Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast
arXiv:2505.13102v4 Announce Type: replace-cross Abstract: Unlike conventional "black-box" transformers with classical self-attention mechanism, we build a lightweight and interpretable transformer-like neural net by unrolling a mixed-graph-based optimization algorithm to forecast traffic with spatial and temporal dimensions. We construct two graphs: an undirected graph $\mathcal{G}^u$ capturing spatial correlations across geography, and a directed graph $\mathcal{G}^d$ capturing sequential relationships over time. We predict future samples of signal $\mathbf{x}$, assuming it is "smooth" with r
The continuous evolution of AI models and the increasing demand for efficient and interpretable forecasting tools are driving innovations in transformer architectures.
Developing lightweight and interpretable AI models enables broader deployment in resource-constrained environments and fosters trust in critical applications like infrastructure management.
This approach offers an alternative to 'black-box' AI models, potentially improving explainability and reducing computational overhead for real-world predictions.
- · Traffic management agencies
- · Smart city initiatives
- · Researchers in explainable AI
- · Overly complex AI solutions
- · High-compute dependent forecasting models
More accurate and resource-efficient traffic predictions improve urban planning and reduce congestion.
Increased adoption of interpretable AI could lead to new regulatory frameworks emphasizing model transparency.
The methodology might extend to other spatio-temporal forecasting domains, such as climate modeling or supply chain logistics, for greater clarity and efficiency.
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Read at arXiv cs.AI