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.

翻译：准确估计无传感器区域的数据对于理解系统动态至关重要，例如交通状态估计和环境监测。本研究通过将问题构建为时空克里金任务并提出一种新颖的图Transformer模型Kriformer，解决了传感器部署稀疏和数据不可靠带来的挑战。该模型通过挖掘空间和时间相关性，即使在资源有限的情况下，也能估计无传感器位置的数据。Kriformer利用Transformer架构来增强模型的感知范围并解决边缘信息聚合的难题，从而有效捕获时空信息。精心构建的位置编码模块嵌入节点的时空特征，而复杂的时空注意力机制则提高了估计精度。多头空间交互注意力模块捕获观测位置与未观测位置之间细微的空间关系。在训练过程中，随机掩码策略促使模型在部分信息缺失的情况下学习，使时空嵌入和多头注意力机制能够协同捕获位置间的相关性。实验结果表明，Kriformer在未观测位置的表示学习方面表现优异，在两个真实交通速度数据集上验证了其在时空克里金任务中的有效性。