Summary of Ccdsreformer: Traffic Flow Prediction with a Criss-crossed Dual-stream Enhanced Rectified Transformer Model, by Zhiqi Shao et al.
CCDSReFormer: Traffic Flow Prediction with a Criss-Crossed Dual-Stream Enhanced Rectified Transformer Model
by Zhiqi Shao, Michael G.H. Bell, Ze Wang, D. Glenn Geers, Xusheng Yao, Junbin Gao
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an innovative Spatio-Temporal Transformer model, Criss-Crossed Dual-Stream Enhanced Rectified Transformer (CCDSReFormer), to improve traffic forecasting for smart traffic systems. The CCDSReFormer combines three novel modules: ReSSA, ReDASA, and ReTSA, which aim to balance computational efficiency with accuracy by focusing on local information and merging spatial-temporal insights. The model outperforms existing approaches in extensive tests on six real-world datasets. Ablation studies confirm the significance of each component’s impact on predictive accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to create a better traffic forecasting model for smart cities. It makes a new type of model that combines three parts: ReSSA, ReDASA, and ReTSA. These parts help the model be more accurate while using less computer power. The new model does better than other models on real-world data. |
Keywords
* Artificial intelligence * Transformer
