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Transforming Urban Dynamics: Harnessing Large Language Models for Smarter Mobility

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  • NETL
  • NSTL
  • IEEE
Artificial intelligence (AI) has the potential to analyze mobility data and make mobility systems smarter by leveraging diverse data sources such as geospatial data, transportation logs, and real-time sensor data to optimize traffic flow, enhance public transportation systems, and support the development of autonomous vehicles. With the newly emerged generative AI paradigm, exemplified by large language models (LLMs), there is great potential to transform the current AI applications in mobility, transportation, and urban domains. This article provides an overview of recent efforts and aims to shed light on the challenges and future opportunities to facilitate the adaptation of LLMs for smarter mobility systems.

Generative AILarge language modelsSoft sensorsTransformsReal-time systemsGeospatial analysisVehicle dynamicsIntelligent systemsPublic transportationAutonomous vehicles

Hao Xue、Ming Jin、Shirui Pan、Flora Salim

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University of New South Wales, Sydney, N.S.W., Australia

Griffith University, Gold Coast, Queens, Australia

2025

10.1109/MIS.2025.3544937

IEEE intelligent systems

IEEE intelligent systems

ISSN：
年,卷(期)：2025.40(2)
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