PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks
arXiv:2606.09872v1 Announce Type: new Abstract: Traffic forecasting is a fundamental component of intelligent transportation systems, yet remains challenging in real-world settings due to irregular sensor distributions and the high computational cost of modeling large-scale spatiotemporal dependencies. In practical traffic networks, sensors are unevenly distributed across regions, leading to non-uniform spatial structures that limit the effectiveness and scalability of existing graph-based and attention-based models. To address these challenges, we propose PatchSTG, a patch-based spatiotempora
The continuous growth of urban populations and the demand for more efficient transportation systems drive ongoing research into advanced traffic forecasting methods.
Improved traffic forecasting, especially for irregular sensor networks, can significantly enhance urban planning, logistics, and the development of intelligent transportation systems.
This research proposes a methodology (PatchSTG) that addresses scalability and irregularity challenges in spatiotemporal graph transformers for traffic prediction, potentially making these systems more viable for real-world large-scale applications.
- · Smart city initiatives
- · Logistics and delivery companies
- · Transportation infrastructure providers
- · Traffic congestion
- · Inefficient routing algorithms
More accurate and scalable traffic predictions will enable dynamic traffic management systems.
Optimized traffic flow reduces fuel consumption and environmental impact, while also decreasing commute times for individuals.
The underlying methodology could be adapted to other irregular sensor network problems, such as climate modeling or utility grid management.
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Read at arXiv cs.LG