MagiNet: Mask-Aware Graph Imputation Network for Incomplete Traffic Data
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914826934026240 |
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| author | Zhou, Jianping Lu, Bin Liu, Zhanyu Pan, Siyu Feng, Xuejun Wei, Hua Zheng, Guanjie Wang, Xinbing Zhou, Chenghu |
| author_facet | Zhou, Jianping Lu, Bin Liu, Zhanyu Pan, Siyu Feng, Xuejun Wei, Hua Zheng, Guanjie Wang, Xinbing Zhou, Chenghu |
| contents | Due to detector malfunctions and communication failures, missing data is ubiquitous during the collection of traffic data. Therefore, it is of vital importance to impute the missing values to facilitate data analysis and decision-making for Intelligent Transportation System (ITS). However, existing imputation methods generally perform zero pre-filling techniques to initialize missing values, introducing inevitable noises. Moreover, we observe prevalent over-smoothing interpolations, falling short in revealing the intrinsic spatio-temporal correlations of incomplete traffic data. To this end, we propose Mask-Aware Graph imputation Network: MagiNet. Our method designs an adaptive mask spatio-temporal encoder to learn the latent representations of incomplete data, eliminating the reliance on pre-filling missing values. Furthermore, we devise a spatio-temporal decoder that stacks multiple blocks to capture the inherent spatial and temporal dependencies within incomplete traffic data, alleviating over-smoothing imputation. Extensive experiments demonstrate that our method outperforms state-of-the-art imputation methods on five real-world traffic datasets, yielding an average improvement of 4.31% in RMSE and 3.72% in MAPE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03511 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MagiNet: Mask-Aware Graph Imputation Network for Incomplete Traffic Data Zhou, Jianping Lu, Bin Liu, Zhanyu Pan, Siyu Feng, Xuejun Wei, Hua Zheng, Guanjie Wang, Xinbing Zhou, Chenghu Machine Learning Artificial Intelligence Due to detector malfunctions and communication failures, missing data is ubiquitous during the collection of traffic data. Therefore, it is of vital importance to impute the missing values to facilitate data analysis and decision-making for Intelligent Transportation System (ITS). However, existing imputation methods generally perform zero pre-filling techniques to initialize missing values, introducing inevitable noises. Moreover, we observe prevalent over-smoothing interpolations, falling short in revealing the intrinsic spatio-temporal correlations of incomplete traffic data. To this end, we propose Mask-Aware Graph imputation Network: MagiNet. Our method designs an adaptive mask spatio-temporal encoder to learn the latent representations of incomplete data, eliminating the reliance on pre-filling missing values. Furthermore, we devise a spatio-temporal decoder that stacks multiple blocks to capture the inherent spatial and temporal dependencies within incomplete traffic data, alleviating over-smoothing imputation. Extensive experiments demonstrate that our method outperforms state-of-the-art imputation methods on five real-world traffic datasets, yielding an average improvement of 4.31% in RMSE and 3.72% in MAPE. |
| title | MagiNet: Mask-Aware Graph Imputation Network for Incomplete Traffic Data |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.03511 |