Traffic　forecasting　is　a　challenging　problem　because　of　the　irregular　and　complex　road　network　in　space　and　the　dynamic　and　non-stationary　traffic　flow　in　time.　To　solve　this　problem,　the　recently　proposed　temporal　graph　convolution　models　abstracted　the　spatial　and　temporal　features　of　the　traffic　system　and　obtained　considerable　improvement.　However,　most　of　the　current　methods　use　empirical　graphs　to　represent　the　road　network,　which　don＇t　fully　extract　the　spatial　and　temporal　features.　This　paper　proposes　an　Optimized　Temporal-Spatial　Gated　Graph　Convolution　Network　(OTSGGCN)　for　traffic　forecasting,　in　which　the　spatial-temporal　traffic　feature　is　captured　by　an　innovative　graph　convolution　network　with　the　graph　constructed　in　a　data-driven　way.　The　experiments　on　two　real-world　traffic　datasets　show　that　the　proposed　method　outperforms　the　state　of　the　art　traffic　forecasting　methods.