Traffic　prediction　is　a　core　problem　in　the　intelligent　transportation　system　and　has　broad　applications　in　the　transportation　management　and　planning,　and　the　main　challenge　of　this　field　is　how　to　efficiently　explore　the　spatial　and　temporal　information　of　traffic　data.　Recently,　various　deep　learning　methods,　such　as　convolution　neural　network　(CNN),　have　shown　promising　performance　in　traffic　prediction.　However,　it　samples　traffic　data　in　regular　grids　as　the　input　of　CNN,　thus　it　destroys　the　spatial　structure　of　the　road　network.　In　this　paper,　we　introduce　a　graph　network　and　propose　an　optimized　graph　convolution　recurrent　neural　network　for　traffic　prediction,　in　which　the　spatial　information　of　the　road　network　is　represented　as　a　graph.　Additionally,　distinguishing　with　most　current　methods　using　a　simple　and　empirical　spatial　graph,　the　proposed　method　learns　an　optimized　graph　through　a　data-driven　way　in　the　training　phase,　which　reveals　the　latent　relationship　among　the　road　segments　from　the　traffic　data.　Lastly,　the　proposed　method　is　evaluated　on　three　real-world　case　studies,　and　the　experimental　results　show　that　the　proposed　method　outperforms　state-of-the-art　traffic　prediction　methods.