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

• URL: http://arxiv.org/abs/2404.05774v1
• Date: Mon, 8 Apr 2024 03:38:52 GMT
• Title: STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting
• Authors: Zhengyang Zhao, Haitao Yuan, Nan Jiang, Minxiao Chen, Ning Liu, Zengxiang Li,
• Abstract summary: 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.
• Score: 12.809369696629625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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