Kriformer: A Novel Spatiotemporal Kriging Approach Based on Graph Transformers

TL;DR

Kriformer introduces a graph transformer model for accurate spatiotemporal data estimation at unobserved locations, effectively handling sparse sensors and unreliable data in environmental and traffic monitoring.

Contribution

This paper presents Kriformer, a novel graph transformer architecture that enhances spatiotemporal kriging by capturing complex correlations with a new attention mechanism and positional encoding.

Findings

Outperforms existing methods in traffic speed datasets

Effectively estimates data in sensor-less areas

Demonstrates robustness with limited resources

Abstract

Accurately estimating data in sensor-less areas is crucial for understanding system dynamics, such as traffic state estimation and environmental monitoring. This study addresses challenges posed by sparse sensor deployment and unreliable data by framing the problem as a spatiotemporal kriging task and proposing a novel graph transformer model, Kriformer. This model estimates data at locations without sensors by mining spatial and temporal correlations, even with limited resources. Kriformer utilizes transformer architecture to enhance the model's perceptual range and solve edge information aggregation challenges, capturing spatiotemporal information effectively. A carefully constructed positional encoding module embeds the spatiotemporal features of nodes, while a sophisticated spatiotemporal attention mechanism enhances estimation accuracy. The multi-head spatial interaction attention module captures subtle spatial relationships between observed and unobserved locations. During training, a random masking strategy prompts the model to learn with partial information loss, allowing the spatiotemporal embedding and multi-head attention mechanisms to synergistically capture correlations among locations. Experimental results show that Kriformer excels in representation learning for unobserved locations, validated on two real-world traffic speed datasets, demonstrating its effectiveness in spatiotemporal kriging tasks.