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.

翻译：交通预测是智能交通系统的核心组成部分，旨在利用历史数据预测特定位置未来的交通特征。尽管现有的交通预测模型通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显著提升。近年来，大语言模型在时间序列分析领域展现出卓越的能力。与现有模型不同，大语言模型主要通过参数扩展和大规模预训练实现性能提升，同时保持其基础结构不变。受这些进展的启发，我们提出了一种用于交通预测的时空大语言模型。在该模型中，我们将每个位置的时间步定义为标记，并设计了一种时空嵌入来学习这些标记的空间位置和全局时间模式。此外，我们通过融合卷积将时空嵌入整合到每个标记中，形成统一的时空表示。进一步地，我们创新性地提出了一种部分冻结注意力策略，使大语言模型能够适应交通预测任务，捕捉全局时空依赖关系。在真实交通数据集上的综合实验表明，ST-LLM是一种强大的时空学习器，其性能优于当前最先进的模型。值得注意的是，ST-LLM在少样本和零样本预测场景中也表现出鲁棒的性能。代码已公开于 https://github.com/ChenxiLiu-HNU/ST-LLM。