Complete　and　accurate　traffic　data　is　critical　in　urban　traffic　management,　planning　and　operation.　In　fact,　real-world　traffic　data　contains　missing　values　due　to　multiple　factors,　such　as　device　outages　and　communication　errors.　For　traffic　data　completion　task,　most　of　the　existing　methods　are　matrix/tensor　completion　methods,　which　usually　enforce　low　rank　constraint　on　traffic　data　matrix/tensor.　But　they　neglect　the　graph　structure　of　traffic　data,　resulting　in　low　completion　performance.　Recently,　graph　convolutional　networks　have　achieved　remarkable　results　in　traffic　data　forecasting　due　to　their　abilities　of　feature　extraction　and　nonlinear　fitting　on　arbitrarily　graph-structured　data.　However,　there　are　few　studies　based　on　graph　neural　networks　for　traffic　data　completion　task.　In　this　paper,　we　propose　a　traffic　data　completion　model　based　on　graph　convolutional　network　model　to　impute　missing　values　from　the　perspective　of　deep　learning.　This　model　utilizes　graph　convolution　to　model　the　local　spatial　dependency.　As　for　global　spatial　dependency　and　temporal　dependency,　this　model　incorporates　self-attention　mechanism,　which　is　applied　in　the　spatial　and　temporal　dimensions　respectively.　The　experimental　results　on　the　two　real-time　datasets　demonstrate　that　the　proposed　model　outperforms　the　baseline　methods　significantly　under　arbitrarily　missing　scenarios.　IEEE