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Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction
2022
Applied Sciences
Accurate traffic prediction is crucial to the construction of intelligent transportation systems. This task remains challenging because of the complicated and dynamic spatiotemporal dependency in traffic networks. While various graph-based spatiotemporal networks have been proposed for traffic prediction, most of them rely on predefined graphs from different views or static adaptive matrices. Some implicit dynamics of inter-node dependency may be neglected, which limits the performance of
doi:10.3390/app12062842
fatcat:jt5b6gtiv5fo3mt7zcpsafxwny