Abstract
Abstract Visualisation of road traffic noise is vital for traffic noise planning policies. Several factors affect the noise from road traffic with physical and environmental conditions. Collecting noise levels around the world is not a possible task. Therefore, calculating noise levels by a valid noise model, and spatial interpolations, is prime to traffic noise visualisation. In this study, the Henk de Klujijver noise model is used. Designing noise observation points (Nops) embedding with a three-dimensional (3D) building model and identifying the best suitable spatial interpolation are important to visualise the traffic noise accurately. However, interpolating noise in 3D space (vertical direction) is a more complex process than interpolating in two-dimensional (2D) space. Flat triangles should be eliminated in the vertical direction. Therefore, the structure of Nop has a major influence on spatial interpolation. Triangular Irregular Network (TIN) interpolation is more accurate for visualising traffic noise as 3D noise contours than Inverse Distance Weighted and kriging. Although kriging is vital to visualise noise as raster formats in 2D space. The 3D kriging in Empirical Bayesian shows a 3D voxel visualisation with higher accuracy than 3D TIN noise contours.
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