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    <abstract>Traffic flow forecasting aims to predict future traffic flows based on historical traffic conditions and the road network. It is an important problem in intelligent transportation systems, with a plethora of methods being proposed. Existing efforts mainly focus on capturing and utilizing spatio-temporal dependencies to predict future traffic flows. Though promising, they fall short in adapting to test-time environmental changes in traffic conditions. To tackle this challenge, we propose to introduce large language models (LLMs) to help traffic flow forecasting and design a novel method named Large Language Model Enhanced Traffic Flow Predictor (LEAF). LEAF adopts two branches, capturing different spatio-temporal relations using graph and hypergraph structures, respectively. The two branches are first pre-trained individually, and during test time, they yield different predictions. Based on these predictions, a large language model is used to select the most likely result. Then, a ranking loss is applied as the learning objective to enhance the prediction ability of the two branches. Extensive experiments on several datasets demonstrate the effectiveness of LEAF. Our code is available at https://github.com/YushengZhao/LEAF.</abstract>
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%0 Conference Proceedings
%T Embracing Large Language Models in Traffic Flow Forecasting
%A Zhao, Yusheng
%A Luo, Xiao
%A Wen, Haomin
%A Xiao, Zhiping
%A Ju, Wei
%A Zhang, Ming
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhao-etal-2025-embracing
%X Traffic flow forecasting aims to predict future traffic flows based on historical traffic conditions and the road network. It is an important problem in intelligent transportation systems, with a plethora of methods being proposed. Existing efforts mainly focus on capturing and utilizing spatio-temporal dependencies to predict future traffic flows. Though promising, they fall short in adapting to test-time environmental changes in traffic conditions. To tackle this challenge, we propose to introduce large language models (LLMs) to help traffic flow forecasting and design a novel method named Large Language Model Enhanced Traffic Flow Predictor (LEAF). LEAF adopts two branches, capturing different spatio-temporal relations using graph and hypergraph structures, respectively. The two branches are first pre-trained individually, and during test time, they yield different predictions. Based on these predictions, a large language model is used to select the most likely result. Then, a ranking loss is applied as the learning objective to enhance the prediction ability of the two branches. Extensive experiments on several datasets demonstrate the effectiveness of LEAF. Our code is available at https://github.com/YushengZhao/LEAF.
%R 10.18653/v1/2025.findings-acl.424
%U https://aclanthology.org/2025.findings-acl.424/
%U https://doi.org/10.18653/v1/2025.findings-acl.424
%P 8108-8123

Markdown (Informal)

[Embracing Large Language Models in Traffic Flow Forecasting](https://aclanthology.org/2025.findings-acl.424/) (Zhao et al., Findings 2025)

• Embracing Large Language Models in Traffic Flow Forecasting (Zhao et al., Findings 2025)

ACL

• Yusheng Zhao, Xiao Luo, Haomin Wen, Zhiping Xiao, Wei Ju, and Ming Zhang. 2025. Embracing Large Language Models in Traffic Flow Forecasting. In Findings of the Association for Computational Linguistics: ACL 2025, pages 8108–8123, Vienna, Austria. Association for Computational Linguistics.