mlsanityData to 7 Oct 2026

Dynamic Trend Fusion Module for Traffic Flow Prediction

arXiv 2501.10796 ↗First posted 18 Jan 2025cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 19 of its 40 references that are in the map. First posted on arXiv 18 Jan 2025.

Builds on

19 of 40 references.

References · 19

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7 more
  1. 2018GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphscited by 164
  2. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
  3. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 156
  4. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 135
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  7. 2022Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecastingcited by 17

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Lightweight Spatio-Temporal Attention Network with Graph Embedding and Rotational Position Encoding for Traffic Forecastingcited by 1
  2. 2025Efficient Traffic State Prediction With Dynamic Joint Spatio-Temporal Relation Inferencecited by 0
  3. 2025MSTIM: A MindSpore-Based Model for Traffic Flow Predictioncited by 0
  4. 2026Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performancecited by 0
  5. 2026Structure-Guided Spatiotemporal Attention Graph Neural Network for Traffic Flow Predictioncited by 0