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Dynamic adaptive generative adversarial networks with multi-view temporal factorizations for hybrid recovery of missing traffic data

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Abstract

Making reliable recovery of missing traffic data facilitates diverse applications of data-driven intelligent transportation system. But faced with correlation and heterogeneity along spatial–temporal dimensions, most existing works lack sufficient capability to capture these complex properties, resulting in suboptimal imputation performance. By addressing this challenge, we propose a hybrid framework TFs-DGAN consisting of dynamic adaptive generative adversarial networks (DA-GAN) with multi-view temporal factorizations (TFs), which can efficiently repair missing data by modeling those spatial–temporal correlations. Of these, DA-GAN model can generate traffic data from noise distribution and keep iterating dynamically to extract the global consistency. To further exploit the local consistency, TFs model drives the continual reduction in local elements in residuals by a novel truncation mechanism. Unlike the single model computation, TFs-DGAN integrates all stage-optimized residuals by local feedback and finally outputs the best repair results. In fact, our intention for this strategy is that DA-GAN module produces data but inaccurately, while TFs module refines its imperfections by modeling multi-view temporal properties. From the numerical analysis, the empirical evaluation relying on two publicly available traffic datasets suggests that our TFs-DGAN significantly outperforms the state-of-the-art baseline models in terms of accuracy, stability and computational efficiency.

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Acknowledgements

The authors would like to thank anonymous referees for their valuable comments. This study is supported by the project of the National Natural Science Foundation of China, Youth Fund Project (No. 11702099) and the National Natural Science Foundation of China (No. 52072130).

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Authors and Affiliations

  1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510641, China

    Jinlong Li, Pan Wu & Lunhui Xu

  2. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China

    Ruonan Li

  3. Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA

    Zilin Huang

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  1. Jinlong Li
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Correspondence to Lunhui Xu.

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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Li, J., Li, R., Huang, Z. et al. Dynamic adaptive generative adversarial networks with multi-view temporal factorizations for hybrid recovery of missing traffic data. Neural Comput & Applic 35, 7677–7696 (2023). https://doi.org/10.1007/s00521-022-08064-w

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  • DOI: https://doi.org/10.1007/s00521-022-08064-w

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