Kriformer: A Novel Spatiotemporal Kriging Approach Based on Graph Transformers

• URL: http://arxiv.org/abs/2409.14906v1
• Date: Mon, 23 Sep 2024 11:01:18 GMT
• Title: Kriformer: A Novel Spatiotemporal Kriging Approach Based on Graph Transformers
• Authors: Renbin Pan, Feng Xiao, Hegui Zhang, Minyu Shen,
• Abstract summary: This study addresses posed by sparse sensor deployment and unreliable data by framing the problem as an environmental challenge. A graphkriformer model, Kriformer, estimates data at locations without sensors by mining spatial and temporal correlations, even with limited resources.
• Score: 5.4381914710364665
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Graph Masked Autoencoder for Spatio-Temporal Graph Learning [38.085962443141206]
  In urban sensing applications, effective-temporal prediction frameworks play a crucial role in traffic analysis, human mobility evaluations and crime prediction. The presence of data noise and sparsity in spatial and temporal data presents significant challenges for existing neural network models in learning robust representations. We propose a novel self-supervised learning paradigm for effective-temporal data augmentation.
  arXiv  Detail & Related papers  (2024-10-14T07:33:33Z)
• Localized Gaussians as Self-Attention Weights for Point Clouds Correspondence [92.07601770031236]
  We investigate semantically meaningful patterns in the attention heads of an encoder-only Transformer architecture. We find that fixing the attention weights not only accelerates the training process but also enhances the stability of the optimization.
  arXiv  Detail & Related papers  (2024-09-20T07:41:47Z)
• Navigating Spatio-Temporal Heterogeneity: A Graph Transformer Approach for Traffic Forecasting [13.309018047313801]
  Traffic forecasting has emerged as a crucial research area in the development of smart cities. Recent advancements in network modeling for most-temporal correlations are starting to see diminishing returns in performance. To tackle these challenges, we introduce the Spatio-Temporal Graph Transformer (STGormer) We design two straightforward yet effective spatial encoding methods based on the structure and integrate time position into the vanilla transformer to capture-temporal traffic patterns.
  arXiv  Detail & Related papers  (2024-08-20T13:18:21Z)
• Robust Collaborative Perception without External Localization and Clock Devices [52.32342059286222]
  A consistent spatial-temporal coordination across multiple agents is fundamental for collaborative perception. Traditional methods depend on external devices to provide localization and clock signals. We propose a novel approach: aligning by recognizing the inherent geometric patterns within the perceptual data of various agents.
  arXiv  Detail & Related papers  (2024-05-05T15:20:36Z)
• Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting [12.568905377581647]
  Traffic forecasting is one of the most fundamental problems in transportation science and artificial intelligence. Existing methods cannot accurately model both long-term and short-term temporal correlations simultaneously. We propose a novel spatial-temporal neural network framework, which consists of a graph convolutional recurrent module (GCRN) and a global attention module.
  arXiv  Detail & Related papers  (2023-02-25T03:37:00Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecasting [7.232141271583618]
  We propose a novel dynamic multi-graph convolution recurrent network (DMG) to tackle above issues. We use the distance-based graph to capture spatial information from nodes are close in distance. We also construct a novel latent graph which encoded the structure correlations among roads to capture spatial information from nodes are similar in structure.
  arXiv  Detail & Related papers  (2021-12-04T06:51:55Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Spatial-Temporal Conv-sequence Learning with Accident Encoding for Traffic Flow Prediction [17.94199362114272]
  In intelligent transportation system, the key problem of traffic forecasting is how to extract the periodic temporal dependencies and complex spatial correlation. We propose the Spatial-Temporal Conv-sequence Learning (STCL), in which a focused temporal block uses unidirectional convolution to effectively capture short-term periodic temporal dependence. We conduct extensive experiments on large-scale real-world tasks and verify the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2021-05-21T17:43:07Z)
• Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos [78.45050529204701]
  We propose a novel framework to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body.
  arXiv  Detail & Related papers  (2021-04-15T14:32:12Z)
• A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction [74.00750936752418]
  We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC) First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
  arXiv  Detail & Related papers  (2020-03-13T04:35:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.