Graph　convolutional　networks　(GCN)　have　been　applied　in　the　traffic　flow　forecasting　tasks　with　the　graph　capability　in　describing　the　irregular　topology　structures　of　road　networks.　However,　GCN　based　traffic　flow　forecasting　methods　often　fail　to　simultaneously　capture　the　short-term　and　long-term　temporal　relations　carried　by　the　traffic　flow　data,　and　also　suffer　the　over-smoothing　problem.　To　overcome　the　problems,　we　propose　a　hierarchical　traffic　flow　forecasting　network　by　merging　newly　designed　the　long-term　temporal　Transformer　network　(LTT)　and　the　spatio-temporal　graph　convolutional　networks　(STGC).　Specifically,　LTT　aims　to　learn　the　long-term　temporal　relations　among　the　traffic　flow　data,　while　the　STGC　module　aims　to　capture　the　short-term　temporal　relations　and　spatial　relations　among　the　traffic　flow　data,　respectively,　via　cascading　between　the　one-dimensional　convolution　and　the　graph　convolution.　In　addition,　an　attention　fusion　mechanism　is　proposed　to　combine　the　long-term　with　the　short-term　temporal　relations　as　the　input　of　the　graph　convolution　layer　in　STGC,　in　order　to　mitigate　the　over-smoothing　problem　of　GCN.　Experimental　results　on　three　public　traffic　flow　datasets　prove　the　effectiveness　and　robustness　of　the　proposed　method.