Generative-Contrastive-Attentive Spatial-Temporal Network for Traffic Data Imputation

Abstract

Data missing is inevitable in Intelligent Transportation Systems (ITSs). Although many methods have been proposed for traffic data imputation, it is still very challenging because of two reasons. First, the ground truth of missing data is actually inaccessible, which makes most imputation methods hard to be trained. Second, incomplete data would easily mislead the model to learn unreliable spatial-temporal dependencies, which finally hurts the imputation performance. In this paper, we proposes a novel $\underline{{\boldsymbol{G}}}$enerative-$\underline{{\boldsymbol{C}}}$ontrastive-$\underline{{\boldsymbol{A}}}$ttentive $\underline{{\boldsymbol{S}}}$patial-$\underline{{\boldsymbol{T}}}$emporal $\underline{{\boldsymbol{N}}}$etwork (GCASTN) for traffic data imputation. It combines the ideas of generative and contrastive self-supervised learning together to develop a new training paradigm for imputation without relying on the ground truth of missing data. In addition, it introduces nearest missing interval to describe missing data and a novel $\underline{{\boldsymbol{M}}}$issing-$\underline{{\boldsymbol{A}}}$ware $\underline{{\boldsymbol{A}}}$ttention (MAA) mechanism is designed to utilize nearest missing interval to guide the model to adaptively learn the reliable spatial-temporal dependencies of incomplete traffic data. Extensive experiments covering three types of missing scenarios on two real-world traffic flow datasets demonstrate that GCASTN outperforms the state-of-the-art baselines.