Traffic flow prediction is a critical component of intelligent transportation systems, yet accurately forecasting traffic remains challenging due to the interaction between long-term trends and short-term fluctuations. Standard deep learning models often struggle with these challenges because their architectures inherently smooth over fine-grained fluctuations while focusing on general trends. This limitation arises from low-pass filtering effects, gate biases favoring stability, and memory update mechanisms that prioritize long-term information retention. To address these shortcomings, this study introduces a hybrid deep learning framework that integrates both long-term trend and short-term fluctuation information using two input features processed in parallel, designed to capture complementary aspects of traffic flow dynamics. Further, our approach leverages attention mechanisms, specifically Bahdanau attention, to selectively focus on critical time steps within traffic data, enhancing the model's ability to predict congestion and other transient phenomena. Experimental results demonstrate that features learned from both branches are complementary, significantly improving the goodness-of-fit statistics across multiple prediction horizons compared to a baseline model. Notably, the attention mechanism enhances short-term forecast accuracy by directly targeting immediate fluctuations, though challenges remain in fully integrating long-term trends. This framework can contribute to more effective congestion mitigation and urban mobility planning by advancing the robustness and precision of traffic prediction models.

本研究解决了在交通流预测中长期趋势与短期波动之间相互作用所带来的挑战。提出了一种混合深度学习框架，通过并行处理两个输入特征，结合长期趋势和短期波动信息，并应用Bahdanau注意力机制以增强关键时间步的关注，从而显著提高模型预测交通拥堵等瞬态现象的能力。这一框架有望推动交通预测模型的稳健性和精确性，为缓解拥堵和城市交通规划提供支持。

通过整合趋势和波动与注意力机制来增强短期交通预测