  With rapid urbanisation and the accompanying increase in traffic density,
traffic noise has become a major concern in urban planning. However,
traditional grid noise mapping methods have limitations in terms of time
consumption, software costs, and a lack of parameter integration interfaces.
These limitations hinder their ability to meet the need for iterative updates
and rapid performance feedback in the early design stages of street-scale urban
planning. Herein, we developed a rapid urban traffic noise mapping technique
that leverages generative adversarial networks (GANs) as a surrogate model.
This approach enables the rapid assessment of urban traffic noise distribution
by using urban elements such as roads and buildings as the input. The mean
values for the mean squared error (MSE) and structural similarity index (SSIM)
are 0.0949 and 0.8528, respectively, for the validation dataset. Hence, our
prediction accuracy is on par with that of conventional prediction software.
Furthermore, the trained model is integrated into Grasshopper as a tool,
facilitating the rapid generation of traffic noise maps. This integration
allows urban designers and planners, even those without expertise in acoustics,
to easily anticipate changes in acoustics impacts caused by design.
