In　intelligent　transportation　systems　(ITS),　incomplete　traffic　data　due　to　sensor　malfunctions　and　communication　faults,　seriously　restricts　the　related　applications　of　ITS.　Recovering　missing　data　from　incomplete　traffic　data　becomes　an　important　issue　for　ITS.　Existing　works　on　traffic　data　imputation　cannot　achieve　satisfactory　accuracy　due　to　inefficiently　exploiting　the　underlying　topological　structure　of　the　traffic　data.　In　this　paper,　we　model　the　topology　of　the　road　network　as　a　graph　and　introduce　graph　Fourier　transform　(GFT)　to　process　the　traffic　data.　Then　we　utilize　an　algebraic　framework　termed　as　graph-tensor　singular　value　decompositions　(GT-SVD)　to　extract　the　hidden　spatial　information　of　traffic　data.　Furthermore,　we　propose　a　novel　graph　spectral　regularized　tensor　completion　algorithm　based　on　GT-SVD　and　construct　temporal　regularized　constraints　to　improve　the　recovery　accuracy.　The　extensive　experimental　results　on　real　traffic　datasets　demonstrate　that　the　proposed　algorithm　outperforms　the　state-of-the-art　methods　under　different　missing　patterns.