Abstract
Digitalization is changing the way cities are planned. Technological innovations in AI and data analysis are leading to new design strategies and processes in which urban designers must constantly redefine their role in the interaction with digital tools. In this chapter, we demonstrate how various generative and analytical methods can be used to create adaptive master plans (AMPs). AMPs are computational models that are able to generate urban plans based on context information, boundary conditions, and design performance goals. In contrast to traditional urban design models, where one static representation of a design is created, AMPs represent a flexible design solution, allowing the designeror other stakeholdersto change the input parameters and explore their effects on the resulting design. In this chapter, we briefly summarize the origins of AMPs and show some of their basic components. By means of two examplesthe design of numerous small cities in Ethiopia and of a new urban district on Singapore's waterfront, we show the potential that such AMPs have for the creation, analysis, and optimization of urban plans.
Original language | English |
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Title of host publication | Artificial Intelligence in Urban Planning and Design |
Editors | Imdat As, Prithwish Basu, Pratap Talwar |
Publisher | Elsevier |
Pages | 323-337 |
Number of pages | 15 |
ISBN (Print) | 9780128239414 |
DOIs | |
Publication status | Published - 2022 |
Research Field
- Former Research Field - Integrated Digital Urban Planning
Keywords
- Adaptive master plans; Computational urban planning; Generative design; Optimization; Artificial intelligence (AI); Urban geography; Geomatics; AI in design