Large language model empowered smart city mobility  

作　　者：Yong CHEN Haoyu ZHANG Chuanjia LI Ben CHI Xiqun(Michael)CHEN Jianjun WU 

机构地区：[1]Institute of Intelligent Transportation Systems,College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,China [2]Polytechnic Institute&Institute of Intelligent Transportation Systems,Zhejiang University,Hangzhou 310015,China [3]School of Economics and Management,Dalian University of Technology,Dalian 116024,China

出　　处：《Frontiers of Engineering Management》2025年第1期201-207,共7页工程管理前沿(英文版)

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.

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 high-light avenues for advancing LLM-based mobility systems through multi-modal data integration and developing robust,lightweight models.

关 键 词：smart city mobility large language model urban computing TRANSPORTATION 

分 类 号：F42[经济管理—产业经济]