Spatial-Temporal Large Language Model for Traffic Prediction
CoRR(2024)
摘要
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.
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