Evaluation the interpolation methods to product the traffic noise maps in the city center of Baghdad using Arc map pro model builder.

Developing countries suffer from different problems where irregular expansion, wrong transport policy and weak legislation lead to an important problem of traffic noise. Noise is exacerbated at squares and intersections, which represent points for conflict and increasing traffic movement and cause complexity for motorists, cyclists, pedestrians and other road users. Today's the technologies allow us to measure the amount of noise for any given site, but the measurements require cost, effort and time to do so, as it is impossible to measure noise per meter in study areas and that is neither practical nor useful. Traffic noise analysis relies on spatial and attributes data for noise points measured in situ and since the measured noise points are separate and to make them a continuous surface, spatial interpolation methods were required used to fill in these gaps. This research evaluated four spatial interpolation methods (IDW, Natural Neighbor, Spline, Kriging) to determine the best way to produce the traffic noise maps for squares and intersections of Baghdad city center. Noise measurements were taken on both sides of the roads entering and exiting the square or intersection during peak traffic congestion from (7-9) in the morning and (12-2) in the noon and (4-6) in the evening and the noise indices (LAeq), (LAmax) and (LAmin) were measured using device (SVAN977 Sound and Vibration Analyzer). Due to the multiplicity of study areas, which amounted to (32) squares and intersection, and to facilitate process of evaluation between interpolation methods for producing and check noise maps, were used the visual programming language (Model Builder) and tools for spatial analysis and processing in (Arc map pro) program to done the project. The results showed to the selected study area in this paper (Almathaf Square) that (Spline) method was the best, less mean predication error and the lest standard deviation, followed by (IDW) method, then (Kriging) method but the (Natural Neighbor) method has been excluded. [ABSTRACT FROM AUTHOR]