For the validation, the Anderson-Kurze, Kang, Yang and Zhang, Bistafa and Naish model were applied, and then the t-Student test were applied. In total, 5124 readings of source positions were evaluated in 84 measured points. The sound source was positioned on the axis of each strip, every five meters. At each point, the user was simulated standing and sitting.
The study was applied in three sections in the city of Maringá, Brazil. In the model, the calculation of direct paths and specular reflections and diffuse was adopted. To help assess the exposure and the environmental impact, sound mapping will be performed using the IMMI software. The objective of this research is to develop a mathematical model to predict the road traffic noise level at the bus stop, to assess the level of noise that users of these urban facilities are exposed daily. All these methods are enabled thanks to the open source ecosystem, such as Python libraries, R software suite and GIS tools. Thus, in this paper, we present different possibilities for a user to perform his own data analysis, namely: a local export of data from the smartphone, access to raw data and pre-processed data from the NoiseCapture server, access to formatted GIS layers from OGC standard service. As these new applications emerged, the development team of NoiseCapture was led to extend the possibilities of exploitation of these data. Beyond the initial objective, other uses of the application have emerged: individually by users for their own needs, by associations of people in charge of the fight against noise pollution, within the framework of educational activities, by researchers for the realization of their own research, by communities to address the subject of noise pollution. After more than 3 years, the database produced from all over the world contributions is considerable (more than 77k contributors, nearly 300k tracks representing about 72 million 1-second measurements, in nearly 200 countries).
NoiseCapture is a smartphone application initially developed as part of a participative approach for environmental noise mapping.
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