Traffic　forecasting　is　attracting　considerable　interest　due　to　its　widespread　application　in　intelligent　transportation　systems.　Given　the　complex　and　dynamic　traffic　data,　many　methods　focus　on　how　to　establish　a　spatial-temporal　model　to　express　the　non-stationary　traffic　patterns.　Recently,　the　latest　Graph　Convolution　Network　(GCN)　has　been　introduced　to　learn　spatial　features　while　the　time　neural　networks　are　used　to　learn　temporal　features.　These　GCN　based　methods　obtain　state-of-the-art　performance.　However,　the　current　GCN　based　methods　ignore　the　natural　hierarchical　structure　of　traffic　systems　which　is　composed　of　the　micro　layers　of　road　networks　and　the　macro　layers　of　region　networks,　in　which　the　nodes　are　obtained　through　pooling　method　and　could　include　some　hot　traffic　regions　such　as　downtown　and　CBD　etc.,　while　the　current　GCN　is　only　applied　on　the　micro　graph　of　road　networks.　In　this　paper,　we　propose　a　novel　Hierarchical　Graph　Convolution　Networks　(HGC-N)　for　traffic　forecasting　by　operating　on　both　the　micro　and　macro　traffic　graphs.　The　proposed　method　is　evaluated　on　two　complex　city　traffic　speed　datasets.　Compared　to　the　latest　GCN　based　methods　like　Graph　WaveNet,　the　proposed　HGCN　gets　higher　traffic　forecasting　precision　with　lower　computational　cost.The　website　of　the　code　is　https://github.com/guokan987/HGCN.git.