Machine Learning-based GIS Model for 2D and 3D Vehicular Noise Modelling in a Data-scarce Environment

Abstract

Abstract Vehicular traffic significantly contributes to economic growth but generates frictional noise that impacts urban environments negatively. Road traffic is a primary noise source, causing annoyance and interference. Traditional regression models predict two-dimensional (2D) noise maps, but this study explores the impact and visualization of noise using 2D and three-dimensional (3D) GIS (Geospatial Information Systems) functionalities. Two models were assessed: (i) a 2D noise model for roads and (ii) a 3D noise model for buildings, utilizing limited noise samples. Combining these models produced a comprehensive 3D noise map. Machine learning (ML) models—artificial neural network (ANN), random forest (RF), and support vector machine (SVM)—were evaluated using performance measures: correlation (R), correlation coefficient (R2), and root mean square error (RMSE). ANN outperformed others, with RF showing better results than SVM. GIS was applied to enhance the visualization of noise maps, reflecting average traffic noise levels during weekday mornings and afternoons in the study area.