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Missing Value Imputation Model Based on Adversarial Autoencoder Using Spatiotemporal Feature Extraction

Dong-Hoon Shin1, Seo-El Lee2, Byeong-Uk Jeon1, Kyungyong Chung3,*

1 Department of Computer Science, Kyonggi University, Suwon-si, Gyeonggi-do, 16227, Korea
2 Department of Public Safety Bigdata, Kyonggi University, Suwon-si, Gyeonggi-do, 16227, Korea
3 Division of AI Computer Science and Engineering, Kyonggi University, Suwon-si, Gyeonggi-do, 16227, Korea

* Corresponding Author: Kyungyong Chung. Email: email

Intelligent Automation & Soft Computing 2023, 37(2), 1925-1940. https://doi.org/10.32604/iasc.2023.039317

Abstract

Recently, the importance of data analysis has increased significantly due to the rapid data increase. In particular, vehicle communication data, considered a significant challenge in Intelligent Transportation Systems (ITS), has spatiotemporal characteristics and many missing values. High missing values in data lead to the decreased predictive performance of models. Existing missing value imputation models ignore the topology of transportation networks due to the structural connection of road networks, although physical distances are close in spatiotemporal image data. Additionally, the learning process of missing value imputation models requires complete data, but there are limitations in securing complete vehicle communication data. This study proposes a missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction to address these issues. The proposed method replaces missing values by reflecting spatiotemporal characteristics of transportation data using temporal convolution and spatial convolution. Experimental results show that the proposed model has the lowest error rate of 5.92%, demonstrating excellent predictive accuracy. Through this, it is possible to solve the data sparsity problem and improve traffic safety by showing superior predictive performance.

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APA Style
Shin, D., Lee, S., Jeon, B., Chung, K. (2023). Missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction. Intelligent Automation & Soft Computing, 37(2), 1925-1940. https://doi.org/10.32604/iasc.2023.039317
Vancouver Style
Shin D, Lee S, Jeon B, Chung K. Missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction. Intell Automat Soft Comput . 2023;37(2):1925-1940 https://doi.org/10.32604/iasc.2023.039317
IEEE Style
D. Shin, S. Lee, B. Jeon, and K. Chung "Missing Value Imputation Model Based on Adversarial Autoencoder Using Spatiotemporal Feature Extraction," Intell. Automat. Soft Comput. , vol. 37, no. 2, pp. 1925-1940. 2023. https://doi.org/10.32604/iasc.2023.039317



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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