sonROAD18: Swiss Implementation of the CNOSSOS-EU Road Traffic Noise Emission Model

Abstract

To ccount for recent developments in the fleet of ro d vehicles nd to extend the llow ble speed r nge, Switzerl nd h s upd ted its ro d tr fficnoise emission model under the cronym sonROAD18.The model description is b sed on the form lism of CNOSSOS, howevers pecified in third oct ve b nds. In extension to the decl r tion of n ver ge sound power r di ted by s ingle vehicle, st tistic l indic tors re provided s well to describe individu l v ri tions. Omnidirection l r di tion ch r cteristics is ssumed in the horizont l pl ne whereby the emission is defined s equiv lent sound power of point source th t induces correct sound event level. In vertic l direction, spectr l corrections re pplied to ccount for the reduced emission strength under l rge elev tion ngles. The ccur cyofthe model is specified for different scen rios with respect to the levelofdet il of v il ble input p r meters.

Adverse Cardiovascular Effects of Traffic Noise with a Focus on Nighttime Noise and the New WHO Noise Guidelines

“…The general trend of increasing road traffic volumes is unbroken (31). With the exception of the lowest speed regimes, rolling noise, i.e., the sound generated by the interaction of tires and pavement, is the dominating noise source (45). The introduction of electric cars, which affects only engine noise, will therefore not lead to an important reduction in noise levels.…”

A Harmonized Data Model for Noise Simulation in the EU

“…Reference [23] determined the compatibility of the old approach (XPS 31-133) with the upcoming one (CNOSSOS-EU Road) for calculating road noise levels for the city of Trento, Italy. Furthermore, Switzerland has updated its road traffic noise emission model (sonROAD18) based on CNOSSOS-EU [24]. CNOSSOS has also been implemented in noise simulation software, such as Geomilieu [25], MithraSIG [26], IMMI [27], Predictor-LimA [28], CadnaA [29], etc.…”

Traffic Noise Estimation from Satellite Imagery with Deep Learning

“…Today, noise mapping is widely achieved by physical models that formulate a relationship between multiple input variables and the resulting noise. However, since physical models like sonROAD18 [4], which is currently employed in Switzerland, rely on parameters related to traffic, road, and environmental conditions as inputs, they require the collection of substantial amounts of data. Alternative approaches utilize Artificial Neural Networks in a regression setup [5,6,7], but still rely on the manual collection of numerous input variables over large areas.…”

“…In this work, we investigate the feasibility of estimating road traffic noise from satellite imagery only, based on road noise estimates for Switzerland derived with the sonROAD18 model [4]. This approach is inspired by methods for the extraction of road networks from aerial images [8,9], but extends that work by also learning the noise characteristics of different areas and road types.…”