Acoustic sensing for road traffic modelling and noise assessment

Road traffic noise is a major environmental pollutant with significant so cietal impact, warranting reliable exposure assessment. Beyond its health impact, noise also contains rich temporal features that can be exploited for analysis. The state-of-the-art CNOSSOS-EU framework requires detailed traffic data, but in cities with limited monitoring infrastructure this input is often unreliable, reducing assessment accuracy. The thesis addresses this gap by developing a methodology that exploits data from roadside noise sensors and expands their functionality to also serve as traffic sensors, providing the necessary inputs to a modelling chain for road traffic noise.

The methodology is validated through a case study on a busy urban road in Stockholm, Sweden. Three primary objectives are considered: (i) estimating traffic flow rates from noise measurements, (ii) evaluating the use of noise data as an alternative to radar in a microscopic noise assessment framework, and (iii) developing a methodology to define noise mitigation strategies using city-wide noise assessments.
Ad-hoc noise sensors were deployed at three positions at the case study location for over 400 days, generating data for the development and testing of traffic flow estimation models. These models were also adapted through specialized training to reduce the need for expensive traffic sensor data when estimating conditions at a new location. A microscopic-traffic-based noise simulation framework was implemented and run with input from noise data, and its outputs were compared against those from radar-based input. Algorithms for optimal vehicle routing were combined with noise-based cost functions to determine vehicle routes reducing population exposure to noise.

Results show reliable traffic flow estimation across temporal and spatial variations. Combining datasets from multiple locations and using synthe- sized data show potential for cost-effective implementation at new locations. Output of noise simulations based on noise measurements show good agreement with output based on radar data. Inclusion of noise exposure constraints in vehicle routing identifies routes with lower noise exposure, while remaining logistically feasible.
The findings demonstrate the potential for noise sensors to serve as a reliable traffic sensor, and drive noise simulations with accuracy similar to that from radar data. These low-cost multifunctional sensors are well suited for large-scale deployment in smart cities, supporting noise assessment and mitigation, as well as systems for intelligent traffic management.

Keywords: Environmental acoustics, acoustic measurements, CNOSSOS-EU, noise sensors, road traffic noise, traffic monitoring, vehicle routing, Smart City.