  Traffic prediction, an essential component for intelligent transportation
systems, endeavours to use historical data to foresee future traffic features
at specific locations. Although existing traffic prediction models often
emphasize developing complex neural network structures, their accuracy has not
improved. Recently, large language models have shown outstanding capabilities
in time series analysis. Differing from existing models, LLMs progress mainly
through parameter expansion and extensive pretraining while maintaining their
fundamental structures. Motivated by these developments, we propose a
Spatial-Temporal Large Language Model (ST-LLM) for traffic prediction. In the
ST-LLM, we define timesteps at each location as tokens and design a
spatial-temporal embedding to learn the spatial location and global temporal
patterns of these tokens. Additionally, we integrate these embeddings by a
fusion convolution to each token for a unified spatial-temporal representation.
Furthermore, we innovate a partially frozen attention strategy to adapt the LLM
to capture global spatial-temporal dependencies for traffic prediction.
Comprehensive experiments on real traffic datasets offer evidence that ST-LLM
is a powerful spatial-temporal learner that outperforms state-of-the-art
models. Notably, the ST-LLM also exhibits robust performance in both few-shot
and zero-shot prediction scenarios. The code is publicly available at
https://github.com/ChenxiLiu-HNU/ST-LLM.
