Traffic flow prediction is crucial for managing urban traffic and preventing congestion in intelligent transportation systems, particularly with the rise of large-scale real-time traffic data. Graph neural networks and Transformer-based architectures have shown significant success in this task. However, existing methods struggle to: (1) fully capture the evolving traffic patterns and complex interactions between nodes over time, and (2) effectively integrate spatio-temporal and multi-angle features, leading to less accurate long-term predictions. In this paper, we propose the Dual-module Adaptive Transformer and Spatio-Temporal Attention Network (DAT-STAN) to address these challenges in the context of large-scale traffic data. The model utilizes dynamic, data-driven structures to capture evolving relationships in the traffic network and incorporates spatio-temporal attention mechanisms to focus on key features while reducing irrelevant information. Extensive experiments on multiple large-scale, real-world datasets show that DAT-STAN outperforms state-of-the-art models in both prediction accuracy and computational efficiency.