A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Enhancing participatory planning with ChatGPT-assisted planning support systems: a hypothetical case study in Seoul
Recent advancements in technology for planning support have led to increased interest in participatory planning support systems (PPSSs). However, existing PPSSs often struggle to facilitate higher levels of public participation due to limitations in their practical usefulness. Emerging large language models (LLMs) like ChatGPT, along with artificial intelligence (AI) technologies such as deep generative methods, offer new opportunities to enhance PPSS, though this potential has yet to be fully explored. This study aims to address these gaps by integrating LLMs, specifically ChatGPT, into a new web-based PPSS platform. The platform operates as a multi-agent system with five key components: users, process, agents, knowledge, and tools. Stakeholders engage with ChatGPT-enabled personalized agents that are supported by a project-specific knowledge base. These agents understand user preferences, concerns, and needs, and call upon task agents, also powered by ChatGPT, to execute tasks, including applying deep generative tools that enable stakeholders to create their own designs. A workflow agent coordinates the overall process, facilitating the sharing of information, data, opinions, and designs to promote communication and build consensus among stakeholders. The platform was tested in a hypothetical sustainable urban regeneration case, the New Seollo Project in Seoul. Compared to the actual Seoullo project, which faced substantial criticism, the simulated results suggest the platform’s potential to significantly improve participation and generate better design solutions. This new PPSS platform enhances usefulness by improving stakeholder communication and empowering the public to contribute comprehensive, inclusive, and creative solutions for sustainable urban development.
Enhancing participatory planning with ChatGPT-assisted planning support systems: a hypothetical case study in Seoul
Recent advancements in technology for planning support have led to increased interest in participatory planning support systems (PPSSs). However, existing PPSSs often struggle to facilitate higher levels of public participation due to limitations in their practical usefulness. Emerging large language models (LLMs) like ChatGPT, along with artificial intelligence (AI) technologies such as deep generative methods, offer new opportunities to enhance PPSS, though this potential has yet to be fully explored. This study aims to address these gaps by integrating LLMs, specifically ChatGPT, into a new web-based PPSS platform. The platform operates as a multi-agent system with five key components: users, process, agents, knowledge, and tools. Stakeholders engage with ChatGPT-enabled personalized agents that are supported by a project-specific knowledge base. These agents understand user preferences, concerns, and needs, and call upon task agents, also powered by ChatGPT, to execute tasks, including applying deep generative tools that enable stakeholders to create their own designs. A workflow agent coordinates the overall process, facilitating the sharing of information, data, opinions, and designs to promote communication and build consensus among stakeholders. The platform was tested in a hypothetical sustainable urban regeneration case, the New Seollo Project in Seoul. Compared to the actual Seoullo project, which faced substantial criticism, the simulated results suggest the platform’s potential to significantly improve participation and generate better design solutions. This new PPSS platform enhances usefulness by improving stakeholder communication and empowering the public to contribute comprehensive, inclusive, and creative solutions for sustainable urban development.
Enhancing participatory planning with ChatGPT-assisted planning support systems: a hypothetical case study in Seoul
Quan, Steven Jige (author) / Lee, Seojung (author)
International Journal of Urban Sciences ; 29 ; 89-122
2025-01-02
34 pages
Article (Journal)
Electronic Resource
English
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