STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting

TL;DR

STMGF tackles traffic forecasting by addressing long-range spatial dependencies and long-term periodic patterns. It introduces a spatial-temporal multi-granularity framework that builds hierarchical spatial clusters, performs temporal aggregation, and applies historical pattern matching to refine predictions. The approach achieves state-of-the-art results on real-world datasets and demonstrates universality across different STGNN backbones, offering a scalable method to improve long-term traffic forecasts. By explicitly modeling multi-granularity information and periodicity, STMGF reduces prediction hops and enhances robustness to irregular events.

Abstract

Accurate Traffic Prediction is a challenging task in intelligent transportation due to the spatial-temporal aspects of road networks. The traffic of a road network can be affected by long-distance or long-term dependencies where existing methods fall short in modeling them. In this paper, we introduce a novel framework known as Spatial-Temporal Multi-Granularity Framework (STMGF) to enhance the capture of long-distance and long-term information of the road networks. STMGF makes full use of different granularity information of road networks and models the long-distance and long-term information by gathering information in a hierarchical interactive way. Further, it leverages the inherent periodicity in traffic sequences to refine prediction results by matching with recent traffic data. We conduct experiments on two real-world datasets, and the results demonstrate that STMGF outperforms all baseline models and achieves state-of-the-art performance.

Figures (4)

• Figure 1: Three main difficult problems for traffic forecasting.
• Figure 2: The overall architecture of the proposed STMGF.
• Figure 3: Temporal aggregation and spatial granularity transformation
• Figure 4: Ablation study results.