The　problem　of　data　missing　is　a　common　issue　in　practical　traffic　data　collection　for　an　Intelligent　Transportation　System.　However,　how　to　efficiently　impute　the　missing　entries　of　the　traffic　data　is　still　a　challenge.　Previous　works　on　missing　traffic　data　imputation　usually　apply　matrix　or　tensor　completion　based　methods　which　are　unable　to　fully　exploit　the　spatio-Temporal　features　of　historical　traffic　data　to　achieve　a　satisfactory　performance.　In　this　paper,　we　propose　a　3D　convolutional　generative　adversarial　networks　to　impute　missing　traffic　data.　We　propose　to　use　a　fractionally　strided　3D　convolutional　neural　network　to　construct　the　generator　network　since　it　can　upsample　efficiently　in　a　deep　network　and　a　3D　convolutional　neural　network　to　construct　the　discriminator　network　to　fully　capture　spatio-Temporal　features　of　traffic　data.　We　also　present　numerical　results　with　real　traffic　flow　dataset　to　show　that　the　proposed　model　can　significantly　improve　the　performance　in　terms　of　recovery　accuracy　over　the　other　existing　tensor　completion　methods　under　various　data　missing　patterns.　We　believe　that　the　proposed　approach　provides　a　promising　alternative　for　data　imputation　in　ITS　and　other　applications.　©　2018　IEEE.