English(EN) Encounter-Driven City-Scale Human Mobility Generation with Large Language Models

LLM生成考虑遭遇因素的人类出行模式

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了EncounterMob，一个利用大型语言模型（LLM）生成考虑个体间真实遭遇的人类出行模式的新框架。与以往以居民为中心的方法不同，EncounterMob联合建模了个人轨迹和群体层面的互动结构。该框架采用粗粒度到细粒度的遭遇生成器和迭代耦合框架来协调遭遇与个人出行行为。在日本和纽约市数据集上的实验表明，EncounterMob在遭遇和个人出行指标方面均显著优于现有方法。 AI

影响这项研究通过更好地模拟社交互动，可能为城市规划和公共卫生带来更真实的模拟。

排序理由详细介绍基于LLM的人类出行生成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Chao Li, Renhe Jiang, Chuan Xiao, Jiawei Wang · 2026-10-15 04:00

基于大型语言模型的遭遇驱动的城市级人类出行生成

arXiv:2610.17672v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for human mobility generation by modeling individual routines, activities, and mobility preferences. However, existing approaches remain largely resident-centric: generating r…

本文如何被排名

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基于大型语言模型的遭遇驱动的城市级人类出行生成

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