Spatial-temporal data mining for traffic flow regulation remains a challenging task due to the complex spatiotemporal correlation and the non-Euclidean nature of traffic data. Existing approaches typically utilize a provided spatial adjacency graph and sophisticated mechanisms for modeling spatial and temporal correlations. However, the static representation of a given spatial adjacency graph may restrict effective spatial-temporal dependency learning and fail to capture dynamic interactions over time. To address those challenges, our paper proposes the Dynamic Adaptive Graph Convolution Network (DAGCN), a highly optimized model with a low memory footprint. DAGCN can effectively learn hidden spatial dependencies by utilizing a novel graph convolution layer with adaptive parameter update mechanisms and innovative attention computations. Additionally, DAGCN could overcome the limitations of static data dependencies by employing the dynamic graph modulation approach. This approach combines the assumption of static adjacency matrices with dynamic feature generation based on the multi-head attention mechanism. Meanwhile, by integrating this module with a novel temporal module, DAGCN could gather information in the temporal domain. Experimental results on two real traffic datasets demonstrate that the proposed model achieves state-of-the-art performance compared to other baselines while maintaining the lowest parameter counts.