Adaptive Urban Planning: A Hybrid Framework for Balanced City Development
This work addresses urban planning problems for rapidly developing cities, but it is incremental as it builds on existing approaches by combining them.
The paper tackles the challenge of balancing city-wide infrastructure needs with localized demographic preferences in urban planning by proposing a hybrid two-tier framework that integrates top-down optimization with bottom-up community planning, resulting in more nuanced urban development while maintaining overall city functionality.
Urban planning faces a critical challenge in balancing city-wide infrastructure needs with localized demographic preferences, particularly in rapidly developing regions. Although existing approaches typically focus on top-down optimization or bottom-up community planning, only some frameworks successfully integrate both perspectives. Our methodology employs a two-tier approach: First, a deterministic solver optimizes basic infrastructure requirements in the city region. Second, four specialized planning agents, each representing distinct sub-regions, propose demographic-specific modifications to a master planner. The master planner then evaluates and integrates these suggestions to ensure cohesive urban development. We validate our framework using a newly created dataset comprising detailed region and sub-region maps from three developing cities in India, focusing on areas undergoing rapid urbanization. The results demonstrate that this hybrid approach enables more nuanced urban development while maintaining overall city functionality.