
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.


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WardropNet: Predicting Traffic Flow Using Neural Networks and Equilibrium Models


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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.


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Deep Multi-View Channel-Wise Spatio-Temporal Network for Accurate Traffic Flow Prediction



Deep learning with CNN-LSTM architecture enhances traffic flow prediction using cellular automata-based models.


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Deep Learning for Traffic Flow Prediction using Cellular Automata-based Model and CNN-LSTM Architecture
