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　and　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.