Missing　data　in　time　series　is　a　pervasive　problem　that　serves　as　obstacles　for　subsequent　traffic　data　analysis.　Consequently,　extensive　research　works　have　been　conducted　on　traffic　missing　data　imputation　tasks.　The　state-of-the-art　traffic　data　imputation　models　are　mostly　based　on　recurrent　neural　networks.　However,　these　methods　belong　to　autoregressive　models　which　are　highly　susceptible　to　error　propagation.　The　attention-based　methods　are　non-autoregressive　models　that　can　avoid　compounding　errors　and　help　achieve　better　imputation　quality.　Moreover,　the　attention-based　methods　in　now　widely　applied　and　have　achieved　remarkable　results,　whereas　their　application　on　traffic　data　imputation　is　still　limited.　Thus,　this　paper　proposes　Self-Attention　Graph　Convolution　Imputation　Network　(SAGCIN)　for　spatio-temporal　traffic　data.　To　ensure　the　accuracy　of　data　imputation,　it　is　necessary　to　fully　capture　the　spatio-temporal　contextual　information　of　traffic　data　to　impute　missing　values.　To　this　end,　the　SAGCIN　model　incorporates　self-attention　mechanism　with　diffusion　graph　convolution　network.　The　SAGCIN　model　consists　of　two　spatio-temporal　blocks　with　a　spatio-temporal　encoder　and　an　imputation　decoder.　The　encoder　learns　spatio-temporal　representations　specialized　for　traffic　data　imputation　tasks.　Based　on　the　learned　representation,　the　decoder　performs　two　stages　of　imputation　operator　for　missing　data.　A　joint-optimization　training　approach　of　imputation　and　reconstruction　is　introduced　for　SAGCIN　to　perform　missing　value　imputation　for　traffic　data.　Empirical　results　demonstrate　that　SAGCIN　model　outperforms　state-of-the-art　methods　in　imputation　tasks　on　relevant　real-world　benchmarks.