  Traffic prediction constitutes a pivotal facet within the purview of
Intelligent Transportation Systems (ITS), and the attainment of highly precise
predictions holds profound significance for efficacious traffic management. The
precision of prevailing deep learning-driven traffic prediction models
typically sees an upward trend with a rise in the volume of training data.
However, the procurement of comprehensive spatiotemporal datasets for traffic
is often fraught with challenges, primarily stemming from the substantial costs
associated with data collection and retention. Consequently, developing a model
that can achieve accurate predictions and good generalization ability in areas
with limited historical traffic data is a challenging problem. It is noteworthy
that the rapidly advancing pretrained Large Language Models (LLMs) of recent
years have demonstrated exceptional proficiency in cross-modality knowledge
transfer and few-shot learning. Recognizing the sequential nature of traffic
data, similar to language, we introduce TPLLM, a novel traffic prediction
framework leveraging LLMs. In this framework, we construct a sequence embedding
layer based on Convolutional Neural Networks (CNNs) and a graph embedding layer
based on Graph Convolutional Networks (GCNs) to extract sequence features and
spatial features, respectively. These are subsequently integrated to form
inputs that are suitable for LLMs. A Low-Rank Adaptation (LoRA) fine-tuning
approach is applied to TPLLM, thereby facilitating efficient learning and
minimizing computational demands. Experiments on two real-world datasets
demonstrate that TPLLM exhibits commendable performance in both full-sample and
few-shot prediction scenarios, effectively supporting the development of ITS in
regions with scarce historical traffic data.
