Summary of Xmtrans: Temporal Attentive Cross-modality Fusion Transformer For Long-term Traffic Prediction, by Huy Quang Ung et al.
xMTrans: Temporal Attentive Cross-Modality Fusion Transformer for Long-Term Traffic Prediction
by Huy Quang Ung, Hao Niu, Minh-Son Dao, Shinya Wada, Atsunori Minamikawa
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This novel paper introduces a temporal attentive cross-modality transformer model for long-term traffic predictions, named xMTrans, which explores the correlations between different data modalities. The proposed model utilizes multi-modal data, comprising target (e.g., traffic congestion) and support (e.g., people flow) modalities to improve prediction accuracy. The authors conducted extensive experiments on real-world datasets, demonstrating the superiority of xMTrans over state-of-the-art methods in long-term traffic predictions. A comprehensive ablation study further analyzes the effectiveness of each module in xMTrans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to predict traffic using multiple types of data. By combining different kinds of information, like people moving around and traffic congestion, the model can make more accurate predictions about what will happen on the roads in the future. The researchers tested their model using real-world data and found that it worked better than other methods for making long-term predictions. |
Keywords
» Artificial intelligence » Multi modal » Transformer
