Traffic　flow　forecasting　is　crucial　for　improving　urban　traffic　management　and　reducing　resource　consumption.　Accurate　traffic　conditions　prediction　requires　capturing　the　complex　spatial-temporal　dependencies　inherent　in　traffic　data.　Traditional　spatial-temporal　graph　modeling　methods　often　rely　on　fixed　road　network　structures,　failing　to　account　for　the　dynamic　spatial　correlations　that　vary　over　time.　To　address　this,　we　propose　a　Transformer-Enhanced　Adaptive　Graph　Convolutional　Network　(TEA-GCN)　that　alternately　learns　temporal　and　spatial　correlations　in　traffic　data　layer-by-layer.　Specifically,　we　design　an　adaptive　graph　convolutional　module　to　dynamically　capture　implicit　road　dependencies　at　different　time　levels　and　a　local-global　temporal　attention　module　to　simultaneously　capture　long-term　and　short-term　temporal　dependencies.　Experimental　results　on　two　public　traffic　datasets　demonstrate　the　effectiveness　of　the　proposed　model　compared　to　other　state-of-the-art　traffic　flow　prediction　methods.