Do We Really Need Transformers for Global Spatial Information Extraction in Traffic Forecasting?
Quick Answer
This study questions the necessity of high-degree adaptive attention in traffic forecasting, showing that simple global aggregation can achieve similar performance.
Quick Take
In tests across six benchmarks, uniform full-range mixing and standard spatial attention yielded comparable MAE results, with only a 0.14% difference, while simplifying complexity from O(N²) to O(N).
Key Points
- Uniform full-range mixing reduced spatial mixing complexity from O(N²) to O(N).
- Both mixing methods achieved lower MAE on three datasets with minimal performance difference.
- Spatial attention's value is dataset-dependent, needing stable gains beyond a global background.
- The study provides a controlled ablation framework for testing global interactions.
- Source code for the study is publicly available for further research.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Existing traffic forecasting models commonly focus on extracting spatial dependencies, particularly global spatial information, which characterizes the representations obtained through interactions between each individual node and all nodes across the traffic network. However, the underlying mechanism by which such global information is modeled and extracted remains insufficiently investigated. Whether global information must be extracted by high-degree-of-freedom adaptive attention or can be captured by a simple global aggregation operator remains unclear. For this purpose, we design a controlled ablation framework that replaces only the spatial mixing module to test attention-based global interaction. Across six traffic benchmarks, uniform full-range mixing and standard spatial attention each achieve lower MAE on three datasets, with only a 0.14% difference in mean MAE, while the former reduces node-scale spatial mixing complexity from O(N2) to O(N). Mechanism analysis further decomposes spatial attention into a row-uniform global background and a non-uniform residual. The residual shows dataset-dependent marginal value, suggesting that spatial attention should be justified by stable gains beyond a row-uniform global background. The corresponding source code is publicly available at: this https URL
| Comments: | 15 pages, 6 figures. Source code available at this https URL |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.12462 [cs.AI] |
| (or arXiv:2607.12462v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.12462 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Siyao Zhang [view email]
[v1]
Tue, 14 Jul 2026 07:44:01 UTC (813 KB)
— Originally published at arxiv.org
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