Approach to acoustic mapping through continuous mobile monitoring
- Autores: Guillermo Quintero Pérez
- Directores de la Tesis: J. Romeu (dir. tes.), Andreu Balastegui Manso (dir. tes.)
- Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2019
- Idioma: español
- Tribunal Calificador de la Tesis: Teresa Pàmies Gómez (presid.), Juan Miguel Barrigón Morillas (secret.), Pierre Aumond (voc.)
- Programa de doctorado: Programa de Doctorado en Ingeniería Ambiental por la Universidad Politécnica de Catalunya
- Materias:
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- Resumen
For the production of representative noise maps, a large amount of information is necessary, which includes, among others, on-site measurements of environmental noise. Thus, for noise maps based on measurements, mobile sampling emerges as a possible solution for the enhancement of data acquisition.
The present research proposes a complete framework to perform mobile sampling. Since the normative requires long-term values to be presented in a noise map, a sampling strategy based on temporal stratification, which reduces the required sampled days to estimate the annual equivalent noise level, is presented. Furthermore, to compute long-term values for the night period, since they are usually affected by noise sources different to traffic, specifically leisure noise, a complementary temporal and spatial stratification is also presented.
Then, the statistical requirements to perform mobile noise measurements using bicycles is evaluated. The vehicles and bicycles journeys are reproduced based on micro-traffic simulation and then coupled with an acoustic modeling. The estimation error of LAeq for the mobile sampling is compared to reference static samples, in terms of the Root Mean Square Error (RMSE), and is computed for different aggregation radius of mobile receivers, and as a function of the number of passes-by and to the distance to its nearest cross street.
To perform the mobile sampling on a real scenario, a low-cost noise monitoring device with the aim of performing georeferenced noise sampling, is developed. The accuracy tests suggest that it is able to acquire noise levels with an equivalent accuracy as a Class 2 sound level meter.
Finally, to validate the results obtained through the modeling framework, a noise monitoring device is mounted on a bicycle and on-site mobile measurements are performed simultaneously to reference static ones. The same scenario is again recreated based on micro-simulation of traffic complemented with acoustic modeling. Then, for the simulated framework and the on-site measurements, the RMSE of the estimation of LAeq for different aggregation radius of mobile samples is compared to the reference static ones. It is confirmed that mobile sampling is a solution to improve noise data acquisition, which reduces the resources required to produce a noise map without sacrificing the accuracy and representativeness.
