A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting - Semin Kwak; Danya Li; Nikolas Geroliminis (Scientific Reports)
Abstract Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks. However, traditional graph convolutional networks (GCNs) face limitations in capturing long-range spatial correlations, which can hinder accurate long-term predictions. To address this issue, we propose the Two-level Resolution Neural Network, which enhances interpretability by introducing two resolution blocks. The first block captures large-scale regional traffic patterns, while the second block, using a GCN, focuses on small-scale spatial correlations, informed by the regional predictions. This structure allows the model to intuitively integrate both local and distant traffic data, improving long-term forecasting. In addition to its predictive capabilities, TwoResNet offers enhanced interpretability, particularly in scenarios involving noisy or incomplete data.