Adaptive Urban Planning: A Hybrid Framework for Balanced City Development

ACL ARR 2025 May Submission664 Authors

Abstract: 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 evaluates and integrates these proposals, ensuring both efficiency and adaptability. We validate our framework on a newly created dataset of detailed region and sub-region maps from three cities in India, focusing on areas undergoing rapid urbanization. The results show that it can improve planning efficiency, better address local demographic needs, and scale for real-world deployment. Our work also identifies key challenges, including the trade-off between system efficiency and adaptability, as well as the complexities of handling diverse urban datasets. These insights contribute to a broader understanding of how multi-agent systems can enhance large-scale urban planning.

Paper Type: Short

Research Area: NLP Applications

Research Area Keywords: NLP Applications

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 664