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.