Spatial-Temporal Feedback Diffusion Guidance for Controlled Traffic Imputation
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866912809781035008 |
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| author | Mao, Xiaowei Ding, Huihu Lin, Yan Wu, Tingrui Guo, Shengnan Qiu, Dazhuo Fang, Feiling Hu, Jilin Wan, Huaiyu |
| author_facet | Mao, Xiaowei Ding, Huihu Lin, Yan Wu, Tingrui Guo, Shengnan Qiu, Dazhuo Fang, Feiling Hu, Jilin Wan, Huaiyu |
| contents | Imputing missing values in spatial-temporal traffic data is essential for intelligent transportation systems. Among advanced imputation methods, score-based diffusion models have demonstrated competitive performance. These models generate data by reversing a noising process, using observed values as conditional guidance. However, existing diffusion models typically apply a uniform guidance scale across both spatial and temporal dimensions, which is inadequate for nodes with high missing data rates. Sparse observations provide insufficient conditional guidance, causing the generative process to drift toward the learned prior distribution rather than closely following the conditional observations, resulting in suboptimal imputation performance.
To address this, we propose FENCE, a spatial-temporal feedback diffusion guidance method designed to adaptively control guidance scales during imputation. First, FENCE introduces a dynamic feedback mechanism that adjusts the guidance scale based on the posterior likelihood approximations. The guidance scale is increased when generated values diverge from observations and reduced when alignment improves, preventing overcorrection. Second, because alignment to observations varies across nodes and denoising steps, a global guidance scale for all nodes is suboptimal. FENCE computes guidance scales at the cluster level by grouping nodes based on their attention scores, leveraging spatial-temporal correlations to provide more accurate guidance. Experimental results on real-world traffic datasets show that FENCE significantly enhances imputation accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_04572 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Spatial-Temporal Feedback Diffusion Guidance for Controlled Traffic Imputation Mao, Xiaowei Ding, Huihu Lin, Yan Wu, Tingrui Guo, Shengnan Qiu, Dazhuo Fang, Feiling Hu, Jilin Wan, Huaiyu Machine Learning Artificial Intelligence Imputing missing values in spatial-temporal traffic data is essential for intelligent transportation systems. Among advanced imputation methods, score-based diffusion models have demonstrated competitive performance. These models generate data by reversing a noising process, using observed values as conditional guidance. However, existing diffusion models typically apply a uniform guidance scale across both spatial and temporal dimensions, which is inadequate for nodes with high missing data rates. Sparse observations provide insufficient conditional guidance, causing the generative process to drift toward the learned prior distribution rather than closely following the conditional observations, resulting in suboptimal imputation performance. To address this, we propose FENCE, a spatial-temporal feedback diffusion guidance method designed to adaptively control guidance scales during imputation. First, FENCE introduces a dynamic feedback mechanism that adjusts the guidance scale based on the posterior likelihood approximations. The guidance scale is increased when generated values diverge from observations and reduced when alignment improves, preventing overcorrection. Second, because alignment to observations varies across nodes and denoising steps, a global guidance scale for all nodes is suboptimal. FENCE computes guidance scales at the cluster level by grouping nodes based on their attention scores, leveraging spatial-temporal correlations to provide more accurate guidance. Experimental results on real-world traffic datasets show that FENCE significantly enhances imputation accuracy. |
| title | Spatial-Temporal Feedback Diffusion Guidance for Controlled Traffic Imputation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.04572 |