Abstract: Deep-learning-based data-driven forecasting methods have achieved impressive results for traffic forecasting. Specifically, spatiotemporal graph neural networks have emerged as a promising ...
Abstract: Traffic flow prediction faces challenges in spatial relationship modeling and risk-aware external factor integration. Current graph-based methods typically rely on single adjacency matrices ...
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