Transforming urban dynamics: Harnessing large language models for smarter mobility

Author

Hao XUE
Ming JIN
Shirui PAN
Flora SALIM
Guansong PANG, Singapore Management UniversityFollow

Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2025

Abstract

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.

Keywords

Urban Dynamics, Large Language Models, Transport System, Urban Planning

Discipline

Artificial Intelligence and Robotics | Transportation | Urban Studies and Planning

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Intelligent Systems

Volume

40

Issue

2

First Page

5

Last Page

7

ISSN

1541-1672

Identifier

10.1109/MIS.2025.3544937

Publisher

Institute of Electrical and Electronics Engineers

Citation

XUE, Hao; JIN, Ming; PAN, Shirui; SALIM, Flora; and PANG, Guansong. Transforming urban dynamics: Harnessing large language models for smarter mobility. (2025). IEEE Intelligent Systems. 40, (2), 5-7.
Available at: https://ink.library.smu.edu.sg/sis_research/10156

Copyright Owner and License

Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

https://doi.org/10.1109/MIS.2025.3544937