Large Language Model for Participatory Urban Planning

• URL: http://arxiv.org/abs/2402.17161v1
• Date: Tue, 27 Feb 2024 02:47:50 GMT
• Title: Large Language Model for Participatory Urban Planning
• Authors: Zhilun Zhou, Yuming Lin, Depeng Jin, Yong Li
• Abstract summary: Large Language Models (LLMs) have shown considerable ability to simulate human-like agents. We introduce an LLM-based multi-agent collaboration framework for participatory urban planning. Our method achieves state-of-the-art performance in residents satisfaction and inclusion metrics.
• Score: 22.245571438540512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Participatory urban planning is the mainstream of modern urban planning that involves the active engagement of residents. However, the traditional participatory paradigm requires experienced planning experts and is often time-consuming and costly. Fortunately, the emerging Large Language Models (LLMs) have shown considerable ability to simulate human-like agents, which can be used to emulate the participatory process easily. In this work, we introduce an LLM-based multi-agent collaboration framework for participatory urban planning, which can generate land-use plans for urban regions considering the diverse needs of residents. Specifically, we construct LLM agents to simulate a planner and thousands of residents with diverse profiles and backgrounds. We first ask the planner to carry out an initial land-use plan. To deal with the different facilities needs of residents, we initiate a discussion among the residents in each community about the plan, where residents provide feedback based on their profiles. Furthermore, to improve the efficiency of discussion, we adopt a fishbowl discussion mechanism, where part of the residents discuss and the rest of them act as listeners in each round. Finally, we let the planner modify the plan based on residents' feedback. We deploy our method on two real-world regions in Beijing. Experiments show that our method achieves state-of-the-art performance in residents satisfaction and inclusion metrics, and also outperforms human experts in terms of service accessibility and ecology metrics.

Related papers

• MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility [52.0930915607703]
  Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Micromobility enabled by AI for short-distance travel in public urban spaces plays a crucial component in the future transportation system. We present MetaUrban, a compositional simulation platform for the AI-driven urban micromobility research.
  arXiv  Detail & Related papers  (2024-07-11T17:56:49Z)
• PDDLEGO: Iterative Planning in Textual Environments [56.12148805913657]
  Planning in textual environments has been shown to be a long-standing challenge even for current models. We propose PDDLEGO that iteratively construct a planning representation that can lead to a partial plan for a given sub-goal. We show that plans produced by few-shot PDDLEGO are 43% more efficient than generating plans end-to-end on the Coin Collector simulation.
  arXiv  Detail & Related papers  (2024-05-30T08:01:20Z)
• PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval [8.345858904808873]
  General-purpose large language models often struggle to meet the specific needs of planners. PlanGPT is the first specialized Large Language Model tailored for urban and spatial planning.
  arXiv  Detail & Related papers  (2024-02-29T15:41:20Z)
• TravelPlanner: A Benchmark for Real-World Planning with Language Agents [63.199454024966506]
  We propose TravelPlanner, a new planning benchmark that focuses on travel planning, a common real-world planning scenario. It provides a rich sandbox environment, various tools for accessing nearly four million data records, and 1,225 meticulously curated planning intents and reference plans. Comprehensive evaluations show that the current language agents are not yet capable of handling such complex planning tasks-even GPT-4 only achieves a success rate of 0.6%.
  arXiv  Detail & Related papers  (2024-02-02T18:39:51Z)
• Large language model empowered participatory urban planning [5.402147437950729]
  This research introduces an innovative urban planning approach integrating Large Language Models (LLMs) within the participatory process. The framework, based on the crafted LLM agent, consists of role-play, collaborative generation, and feedback, solving a community-level land-use task catering to 1000 distinct interests.
  arXiv  Detail & Related papers  (2024-01-24T10:50:01Z)
• LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning [65.86754998249224]
  We develop a novel hybrid planner that leverages a conventional rule-based planner in conjunction with an LLM-based planner. Our approach navigates complex scenarios which existing planners struggle with, produces well-reasoned outputs while also remaining grounded through working alongside the rule-based approach.
  arXiv  Detail & Related papers  (2023-12-30T02:53:45Z)
• AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning [8.363841553742912]
  We introduce a Consensus-based Multi-Agent Reinforcement Learning framework for real-world land use readjustment. This framework serves participatory urban planning, allowing diverse intelligent agents as stakeholder representatives to vote for preferred land use types. By integrating Multi-Agent Reinforcement Learning, our framework ensures that participatory urban planning decisions are more dynamic and adaptive to evolving community needs.
  arXiv  Detail & Related papers  (2023-10-25T17:04:11Z)
• AdaPlanner: Adaptive Planning from Feedback with Language Models [56.367020818139665]
  Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback. To mitigate hallucination, we develop a code-style LLM prompt structure that facilitates plan generation across a variety of tasks, environments, and agent capabilities.
  arXiv  Detail & Related papers  (2023-05-26T05:52:27Z)
• Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning [57.91323079939641]
  We develop a novel human-instructed deep hierarchical generative model to generate optimal urban plans. The first stage is to label the grids of a target area with latent functionalities to discover functional zones. The second stage is to perceive the planning requirements to form urban functionality projections. The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations.
  arXiv  Detail & Related papers  (2022-12-01T23:06:41Z)
• Dynamic Urban Planning: an Agent-Based Model Coupling Mobility Mode and Housing Choice. Use case Kendall Square [5.620109882646823]
  This paper presents an agent-based model that characterizes citizens' behavioural patterns with respect to housing and mobility choice. The realistic identification and representation of the criteria that affect this decision-making process will help understand and evaluate the impacts of potential housing incentives.
  arXiv  Detail & Related papers  (2021-06-28T10:54:44Z)
• Reimagining City Configuration: Automated Urban Planning via Adversarial Learning [28.930624100994514]
  Urban planning refers to the efforts of designing land-use configurations. Recent advance of deep learning motivates us to ask: can machines learn at a human capability to automatically and quickly calculate land-use configuration.
  arXiv  Detail & Related papers  (2020-08-22T21:15:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.