Paper Info
Title
Missing Data Repairs for Traffic Flow With Self-Attention Generative Adversarial Imputation Net
Abstract
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.
Year
DOI
Venue
2022
10.1109/TITS.2021.3074564
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Keywords
DocType
Volume
Data imputation, spatio-temporal analysis, deep learning, generative adversarial network, self-attention
Journal
23
Issue
ISSN
Citations 
7
1524-9050
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Weibin Zhang13110.03
Pulin Zhang200.34
Yinghao Yu300.34
Xiying Li400.34
Salvatore Antonio Biancardo500.34
Junyi Zhang663.79