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Model based monitoring of traffic noise in an urban district

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Author: Eerden, F. van der · Graafland, F. · Wessels, P. · Segers, A. · Salomons, E.
Type:article
Date:2014
Publisher: Australian Acoustical Society
Source:Davy, J.Burgess, M.Don, C.Dowsett, L.McMinn, T.Broner, N., INTERNOISE 2014 - 43rd International Congress on Noise Control Engineering: Improving the World Through Noise Control, November 16-19, 2014, Melbourne, Australia
Identifier: 524087
Keywords: Data assimilation · Monitoring · Traffic noise · Urban sound propagation · Acoustic variables control · Monitoring · Noise pollution · Railings · Roads and streets · Sensor networks · Time varying networks · Transportation · Data assimilation techniques · Engineering modeling · Model-based monitoring · Noise map · Sound propagation · Time-varying sounds · Acoustic noise

Abstract

Noise control for an urban district starts by understanding the actual noise situation. A correct understanding is needed to take appropriate and cost efficient measures. For a noise burdened urban district, surrounded by road and rail traffic, the traffic noise as well as the annoyance has been measured. The size of the district is approximately one square km. With the help of 35 microphones, applied in a scalable sensor network, the time-varying sound levels were recorded. These results were coupled to an engineering model to obtain the sound levels for the complete district as well as to discriminate between road and rail traffic noise. Also, a data assimilation technique has been applied to increase the agreement between the measurement and model results. For example, for Lden sound levels the standard used source strengths for road and rail needed to be adapted to better match the sound level measurement results. In a separate paper these corrected sound levels at the façades are coupled to annoyance survey results to derive a local exposure-response relation. The annoyance survey also indicated the importance of peak levels and vibrations. This is further investigated by considering the measured noise dynamics.