This　article　proposes　a　robust　decoupling　network　named　RDNet　to　provide　stable　traffic　predictions　even　when　perturbations　exist　in　historical　data.　A　decoupling　block　is　designed　in　the　RDNet　for　dividing　traffic　data　into　the　invariable　component　(IC)　and　variable　component　(VC).　The　IC　of　historical　or　future　data　is　estimated　through　the　invariable　block　without　historical　data　and　thus　would　not　be　perturbed.　The　variable　block　is　developed　to　forecast　the　VC　of　future　data　using　the　VC　of　historical　data.　Besides,　the　robust　graph　neural　network　and　smoothing　loss　are　designed　to　reduce　the　effects　of　perturbations.　The　RDNet　fuses　the　obtained　IC　and　VC　of　future　data　to　produce　the　predictions,　and　the　invariable　and　decoupling　losses　are　developed　for　stabilizing　the　prediction.　The　results　on　six　open　datasets　have　demonstrated　that　the　RDNet　can　achieve　a　15.62%　average　improvement　in　accuracy　compared　with　the　state-of-the-art　predictor.　©　2005-2012　IEEE.