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This is a Quantitative Study study.

Exploring the potential of large language models (LLMs) in analyzing passengers’ perceptions of transit service quality

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LLMs significantly improve transit service quality assessment, enhancing responsiveness to commuter feedback.
Performance metrics show LLMs excel in classification tasks compared to traditional models, highlighting their effectiveness.
Utilizing a Weibo dataset, the study demonstrates LLMs' application for understanding user experiences in urban transit systems.
Findings reveal potential challenges, such as hallucinations, emphasizing the need for refined NLP techniques in transportation.

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This analysis uncovers LLMs' impact on transit service quality in urban planning, implying better user experience.

Luo et al. (2025) studied this question.

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