Exploring Deep Learning Applications in Traffic Flow Prediction

Traffic flow prediction plays an important role in intelligent transportation systems, supporting efficient traffic management and scientific decision-making. With the rapid advancement of deep learning, various advanced models have sprung up, aiming to capture the nonlinear, high-dimensional and dynamic evolution of traffic data. This paper systematically reviews three representative methods: autoencoder-based models, recurrent neural networks (RNNs), and graph neural networks (GNNs). With its stack-optimized structure, Autoencoder has demonstrated its capability in feature extraction, noise reduction and efficient representation learning, thereby improving prediction robustness and computational efficiency. RNNs and their variants (including LSTM and GRU) excel at portraying the temporal dependence of traffic sequences, while hybrid models that incorporate external data or optimize algorithms further enhance accuracy and adaptability. In recent years, GNN methods have emerged to specialize in spatial dependencies caused by complex road network structures. By combining GCNs with temporal models and enhanced learning, these methods have achieved remarkable results in capturing spatio-temporal correlations and long-term predictions. Overall, the integrated evolution of AE, RNN and GNN models explains the leap-forward development from feature compression, temporal modeling to spatio-temporal integrated learning. Despite the significant advantages, challenges remain in multimodal data fusion, computing efficiency and real-time deployment, which points out a promising direction for future research.