Dynamic Trend Fusion Module for Traffic Flow Prediction

• URL: http://arxiv.org/abs/2501.10796v1
• Date: Sat, 18 Jan 2025 15:16:47 GMT
• Title: Dynamic Trend Fusion Module for Traffic Flow Prediction
• Authors: Jing Chen, Haocheng Ye, Zhian Ying, Yuntao Sun, Wenqiang Xu,
• Abstract summary: Existing methods often model spatial and temporal correlations separately failing to effectively fuse them. We propose Dynamic Spatial-Temporal Trend Transformer DST2 to fuse dynamic correlations for learning multi-view dynamic features of traffic networks. Experiments on four real-world traffic datasets demonstrate that our framework achieves state-of-the-art performance.
• Score: 9.650380389159459
• License:
• Abstract: Accurate traffic flow prediction is essential for applications like transport logistics but remains challenging due to complex spatio-temporal correlations and non-linear traffic patterns. Existing methods often model spatial and temporal dependencies separately, failing to effectively fuse them. To overcome this limitation, the Dynamic Spatial-Temporal Trend Transformer DST2former is proposed to capture spatio-temporal correlations through adaptive embedding and to fuse dynamic and static information for learning multi-view dynamic features of traffic networks. The approach employs the Dynamic Trend Representation Transformer (DTRformer) to generate dynamic trends using encoders for both temporal and spatial dimensions, fused via Cross Spatial-Temporal Attention. Predefined graphs are compressed into a representation graph to extract static attributes and reduce redundancy. Experiments on four real-world traffic datasets demonstrate that our framework achieves state-of-the-art performance.

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