Traffic prediction, a critical component for intelligent transportation systems, endeavors to foresee future traffic at specific locations using historical data. Although existing traffic prediction models often emphasize developing complex neural network structures, their accuracy has not seen improvements accordingly. Recently, Large Language Models (LLMs) have shown outstanding capabilities in time series analysis. Differing from existing models, LLMs progress mainly through parameter expansion and extensive pre-training while maintaining their fundamental structures. In this paper, we propose a Spatial-Temporal Large Language Model (ST-LLM) for traffic prediction. Specifically, ST-LLM redefines the timesteps at each location as tokens and incorporates a spatial-temporal embedding module to learn the spatial location and global temporal representations of tokens. Then these representations are fused to provide each token with unified spatial and temporal information. Furthermore, we propose a novel partially frozen attention strategy of the LLM, which is designed to capture spatial-temporal dependencies for traffic prediction. Comprehensive experiments on real traffic datasets offer evidence that ST-LLM outperforms state-of-the-art models. Notably, the ST-LLM also exhibits robust performance in both few-shot and zero-shot prediction scenarios.

提出了一种基于空间-时间大型语言模型（ST-LLM）的交通预测方法，通过重新定义位置上的时间步长作为标记，并结合空间-时间嵌入模块来学习标记的空间位置和全局时间表示，在提供统一的空间和时间信息的基础上，进一步提出了一种部分冻结的LLM关注策略，用于捕捉交通预测的空间-时间依赖关系。综合实验表明ST-LLM在真实交通数据集上优于最先进的模型，同时在少样本和零样本预测场景中也表现出鲁棒性。

时空大型语言模型用于交通预测