English(EN) CoReFlow: Consistency-Relaxed Flow Maps for Probabilistic Traffic Forecasting

新的CoReFlow模型提高了概率交通预测的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了CoReFlow，这是一种用于概率交通预测的新型模型，旨在提高智能交通系统的准确性并降低推理成本。与可能无法很好地匹配交通数据复杂性的现有分步生成模型不同，CoReFlow基于流图采用一步生成方法。该模型通过监督插值端点并使用条件均值限制一致性的训练目标来实现更高的准确性，在真实数据集上表现优于当前基线。 AI

影响 CoReFlow在概率交通预测方面的进步可能带来更可靠、更高效的智能交通系统。

排序理由该集群包含一篇详细介绍新模型和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Pengnan Chi, Zhiguo Zhang, Xiaoliang Ma · 2026-11-05 05:00

CoReFlow：概率交通预测的放松一致性流图

arXiv:2611.05715v1 Announce Type: new Abstract: Probabilistic traffic forecasting is essential for reliable Intelligent Transportation Systems (ITS), as it captures uncertainties in complex road networks. Recent diffusion- and flow-based generative models have facilitated uncerta…

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报道来源 [1]

CoReFlow：概率交通预测的放松一致性流图

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