A large language model-based chatbot system framework for urban planners
Xiaoxin Zhou, Byeonghwa Jeong, Karen Chapple · Expert Systems with Applications · 2025
Currently, urban planners, private developers, and related stakeholders face significant challenges due to the complexity and dispersion of municipal bylaws and zoning regulations across jurisdictions. This study proposes a novel Large Language Model (LLM)-based chatbot framework 1 1 GitHub: https://github.com/zhoux121/School_of_cities_AI designed to streamline access to and interpretation of these regulations. The framework integrates a hybrid database system, combining pre-collected static data from official sources with dynamically scraped real-time content, ensuring comprehensive and up-to-date information retrieval. Leveraging GPT-3.5-turbo for hierarchical text preprocessing and a dual retrieval mechanism (BM25 and cosine similarity with Reciprocal Rank Fusion), the framework achieves strong accuracy in answering regulatory queries. Evaluated across six Canadian cities, the model demonstrated 72–92% accuracy on binary questions and 40–70% on continuous questions, outperforming baseline models such as GPT-4o and LLaMA 3.2. This approach not only reduces administrative burdens but also enhances accessibility for stakeholders, offering a scalable solution for navigating fragmented urban policy landscapes.