a highway sound barrier, etc.) enable continuous and automated monitoring of traffic noise while filtering out irrelevant background sounds. The resulting large datasets can be analyzed to more accurately identify trends, outliers, and correlations.
Machine learning prediction. Models that can predict future noise levels through historical traffic and environmental data allow planners to assess potential noise impacts before changes occur. Through modeling different traffic scenarios, AI-driven simulations can demonstrate the effect of a road expansion or speed limit adjustment on noise pollution levels. Models can incorporate environmental factors like weather, vehicle types, and road surfaces for higher forecasting accuracy, and ultimately, more informed decisions.
Noise maps. Monitoring station and traffic sensor data can be processed by AI to produce real-time and dynamic noise pollution visualizations, including hot spots. With the additional integration of satellite and drone imagery, AI enables frequent map updates, which improves the useful life of noise impact assessments in urban environments.
Simulating noise propagation. AI can dynamically adjust simulations based on real-time traffic conditions, weather variations, and environmental factors like road surface materials, vehicle speed, and urban structures that influence sound reflection and absorption. With the ability to run several traffic scenarios simultaneously, planners can assess the effectiveness of mitigation strategies before making decisions.
Noise source identification. AI algorithms can readily distinguish traffic noise from construction work, weather events, barking dogs, music, voices, birds, and aircraft through pattern recognition and deep learning. Sound classification models analyze audio data to determine the contribution of different vehicle types to overall noise levels. Through processing data from multiple sensors, AI tools can locate and identify specific noise sources to support targeted noise mitigation strategies.
Streamlined Analysis with SoundScanNX
BRYX’s latest AI offering, SoundScanNX, is specifically designed for traffic noise impact studies. This machine learning model helps streamline the process of reviewing audio for traffic noise studies by automatically distinguishing traffic noise from non-traffic related noise, eliminating time-consuming and error-prone manual processes. Save hours of manual audio playback and spend time on more productive work with SoundScanNX.