As　a　significant　function　of　an　intelligent　transportation　system　(ITS),　accurate　traffic　flow　prediction　plays　an　important　role　in　traffic　management.　However,　the　nonlinear　and　complex　characteristics　of　traffic　flow　makes　the　traditional　approaches　unable　to　achieve　satisfactory　prediction　performance.　Although　the　existing　methods　based　on　deep　learning　models　have　improved　the　accuracy　of　traffic　flow　prediction,　they　still　ignore　some　potential　features　of　traffic　data.　In　this　paper,　we　propose　a　novel　network　model　called　Graph　Transformer　Attention　Network　(GTAN)　for　traffic　flow　prediction.　Firstly,　we　combine　Transformer　with　Graph　Convolutional　Network　(GCN)　to　discover　the　relationships　between　nodes　by　considering　the　information　of　all　the　positions　of　nodes　in　the　traffic　network　and　efficiently　extract　the　spatial　and　temporal　features　of　traffic　flow.　Secondly,　to　better　exploit　the　flow　characteristic　of　each　node,　we　assign　learnable　parameters　for　each　node　separately　to　learn　its　unique　variance.　Finally,　the　experimental　results　on　two　real　datasets　show　that　the　proposed　model　can　achieve　better　prediction　performance　and　less　computation　complexity　compared　with　the　other　existing　methods.　©　2021　IEEE.