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<p>Smart city mobility faces mounting challenges as urban mobility systems grow increasingly complex. Large language models (LLMs) have promise in interpreting and processing multi-modal urban data, but issues like model instability, computational inefficiency, and concerns about reliability hinder their implementations. In this Comment, we outline feasible LLM application scenarios, critically evaluate existing challenges, and highlight avenues for advancing LLM-based mobility systems through multi-modal data integration and developing robust, lightweight models.</p>

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Large language model empowered smart city mobility

  • 期刊论文

  • 发表日期：

    2025年2月24日

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  • Yong Chen,
  • Haoyu Zhang,
  • Chuanjia Li,
  • Ben Chi,
  • Xiqun Michael Chen,
  • Jianjun Wu

摘要

Smart city mobility faces mounting challenges as urban mobility systems grow increasingly complex. Large language models (LLMs) have promise in interpreting and processing multi-modal urban data, but issues like model instability, computational inefficiency, and concerns about reliability hinder their implementations. In this Comment, we outline feasible LLM application scenarios, critically evaluate existing challenges, and highlight avenues for advancing LLM-based mobility systems through multi-modal data integration and developing robust, lightweight models.