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 ins...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 high-light avenues for advancing LLM-based mobility systems through multi-modal data integration and developing robust,lightweight models.展开更多
基金National Natural Science Foundation of China(Grant Nos.72431009,72171210,and 72350710798)Zhejiang Provincial Natural Science Foundation of China(LZ23E080002)Smart Urban Future(SURF)Laboratory,Zhejiang Province,China.
文摘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 high-light avenues for advancing LLM-based mobility systems through multi-modal data integration and developing robust,lightweight models.
