What question did this study set out to answer?

The aim is to develop a model for imputing missing traffic flow data using advanced machine learning techniques.

May 4, 2021

Missing Data Repairs for Traffic Flow With Self-Attention Generative Adversarial Imputation Net

Key Points

The aim is to develop a model for imputing missing traffic flow data using advanced machine learning techniques.
Proposes a self-attention generative adversarial imputation net (SA-GAIN) combining self-attention, auto-encoder, and GAN.
Utilizes a framework with generators and discriminators for adversarial training.
Compares the performance of SA-GAIN with other imputation models.
SA-GAIN outperforms standard imputation models in restoring missing traffic flow data.
The model effectively captures spatial correlations among multiple sensors over time.
Imputation results are shown to be similar to real traffic data based on performance metrics.

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.

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