Road Traffic Noise Visualization Modeling in Two- and Three-Dimensional Space: A Review

Traffic noise causes 90% of urban noise pollution. Visualizing noise is challenging. Three-dimensional (3D) space noise visualization reflects natural movement or acoustic dispersion. However, improving traffic noise visualization is hardly addressed. This review paper aims to fulfill the gap mentioned earlier. Noise models such as the Federal Highway Administration (U.S.), RLS-90 Model (Germany), Road Traffic Noise Prediction (U.K.), Stop and Go model (Thailand), and Henk de Kluijver model are compared and described. Noise is visualized using hotspots, contours, and building facades. The shape and direction of building facades do not affect 3D noise visualization. Traffic volume, composition, speed, road gradient, surface, ground covering, and distance to traffic sources affect noise. Green spaces absorb noise and provide acoustic relief. The 2D and 3D propagation should address noise screening and diffraction. Interpolating noise for visualization uses IDW, Kriging, and TIN. TIN outputs correct noise contours. Flat triangles must be avoided to remove unpredictable oscillation of interpolated noise levels in 3D space. For 2D noise visualization, raster cell size and noise level accuracy are essential. Noise interpolation requires 2 m minimum observation points. Plus, this review identifies the IDW distance weighted factor and the optimal Kriging variogram. Furthermore, this review suggests purple instead of red to denote high noise levels.