Volume 25 Issue 1
Feb.  2025
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XIAO Jian-li, QIU Xue, ZHANG Yang, SU Hai-sheng, LI Zhi-peng, ZHANG Chuan-ming. Review on large language models in transportation[J]. Journal of Traffic and Transportation Engineering, 2025, 25(1): 8-28. doi: 10.19818/j.cnki.1671-1637.2025.01.002
Citation: XIAO Jian-li, QIU Xue, ZHANG Yang, SU Hai-sheng, LI Zhi-peng, ZHANG Chuan-ming. Review on large language models in transportation[J]. Journal of Traffic and Transportation Engineering, 2025, 25(1): 8-28. doi: 10.19818/j.cnki.1671-1637.2025.01.002
shu

Review on large language models in transportation

doi: 10.19818/j.cnki.1671-1637.2025.01.002
  • 1.

    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

  • 2.

    Shanghai Urban-Rural Construction and Transportation Development Research Institute (Shanghai Digital Urban Management Center), Shanghai 200032, China

  • 3.

    School of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China

  • 4.

    School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China

  • 5.

    Beijing Baidu Netcom Science and Technology Co., Ltd., Beijing 100085, China

Funds:

National Natural Science Foundation of China 92370201

National Natural Science Foundation of China 61603257

Fundamental Research Funds for the Central Universities 22120230311

More Information
  • Corresponding author: XIAO Jian-li(1982-), male, professor, PhD, audyxiao@sjtu.edu.cn
  • Received Date: 2024-07-25
  • Publish Date: 2025-02-25
    Abstract
  • The promotion of large language models(LLMs) in transportation was further discussed. Their great potentials were demonstrated in improving traffic management and control, enhancing traffic safety, and advancing autonomous driving. The basic concepts and development of LLMs, large vision models, and large multimodal models were systematically expounded. Some LLMs in transportation were summarized in terms of their structures and training methods. The major applications of LLMs in transportation were discussed, such as traffic management and control, traffic safety, and autonomous driving. Research results show that, when it comes to traffic management and control, issues such as traffic signal control and traffic state prediction can be significantly addressed by the application of LLMs. New possibilities are also brought for urban traffic management. Traffic congestion and environmental pollution are both reduced. As for traffic safety, compared with previous models, LLMs application significantly improve the ability to analyze and predict traffic accidents. Through deep learning of historical accident data, the models can identify those areas and time periods with a high incidence of accidents. Consequently, preventive measures can be taken to improve the traffic safety index. In the field of autonomous driving, the shift from traditional models to multimodal autonomous driving models can not only enhance the abilities of decision-making and environmental adaptation, but also provide users with a safer and more comfortable driving experience. The potential and value of LLMs in transportation are explored. Besides, practical suggestions are also offered to create a more intelligent and efficient transportation system, such as reducing the computational cost of LLMs in transportation and improving the real-time performance and reliability of models.

     

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