Traffic Flow Prediction Based on Multi-Scale Graph Convolution Enhanced Large Language Model

Abstract

Traffic flow prediction is a critical foundational problem in intelligent transportation systems. Although Large Language Model (LLM) has shown promising potential in time series modeling tasks in recent years, existing LLM-based methods generally overlook the inherent multi-scale characteristics of traffic flow data, which significantly limits their ability to capture complex spatio-temporal evolution patterns. To address this issue, this paper proposes a traffic flow forecasting framework named Multi-Scale Graph Convolution Enhanced Large Language Model (MSG-LLM). Firstly, the traffic flow series are decomposed based on frequency-domain analysis to identify periodic components, enabling the adaptive partitioning of the original series into multiple time scales. Subsequently, adaptive graph structures are constructed at different time scales, and graph convolution operations are introduced to fully characterize the correlation dependencies of traffic nodes during multi-scale spatio-temporal evolution. On this basis, a bidirectional multi-scale fusion module is designed to obtain comprehensive and consistent multi-scale representations through information fusion from fine-to-coarse and coarse-to-fine scales. Finally, the fused multi-scale spatio-temporal features are integrated into a partially frozen pre-trained large language model. By fine-tuning only task-specific parameters, this approach preserves the LLM’s general time series modeling capabilities while effectively reducing training costs and mitigating overfitting risks. Extensive experimental results on the PEMS04 and PEMS08 datasets demonstrate that the proposed method significantly outperforms existing mainstream models in both short-term and long-term traffic flow forecasting tasks, validating the effectiveness and strong generalization ability of MSG-LLM in modeling complex traffic systems.