The　traffic　data　corrupted　by　noise　and　missing　entries　often　lead　to　the　poor　performance　of　Intelligent　Transportation　Systems　(ITS),　such　as　the　bad　congestion　prediction　and　route　guidance.　How　to　efficiently　impute　the　traffic　data　is　an　urgent　problem.　As　a　classic　deep　learning　method,　Generative　Adversarial　Network　(GAN)　achieves　remarkable　success　in　image　recovery　fields,　which　opens　up　a　new　way　for　the　traffic　data　imputation.　In　this　paper,　we　propose　a　novel　spatio-temporal　GAN　model　for　the　traffic　data　imputation　(STGAN).　Firstly,　we　design　the　generative　loss　and　center　loss,　which　not　only　minimizes　the　reconstructed　errors　of　the　imputed　entries,　but　also　ensures　each　imputed　entry　and　its　neighbors　conform　to　the　local　spatio-temporal　distribution.　Then,　the　discriminator　uses　the　convolution　neural　network　classifier　to　judge　whether　the　imputed　matrix　conforms　to　the　global　spatio-temporal　distribution.　As　for　the　network　architecture　of　the　generator,　we　introduce　the　skip-connection　to　keep　all　well　preserved　data　unchanged,　and　employ　the　dilated　convolution　to　capture　the　spatio-temporal　correlation　in　the　traffic　data.　The　experimental　results　show　that　our　proposed　method　obviously　outperforms　other　competitive　traffic　data　imputation　methods.