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Editorial | Open Access

Harnessing multimodal large language models for traffic knowledge graph generation and decision-making

Senyun KuangaYang LiubXin Wanga( )Xinhua WucYintao Weia( )
School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China
Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, SE-41296, Sweden
Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, 02115, USA
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References

 
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K., 2018. BERT: pre-training of deep bidirectional transformers for language understanding. https://arxiv.org/abs/1810.04805v2.
 
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Wandelt, S., Sun, X., Zhang, J., 2024. GraphCast for solving the air transportation nexus among safety, efficiency, and resilience. Commun. Transp. Res. 4, 100120.

 

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., et al., 2022. Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural Inf. Process. Syst. 35, 24824–24837.

Communications in Transportation Research
Article number: 100146
Cite this article:
Kuang S, Liu Y, Wang X, et al. Harnessing multimodal large language models for traffic knowledge graph generation and decision-making. Communications in Transportation Research, 2024, 4(4): 100146. https://doi.org/10.1016/j.commtr.2024.100146

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Received: 28 August 2024
Accepted: 17 September 2024
Published: 06 November 2024
© 2024 The Author(s).

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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