Bidirectional spatial–temporal traffic data imputation via graph attention recurrent neural network

Abstract

Spatiotemporal traffic data is increasingly important in transportation services with the development of intelligent transportation system (ITS). However, due to various unpredictable disruptions in the data collection and storage process, traffic data is often incomplete which will seriously hinder downstream tasks if not handled properly. Most existing methods for traffic data imputation either impose too strong assumptions on the data distribution or almost ignore the interdependencies across time steps and the information expressed by missingness. In this article, we propose a graph attention recurrent neural network (GARNN) for traffic data imputation. In our model, we impute data from both temporal and spatial perspectives. First, we model the observations and missingness separately via two LSTMs to treat the missingness of data as another special information distinct from observations. Then, a decay mechanism and graph attention network (GAT) are applied to learn the interdependencies across time steps and capture the spatial correlations respectively to generate temporal estimation and spatial estimation. Finally, those two estimations are integrated into the ultimate imputation. The whole process is in a bidirection. The proposed method is evaluated on two public datasets under three different missing scenarios. Experimental results show the effectiveness of the proposed model compared with other baselines.