A clustering-based approach for data-driven imputation of missing traffic data

Abstract

The problem of missing samples in road traffic data undermines the performance of intelligent transportation applications. This paper proposes a data-driven imputation method that exploits the spatial and temporal relationships existing between the traffic flows of multiple road segments that are correlated with each other. The K-means clustering technique is used to group together road segments with similar traffic flow patterns. Next, a deep-learning model based on stacked denoising autoencoders is constructed for each group of road segments to extract their spatial-temporal relationships and use them for imputing the missing data points. Experiments conducted with real traffic data demonstrate that the imputation accuracy of the proposed method is robust under different missing data rates.