Leveraging large language models for citizen-centric urban accessibility analysis: a case study using Airbnb reviews in Dublin

This study presents a novel approach for assessing urban accessibility by leveraging Large Language Models (LLMs) to analyze crowdsourced textual data. Using Airbnb review content from Dublin, we developed a pipeline that extracts perceptions of accessibility—including travel time to public transportation and the city center—directly from user-generated reviews. The FLAN-T5 LLM was tested on a manually curated synthetic test dataset, achieving 84% and 86% accuracy in extracting public transport and city center travel times, respectively. Correlation analysis further confirmed strong negative associations between inferred accessibility by LLM and user-provided location ratings (r = − 0.91 and r = − 0.95), validating the semantic relevance of textual data. This approach highlights how LLMs can transform unstructured review content into structured urban mobility insights, offering a scalable and cost-effective tool for planners. By capturing lived experiences at scale, the method supports more inclusive and data-driven urban policy. The findings contribute to the growing intersection of AI, spatial information science, and participatory urban analytics.