TY  - EJOU
AU  - Shin, Dong-Hoon 
AU  - Lee, Seo-El 
AU  - Jeon, Byeong-Uk 
AU  - Chung, Kyungyong 

TI  - Missing Value Imputation Model Based on Adversarial Autoencoder Using Spatiotemporal Feature Extraction
T2  - Intelligent Automation \& Soft Computing

PY  - 2023
VL  - 37
IS  - 2
SN  - 2326-005X

AB  - 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.
KW  - Missing value; adversarial autoencoder; spatiotemporal feature extraction

DO  - 10.32604/iasc.2023.039317