Papers › Dynamic Trend Fusion Module for Traffic Flow Prediction

Dynamic Trend Fusion Module for Traffic Flow Prediction

18 Jan 2025arXiv:2501.10796archive 2025-07-28

Jing Chen, Haocheng Ye, Zhian Ying, Yuntao Sun, Wenqiang Xu

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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Code

hitplz/dstrformer officialmentioned in papermentioned on GitHubpytorchApache-2.0 report

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Tasks

Prediction Traffic Prediction

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Traffic Prediction	PeMS04	DTRformer	12 Steps MAE	18	#3 of 12	Archive leaderboard	report
Traffic Prediction	PeMS08	DTRformer	MAE@1h	13.17	#1 of 13	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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