Graph-based　traffic　flow　predictioning　is　widely　applied　in　traffic　systems,　where　constructing　intricate　spatiotemporal　correlation　models　from　relevant　time　series　data　is　imperative　for　comprehending　the　dynamics　of　the　traffic　system.　The　extraction　of　features　from　graphical　data,　coupled　with　the　integration　of　time　series　data,　serves　to　enhance　the　accuracy　of　traffic　flow　predictions.　Additionally,　the　challenge　arises　when　real　traffic　flow　data　often　contains　missing　values.　Predicting　traffic　in　scenarios　with　missing　data　proves　to　be　challenging,　as　existing　traffic　flow　predictioning　methods　frequently　lack　the　capacity　to　model　dynamic　spatiotemporal　correlations　in　the　presence　of　such　gaps,　leading　to　unsatisfactory　prediction　outcomes.　This　paper　introduces　the　Adaptive　Spatiotemporal　Graph　WaveNet-based　Graph　Convolutional　Network　(AST-GW-GCN)　to　tackle　traffic　flow　predictioning.　AST-GW-GCN　comprises　three　independent　components,　each　modeling　short-term,　daily,　and　weekly　dependencies　of　traffic　flow.　Within　each　component,　Gated　Temporal　Convolutional　Network　(TCN)　and　Graph　Convolutional　Network　(GCN)　serve　as　encoders,　conducting　spatial　convolution　to　extract　spatial　correlations　and　temporal　convolution　to　capture　temporal　correlations,　thereby　generating　hidden　features.　The　Gated　Recurrent　Unit　(GRU)　is　employed　to　decode　these　hidden　features　and　weigh　the　outputs　of　the　three　components,　producing　the　final　prediction　result.　The　spatial　convolution　module　establishes　an　adaptive　adjacency　matrix　to　overcome　the　physical　constraints　of　the　graph　structure,　facilitating　improved　extraction　of　hidden　spatial　dependencies　within　the　data.　Furthermore,　experiments　are　conducted　under　various　data　missing　patterns　and　missing　ratios.　The　experimental　findings,　based　on　the　PeMS08　real　dataset,　demonstrate　that　the　proposed　AST-GW-GCN　comprehensively　captures　spatiotemporal　correlations　in　the　data,　outperforming　baseline　models　in　terms　of　performance.　©　2024　SPIE.