Enhancing people’s walking preferences in street design through generative artificial intelligence and crowdsourcing surveys: The case of Tokyo

The emerging generative artificial intelligence (generative AI) model, Stable Diffusion, is increasingly recognized as a promising tool for efficiently translating public preferences and ideas into spatial design representations. At the same time, deep learning techniques and crowdsourcing methods have enabled the large-scale collection of public walking preference data. This study introduces an innovative approach that leverages the Stable Diffusion model, combined with extensive public walking preference data, to create a workflow for generating revitalized street scenes aimed at enhancing subjective walking preferences. Compared to existing GAN-based methods, the approach used in this study is more efficient to train and generates more realistic and controllable outputs. The approach was tested and validated using data from Tokyo’s Setagaya ward, confirming its effectiveness. This workflow represents a significant advancement in street design and redevelopment, delivering practical value and innovation by equipping designers and planners with rapid visual insights in the early design stages. Additionally, it fosters democratic urban design by utilizing crowdsourced data as training input for generative AI models.