A dynamic graph attention network trained on 100,000 hours of SUMO simulation predicts the mean and spread of bidirectional arterial travel times from loop detector counts and signal timing data.
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Dynamic Graph Attention Networks for Travel Time Distribution Prediction in Urban Arterial Roads
A dynamic graph attention network trained on 100,000 hours of SUMO simulation predicts the mean and spread of bidirectional arterial travel times from loop detector counts and signal timing data.