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.

翻译：交通预测作为智能交通系统的关键组成部分，旨在利用历史数据预测特定位置的未来交通状况。尽管现有交通预测模型常侧重于开发复杂的神经网络结构，但其预测精度并未得到相应提升。近期，大语言模型（LLMs）在时间序列分析中展现出卓越能力。与现有模型不同，LLMs主要通过参数扩展和大规模预训练取得进展，同时保持其基本结构不变。本文提出一种面向交通预测的时空大语言模型（ST-LLM）。具体而言，ST-LLM将每个位置的时间步重新定义为令牌，并引入时空嵌入模块学习令牌的空间位置和全局时间表征。随后这些表征被融合，为每个令牌提供统一的时空信息。此外，我们提出一种新颖的LLM部分冻结注意力策略，旨在捕捉交通预测的时空依赖性。在真实交通数据集上的综合实验表明，ST-LLM的性能超越了现有最优模型。值得注意的是，ST-LLM在少样本和零样本预测场景中也展现出稳健的性能。