基于代理的模拟,以互动意识为基础,模拟零售商店的客户轨迹,采用变压器架构
Taizo Horikomi1, Takayuki Mizuno2,3
1The Graduate University for Advanced Studies, SOKENDAI, Shonan Village, Hayama, Kanagawa, 240-0193, Japan. horikomi@nii.ac.jp.
Scientific reports
|November 6, 2025
概括
这项研究引入了一个变压器模型来预测商店中的客户流动,从真实数据中学习社会响应轨迹. 该模型捕捉了适应性行为,如重定向和减速,没有明确的规则,增强零售分析.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 了解客户的移动对于零售优化至关重要.
- 现有的模型往往缺乏捕捉复杂社会互动的能力.
- 需要数据驱动的方法来在动态环境中建模现实的人类行为.
研究的目的:
- 在零售环境中开发一个社会响应的客户轨迹的生成模型.
- 为了使模型能够直接从室内定位数据中学习适应性行为.
- 为轨迹生成提供可扩展和可解释的方法.
主要方法:
- 提出了一个基于变压器的生成模型.
- 客户轨迹以象征性代币表示,包括自我位置和邻居位置.
- 引入了一个不对称的损失掩盖方案,以专注于焦点客户行为.
- 该模型使用高分辨率的室内定位数据进行训练.
- 验证是使用大型基于代理的模拟进行的.
主要成果:
- 该模型成功地复制了适应性客户行为,如减速和重新定向.
- 它复制了观察到的空间密度模式和停留时间分布.
- 由于拥堵而导致的减速被准确地模拟.
- 在代理模拟中展示了分散的,数据驱动的决策.
结论:
- 拟议的变压器模型有效地学习了社会响应的客户轨迹.
- 它提供了一种新的,数据驱动的方法来理解和模拟商业空间中的行人行为.
- 该方法为零售分析和商店设计提供了一个可扩展和可解释的工具.
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