Generative urban design: A systematic review on problem formulation, design generation, and decision-making

Abstract

Urban design is the process of designing and shaping the physical forms of cities, towns, and suburbs. It involves the arrangement and design of street systems, groups of buildings, public spaces, and landscapes, to make the urban environment performative and sustainable. The typical design process, reliant on manual work and expert experience has unavoidable low efficiency in generating high-performing design solutions due to the involvement of complex social, institutional, and economic contexts and the trade-off between conflicting preferences of different stakeholder groups. Taking advantage of artificial intelligence (AI) and computational capacity, generative urban design (GUD) has been developed as a trending technical direction to narrow the gaps and produce design solutions with high efficiency at early design stages. It uses computer-aided generative methods, such as evolutionary optimization and deep generative models, to efficiently explore complex solution spaces and automatically generate design options that satisfy conflicting objectives and various constraints. GUD experiments have attracted much attention from academia, practitioners, and public authorities in recent years. However, a systematic review of the current stage of GUD research is lacking. This study, therefore, reports on a systematic investigation of the existing literature according to the three key stages in the GUD process: (1) design problem formulation, (2) design option generation, and (3) decision-making. For each stage, current trends, findings, and limitations from GUD studies are examined. Future directions and potential challenges are discussed and presented. The review is highly interdisciplinary and involves articles from urban study, computer science, social science, management, and other fields. It reports what scholars have found in GUD experiments and organizes a diverse and complicated technical agenda into something accessible to all stakeholders. The results and discoveries will serve as a holistic reference for GUD developers and users in both academia and industry and form a baseline for the field of GUD development in the coming years.