Researchdynamic graphsreinforcement learningtraffic forecastinginterpretability
Reinforcement Learning Guides Dynamic Multi-Graph Fusion
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8.1
An arXiv preprint (Jan 10, 2026) introduces RL-DMF, a reinforcement-learning-guided dynamic multi-graph fusion framework for real-time evacuation traffic prediction. Using data from 12 Florida hurricanes (2016–2024), the model achieves 95% accuracy (RMSE 293.9) for one-hour forecasts and 90% accuracy (RMSE 426.4) up to six hours, outperforming state-of-the-art baselines and offering interpretable feature selection.
Scoring Rationale
Strong methodological novelty and significant empirical gains, constrained by a single arXiv preprint and domain-limited evaluation.
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