WardropNet is a novel machine learning approach that combines neural networks with equilibrium models to predict traffic flow more accurately and efficiently than traditional methods.
The proposed MVC-STNet model effectively captures the complex spatial and temporal correlations in traffic data by leveraging multi-view spatial graphs and channel-wise graph convolutional networks, outperforming state-of-the-art methods.
Deep learning with CNN-LSTM architecture enhances traffic flow prediction using cellular automata-based models.