Traffic　forecasting　using　deep　learning　represents　a　crucial　aspect　of　intelligent　transportation　systems,　carrying　substantial　implications　for　congestion　reduction　and　efficient　route　planning.　Despite　its　significance,　accurately　predicting　traffic　states　remains　a　challenge.　Existing　methodologies　focus　on　capturing　the　temporal　trends　of　traffic　states　and　the　spatial　dependencies　between　roads　to　enhance　prediction　accuracy.　However,　two　noteworthy　limitations　persist　in　these　approaches:　(1)　Many　models　neglect　the　interaction　between　spatiotemporal　features　across　varying　time　spans,　hindering　their　ability　to　utilize　traffic　state　information　effectively　for　predicting　future　conditions.　(2)　Genuine　correlations　between　roads　are　time-varying,　making　it　inadequate　to　rely　on　static　graphs　or　static　pre-trained　node　embeddings　to　model　dynamic　correlations　between　roads.　To　address　these　challenges,　we　propose　the　Multiple　Time-Scale　Graph　Attention　Network　(MTS-GATN),　which　comprises　two　key　modules:　the　Multiple　Time-Scale　Spatiotemporal　Features　Extraction　Module　and　the　Feature　Augmentation　Module.　The　first　module　involves　stacking　multiple　spatiotemporal　extraction　layers　to　discern　traffic　state　information　at　different　time　scales.　In　the　second　module,　we　employ　dynamic　spatial　semantic　embedding　for　feature　augmentation,　providing　nodes　with　dynamic　representations　over　time.　Subsequently,　we　leverage　a　multi-head　spatiotemporal　attention　mechanism　to　comprehensively　consider　location　information　and　real-time　semantic　data,　facilitating　the　interaction　of　traffic　state　information　across　multiple　time　scales.　Experimental　results　on　two　distinct　traffic　datasets　validate　the　superior　performance　of　MTS-GATN　in　medium-term　and　long-term　forecasting　scenarios.