![]() ![]() Over the years, AI has been defined in many distinct but connected ways, encompassing different subfields and methods. Further, it can be leveraged by more stakeholders in sectors such as real estate, public services, and public health to make decisions as the urban built environment has a fundamental impact on all these fields.Īrtificial intelligence is increasingly being adopted to optimize, simplify, and extend operations in various areas of knowledge. This research equips designers with capabilities to co-design of mobility solutions and urban form early on in the design process. Three scenarios that adapt to different design goals and boundary conditions are presented. The applicability and effectiveness of the workflow are tested in a pedestrian-oriented neighborhood design case study. The proposed framework is versatile and adaptive by allowing designers to tune simulation parameters and customize the decision-making process. ![]() ![]() Key components of the workflow include automating the process of parsing the map data, building contextual models with population and amenity data, conducting an integrated mobility simulation, and generating a street network and amenity allocation plan accordingly. This paper proposes a novel generative workflow for walkable neighborhood design. ![]()
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