A Gated Generative Adversarial Imputation Approach for Signalized Road Networks

Abstract

Missing data imputation is an essential component of a robust traffic surveillance system. Despite the progress of data imputation technologies in intelligent transportation systems (ITS), only limited efforts have been devoted to tackling data missing issues on signalized roads, which present distinct missing patterns due to the effect of traffic signal timing on traffic flow. Unlike most studies that try to impute missing data at the granularity of road segment and aggregated time intervals, we impute missing data for individual traffic lanes at the granularity of signal cycle. We develop a data-driven fine-grained imputation approach based on a novel gated attentional generative adversarial network (GaGAN), which is highly responsive to the dynamic traffic environments of signalized road networks. The advantage of the network lies in that it can automatically learn inter-lane spatio-temporal correlations during each signal cycle. To model the spatial correlations, we jointly leverage spatial attention mechanisms and graph convolutional operations to quantify inter-lane influences within each signal cycle. To model the temporal correlations, we propose to integrate self-attention mechanisms into gated recurrent units (termed as SA-GRU). Two sub-discriminators are developed to model lane-level complete and missing data distributions, respectively, with the goal to improve the consistency between imputed data and overall data distribution. Experimental results demonstrate that the proposed approach is superior to other state-of-the-art methods, achieving robust performance over different data missing types.