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

arXiv:2404.05774v1 Announce Type: new
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 …

abstract arxiv cs.ai cs.lg dependencies forecasting framework intelligent intelligent transportation long-term modeling network networks paper prediction spatial temporal them traffic transportation type