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<p>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 (<i>r</i> = −&#xa0;0.91 and <i>r</i> = −&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月20日

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  • Mehdi Gholamnia,
  • Nasim Eslamirad,
  • Payam Sajadi,
  • Ziba Zarin,
  • Francesco Pilla

摘要

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.