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English(EN) Distilling Aggregate Mobility Statistics into a Language Model Policy for Post-Event Crowd Simulation

使用聚合出行数据对语言模型进行微调以进行人群模拟 · 跟踪 2 个来源

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了一种方法，通过使用聚合出行统计数据（例如区域级设备数量和起点-终点（OD）流量）来指导代理行为，从而对语言模型进行微调以进行人群模拟。这种方法解决了仅有聚合数据时个体行为不确定的挑战。通过迭代地将模型的目的地分布拟合到观察到的 OD 流量，微调后的代理在来自两场棒球比赛的数据上将目的地份额误差降低了 25%，同时保持了相似的网格相关性。 AI

影响 这项研究展示了语言模型在模拟复杂人类行为方面的新颖应用，有可能改善城市规划和活动管理。

排序理由 该集群包含一篇发表在 arXiv 上的研究论文，详细介绍了一种将语言模型应用于人群模拟的新颖方法。

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

使用聚合出行数据对语言模型进行微调以进行人群模拟 · 跟踪 2 个来源

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该集群包含一篇发表在 arXiv 上的研究论文，详细介绍了一种将语言模型应用于人群模拟的新颖方法。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Tatsuya Amano, Hirozumi Yamaguchi ·

    将聚合出行统计数据提炼为用于事件后人群模拟的语言模型策略

    arXiv:2608.19778v1 Announce Type: cross Abstract: Pedestrian simulators need a behaviour rule for every agent, but privacy usually limits the data for setting one to aggregate statistics, namely zone-level device counts and origin-to-destination (OD) flows, with no individual tra…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Hirozumi Yamaguchi ·

    将聚合出行统计数据提炼为用于事件后人群模拟的语言模型策略

    Pedestrian simulators need a behaviour rule for every agent, but privacy usually limits the data for setting one to aggregate statistics, namely zone-level device counts and origin-to-destination (OD) flows, with no individual trajectories. Such aggregates under-determine individ…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    将聚合出行统计数据提炼为用于活动后人群模拟的语言模型策略

    Pedestrian simulators need a behaviour rule for every agent, but privacy usually limits the data for setting one to aggregate statistics, namely zone-level device counts and origin-to-destination (OD) flows, with no individual trajectories. Such aggregates under-determine individ…

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