<ul><li>Traffic prediction is crucial for urban planning and traffic management, but existing methods overlook long-term relationships and anomaly influences.</li><li>A global spatio-temporal fusion-based algorithm with anomaly awareness is proposed to address these issues.</li><li>The algorithm incorporates an anomaly detection network to evaluate the impact of unexpected events on traffic prediction.</li><li>Experiments using real-scenario datasets show that the proposed approach achieves state-of-the-art performance.</li></ul>

Global Spatio-Temporal Fusion-based Traffic Prediction Algorithm with Anomaly Aware

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