Accurate　traffic　forecasting　is　one　of　the　key　applications　within　Internet　of　Things　(IoT)-based　Intelligent　Transportation　Systems　(ITS),　playing　a　vital　role　in　enhancing　traffic　quality,　optimizing　public　transportation,　and　planning　infrastructure.　However,　existing　spatial-temporal　methods　encounter　two　primary　limitations:　(1)　they　have　difficulty　differentiating　samples　over　time　and　often　ignore　dependencies　among　road　network　nodes　at　different　time　scales;　(2)　they　are　limited　in　capturing　dynamic　spatial　correlations　with　predefined　and　adaptive　graphs.　To　overcome　these　limitations,　we　introduce　a　novel　Temporal　Identity　Interaction　Dynamic　Graph　Convolutional　Network　(TIIDGCN)　for　traffic　forecasting.　The　central　concept　involves　assigning　temporal　identity　features　to　raw　data　and　extracting　distinctive,　representative　spatial-temporal　features　through　multiscale　interactive　learning.　Specifically,　we　design　a　multiscale　interactive　model　incorporating　both　spatial　and　temporal　components.　This　network　aims　to　explore　spatial-temporal　patterns　at　various　scales　from　macro　to　micro,　facilitating　their　mutual　enhancement　through　positive　feedback　mechanisms.　For　the　spatial　component,　we　design　a　new　dynamic　graph　learning　method　to　depict　the　changing　dependencies　among　nodes.　We　conduct　comprehensive　experiments　using　four　real-world　traffic　datasets　(PeMS04/07/08　and　NYCTaxi　Drop-off/Pick-up).　Specifically,　TIIDGCN　achieves　a　16.46%　reduction　in　Mean　Absolute　Error　compared　to　the　Spatial-Temporal　Graph　Attention　Gated　Recurrent　Transformer　Network　model　on　the　PeMS08　dataset.　©　2025　IEEE.