Recently,　Graph　Convolution　Network　(GCN)　and　Temporal　Convolution　Network　(TCN)　are　introduced　into　traffic　prediction　and　achieve　state-of-the-art　performance　due　to　their　good　ability　for　modeling　the　spatial　and　temporal　property　of　traffic　data.　In　spite　of　having　good　performance,　the　current　methods　generally　focus　on　the　traffic　measurement　of　road　segments,　i.e.　the　nodes　of　traffic　flow　graph,　while　the　edges　of　the　graph,　which　represent　the　correlation　of　traffic　data　of　different　road　segments　and　form　the　affinity　matrix　for　GCN,　are　usually　constructed　according　to　the　structure　of　road　network,　but　the　spatial　and　temporal　properties　are　not　well　exploited　in　their　theories.　In　this　paper,　we　propose　a　Dual　Dynamic　Spatial-Temporal　Graph　Convolution　Network　(DDSTGCN),　which　not　only　models　the　dynamic　property　of　the　nodes　of　the　traffic　flow　graph　but　also　captures　the　dynamic　spatial-temporal　feature　of　the　edges　by　transforming　the　traffic　flow　graph　into　its　dual　hypergraph.　The　traffic　prediction　is　enhanced　by　the　collaborative　convolutions　on　the　traffic　flow　graph　and　its　dual　hypergraph.　The　proposed　method　is　evaluated　by　extensive　traffic　prediction　experiments　on　six　real　road　datasets　and　the　results　show　that　it　outperforms　state-of-the-art　related　methods.　Source　codes　are　available　at　https://github.com/j1o2h3n/DDSTGCN.