疏散行为的预测因素:关于受访者路线选择和网络互动的数据集
Dajana Snopková1, Martin Tancoš2, Lukáš Herman1
1Department of Geography, Faculty of Science, Masaryk University, Brno, Czech Republic.
Scientific data
|January 20, 2025
概括
这项研究介绍了虚拟建筑中人类疏散行为的新数据集,这对于改善疏散模拟模型至关重要. 这些数据有助于制定更好的应急规划和人类应对预测模型.
科学领域:
- 计算机科学 计算机科学
- 人与计算机的交互
- 应急管理 应急管理
背景情况:
- 疏散模拟的计算模型需要经验数据来准确训练和验证.
- 了解疏散期间的人类决策对于有效的紧急规划至关重要.
- 现有的数据集可能无法从受控虚拟环境中捕获细微的行为数据.
研究的目的:
- 为了提供一个新的数据集的人类决策在虚拟建筑疏散期间.
- 为了促进基于代理的建模和其他计算疏散模拟的改进.
- 支持对紧急疏散策略和人类行为预测的研究.
主要方法:
- 进行了一项在线实验,其中208名参与者 (年龄在17-71岁之间) 浏览了具有T交叉点的虚拟建筑.
- 通过点击感兴趣的区域收集了关于决策时间,鼠标移动和路线选择的定量数据.
- 还记录了参与者的信心和回顾性问卷答复.
主要成果:
- 该数据集包括从受控虚拟环境中详细的交互数据和疏散路线选择.
- 分析考虑了教育水平等因素,排除了技术问题参与者.
- 收集的数据可以研究模拟紧急疏散的决策过程.
结论:
- 这一数据集对于改进疏散规划中使用的计算模型非常有价值.
- 它支持机器学习和人类在紧急情况下行为的预测模型的发展.
- 可访问的数据可以为现实世界的应急战略提供信息,并提高建筑物的安全性.
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