arXiv:2512.07854v2 Announce Type: replace Abstract: Traffic forecasting task is significant to modern urban management. Recently, there is growing attention on large-scale forecasting, as it better reflects the complexity of real-world traffic networks. However, existing models often exhibit quadratic computational complexity, making them impractical for large-scale real-world scenarios. In this paper, we propose a novel framework, Spatio-Temporal Hierarchical Mixer (HieraMix), which leverages an all-MLP architecture for efficient and effective large-scale traffic forecasting. HieraMix employs
The increasing complexity of urban environments and demand for efficient transportation necessitate more robust and scalable traffic forecasting solutions, pushing AI research in this direction.
This development allows for more accurate and efficient management of large-scale urban traffic systems, enabling better resource allocation and reducing congestion.
Traditional computationally intensive traffic forecasting models are being replaced by more efficient, scalable AI architectures capable of handling real-world complexity.
- · Smart City initiatives
- · Urban planning departments
- · Logistics companies
- · AI researchers in spatio-temporal forecasting
- · Developers of legacy traffic forecasting systems
- · Cities with inefficient traffic management
Improved traffic flow and reduced commuting times in large metropolitan areas.
Reduced carbon emissions from idling vehicles and more efficient public transport networks.
Enhanced urban resilience and economic productivity through optimized infrastructure utilization.
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Read at arXiv cs.LG