Statistical learning approach applied to road surface classification.

Abstract

Measures aiming environmental noise abatement usually consider acoustic barriers alongside the road. However, the cost associated with these measures is usually considerably high and its performance in urban areas is reduced. The problem of the visual impact is another issue affecting the communities. Nowadays, road planners have started to consider silent surfaces as an alternative. These types of surfaces are constituted basically by changing the texture and/or porosity of the mixtures. In some conditions, noise level abatements up to 15 dB can be achieved. Therefore, a considerable variety of different surfaces are available. The main goal of this research is to identify and classify different types of road pavements by analyzing the noise profile, using the close proximity method. Feature extraction and selection is one of the first procedures on a classifier algorithm. Moreover, the accuracy of the results is strongly dependent on the right choice of the selected feature vector. Standard classifiers are being tested in order to establish guidelines for future developments of this research. In situations of net road surveillance, searching for inhomogeneities on the surface and the presentation of the results in a geographic map, showing the locations of the surface types and the noise levels, can improve the accuracy of the noise mapping models.

Investigation and analysis of urban noise for sustainability

“…Sustainable cities should offer good quality standards to sustain a well-balanced and harmonious life, most of which can be obtained by establishing sound management policies, the presence of green areas, and acoustic barriers alongside roads, as noise abatement strategies to improving environmental quality of inhabitants [6]. Besides changes at institutional frameworks, human values and patterns of social behaviors are also important [7].…”