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English(EN) Fusing Urban Structure and Semantics: A Conditional Diffusion Model for Cross-City OD Matrix Generation

扩散模型融合城市数据，提升通勤流量预测精度

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了SEDAN，一种新颖的条件扩散模型，旨在生成跨城市通勤流量的起点-终点（OD）矩阵。该模型将城市表示为属性图，整合了人口统计和兴趣点特征以及邻接和距离矩阵等空间结构。SEDAN融合了语义和空间信息，以创建更准确、更具泛化能力的OD矩阵，在U.S.城市数据集上的RMSE表现比现有方法提高了7.38%。 AI

影响 通过改进OD矩阵生成，为城市规划和资源分配提供了一种更准确、更具泛化能力的方法。

排序理由 这是一篇详细介绍OD矩阵生成新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

扩散模型融合城市数据，提升通勤流量预测精度

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这是一篇详细介绍OD矩阵生成新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Bin Chen, Zhuoya Meng, Fang Yang, Runkang Guo, Jingtao Ding, Yin Zhang, Chuan Ai, Zhengqiu Zhu ·

    融合城市结构与语义：一种用于跨城市OD矩阵生成的条件扩散模型

    arXiv:2605.00938v1 Announce Type: new Abstract: Accurate modeling of commuting flows is important for urban governance, traffic planning, and resource allocation. However, the combined influence of individual intentions, geographic constraints, and social dynamics leads to consid…

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