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Found 2 papers, 2 papers with code
Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting
To that end, we develop a new transfer learning approach for DCRNN, where a single model trained on data-rich regions of the highway network can be used to forecast traffic on unseen regions of the highway network.
Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting
We demonstrate the efficacy of the graph-partitioning-based DCRNN approach to model the traffic on a large California highway network with 11, 160 sensor locations.
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