Traffic　flow　data　has　three　main　characteristics:　large　amount　of　noise　and　incompleteness,　temporal　and　spatial　correlation,　and　dynamic　sequential　property.　Problems　of　noise,　loss　and　incompleteness　could　decrease　the　prediction　performance　and　make　it　difficult　for　transportation　system　management.　Inspired　by　recent　work　on　low　rank　representation　(LRR)　and　dynamic　mode　decomposition　(DMD),　we　propose　a　Low　Rank　Dynamic　Mode　Decomposition　(LRDMD)　model　which　solves　the　aforementioned　problems　simultaneously.　LRDMD　predicts　traffic　flow　by　using　a　state　transition　matrix　which　characterizes　the　relationship　between　temporally　neighboring　fragments　of　traffic　flow　with　low　rank　regularization.　We　conduct　experiments　of　traffic　flow　prediction　of　different　time　intervals　using　loop　coil　detector　data　of　Qingdao,　and　the　results　show　that　LRDMD　outperforms　state-of-the-art　methods.