Missing Data Repairs for Traffic Flow With Self-Attention Generative Adversarial Imputation Net
| Autor: | Junyi Zhang, Xiying Li, Pulin Zhang, Yinghao Yu, Weibin Zhang, Salvatore Antonio Biancardo |
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| Přispěvatelé: | Zhang, W., Zhang, P., Yu, Y., Li, X., Biancardo, S. A., Zhang, J. |
| Rok vydání: | 2022 |
| Předmět: |
Computer science
Time series analysi self-attention computer.software_genre Maintenance engineering Matrix decomposition Adversarial system Data imputation spatio-temporal analysi Training Imputation (statistics) Time series Data model Artificial neural network Mechanical Engineering generative adversarial network deep learning Traffic flow Missing data Net (mathematics) Computer Science Applications Automotive Engineering Task analysi Data mining computer Generative grammar |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 23:7919-7930 |
| ISSN: | 1558-0016 1524-9050 |
| Popis: | With the rapid development of sensor technologies, time series data collected by multiple and spatially distributed sensors have been widely used in different research fields. Examples of such data include geo-tagged temperature data collected by temperature sensors, air pollutant monitoring data, and traffic data collected by road traffic sensors. Due to sensor failure, communication errors and storage loss, etc., data collected by sensors inevitably includes missing data. However, models commonly used in the analysis of such large-scale data often rely on complete data sets. This paper proposes a model for the imputation of missing data of traffic flow, which combines a self-attention mechanism, an auto-encoder, and a generative adversarial network, into a self-attention generative adversarial imputation net (SA-GAIN). The introduction of the self-attention mechanism can help the proposed model to effectively capture correlations between spatially-distributed sensors at different time points. Adversarial training through two neural networks, called generators and discriminators, allows the proposed model to generate imputed data close to the real data. In comparison with different imputation models, the proposed model shows the best performance in imputing missing data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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