Fine-grained Urban Flow Inference with Unobservable Data via Space-Time Attraction Learning
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE International Conference on Data Mining (ICDM), 2024, 00, pp. 1367-1372
- Issue Date:
- 2024-02-05
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Fine-grained Urban Flow Inference with Unobservable Data via Space-Time Attraction Learning.pdf | Accepted version | 1.22 MB |
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Fine grained urban flow inference focuses on inferring fine grained urban flows based solely on coarse grained observations which is essential for the city management and transportation services However most of the existing methods assume that partial urban flows in coarse grained regions cannot be observable In this study we propose a multi task framework known as UrbanSTA with space time attraction learning to estimate missing values in coarse grained urban flow map and forecast fine grained urban flows simultaneously Specifically UrbanSTA comprises two parts the flow completion network STA and the fine grained flow inference network FIN STA captures space time features with a separable space time attention encoder and recovers the missing flow features with a decoder FIN directly uses complete coarse grained flow features for further decoding and reconstructs fine grained flow features based on the complex associations between coarse and fine grained urban flows relying on upsampling constraints Extensive experiments conducted on two real world datasets demonstrate that our proposed model yields the best results compared to other state of the art methods The source code has been provided at https github com Wangzheaos UrbanSTA
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