Application of Adaptive Deep Learning Framework for Traffic Flow Prediction

This paper aims to address the limitations of existing traffic flow prediction models in terms of generalization ability, adaptability and real-time updating. Specific issues include: first, existing models have limited ability to capture complex traffic patterns and are difficult to adapt to sudden changes and nonlinear traffic flow patterns. Second, traditional models rely on historical data and are difficult to effectively handle traffic data with high complexity, irregularity and nonlinear characteristics. Third, in order to adapt to the dynamic changes in traffic conditions, real-time model updating is essential. The core technologies adopted in this paper are: first, the existing GCN model is improved to solve the oversmoothing problem and enhance the expressive ability of the model; second, a flexible graph structure is constructed by using the parameter-sharing adaptive graph mechanism to dynamically learn spatial dependencies within the traffic network. The expected research results aim to improve the efficiency of urban traffic management, reduce congestion and accident rates, and promote the sustainable development of urban transportation. Third, Spatio-Temporal Dependency Extraction and Fusion (STDEF), which combines GCN and TCN to improve the model's ability to extract and integrate spatio-temporal features of the traffic flow by utilizing the multiple attention mechanism. Finally, Dilated Causal Convolution (DCC) applies extended convolution in temporal convolutional networks to expand the acceptance domain of the model while preventing information leakage and improving the accuracy of time series prediction.