RASTER DATA BASED AUTOMATED NOISE DATA INTEGRATION FOR NOISE MAPPING LIMITING DATA DEPENDENCY
Keywords: Noise, Vehicle extraction, Clustering, Classification, Path determination, Data Dependency, Instantaneous Noise
Abstract. Noise has become a recurrent problem worldwide. Road traffic noise studies in India are fewer and restricted only to the metropolitan areas. These studies focused on recording, monitoring, analysis, modelling, and mapping. The major concern is with the onsite collection of vehicular noise data from road sites. Road traffic noise maps have been generated by using traditional techniques that involve the collection of road traffic noise by experts. There are negligible studies in the area of automated noise generation for road traffic noise. In this paper, the study examines the problems that an individual is facing in collecting onsite noise data. Onsite Noise data collection with Sound Pressure level increases the delay. A noise map is a graphical representation of the spatial dissemination in a given area for a characterized period. Developing any geospatial application requires the collection of geospatial data and attribute information. Open-source geospatial data are largely available today in the form of Map APIs. Making a model to extract spatial and attribute information from it can offer an easy solution for urban applications, without needing a separate collection of geospatial or attribute information. Google raster maps for city roads and surrounding buildings in UP are tried to be used to extract roads, buildings, vehicles, trees, etc. Various geometrical setups of vehicles in several similar road segments are tried classified using ML algorithms. Vehicular clusters in road segments are classified into 3 categories, high, medium, and low. Further characterized in terms of the range of noise spectra associated with it incorporating field data. These noise scenes are then utilized to predict the various types of simulated noise maps predicted around the road segments on an instantaneous scale, with an estimation of accuracy.