A rapid approach to urban traffic noise mapping with a generative adversarial network
This enables urban designers and planners, including non-experts, to quickly assess noise impacts during early design stages, addressing a domain-specific need in urban planning.
The paper tackled the problem of slow and costly urban traffic noise mapping by developing a rapid technique using generative adversarial networks (GANs) as a surrogate model, achieving a mean RMSE of 0.3024 dB(A) and SSIM of 0.8528 on validation data.
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 (RMSE) and structural similarity index (SSIM) are 0.3024 dB(A) and 0.8528, respectively, for the validation dataset. 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 in the early design stages.