Snopková Dajana, Tancoš Martin, Herman Lukáš, Juřík Vojtěch
Department of Geography, Faculty of Science, Masaryk University, Brno, Czech Republic.
International Research Team on Internet and Society, Faculty of Social Studies, Masaryk University, Brno, Czech Republic.
Sci Data. 2025 Jan 20;12(1):116. doi: 10.1038/s41597-025-04440-y.
Empirical data on human evacuation behavior are invaluable for adjusting and training computational algorithms that simulate evacuation processes, including agent-based modeling. We provide a dataset on human decision-making during evacuations from virtual buildings, captured using experimental methods that controlled specific building layout parameters. An online experiment assigned participants a random subset of tasks featuring T-intersections. Data from 208 respondents, aged 17 to 71, were analyzed, considering education levels and excluding those with significant technical issues. Quantitative data on user interaction and evacuation route choices included decision time, mouse rotation, and the selected corridor, recorded through mouse clicks on invisible areas of interest. Respondents also self-reported their choice confidence on a Likert scale. Additionally, responses to final retrospective evaluation questionnaires were recorded. This dataset offers diverse research opportunities, particularly in emergency evacuation planning, where understanding evacuation choices in simulations can inform real-world strategies. It supports the development of models to predict human behavior in emergencies using machine learning and predictive modeling and is accessible for both academic and commercial use.
关于人类疏散行为的实证数据对于调整和训练模拟疏散过程的计算算法（包括基于智能体的建模）非常宝贵。我们提供了一个关于人们从虚拟建筑中疏散时决策情况的数据集，该数据集是通过控制特定建筑布局参数的实验方法获取的。一项在线实验为参与者分配了包含T型交叉路口的随机任务子集。分析了208名年龄在17至71岁之间的受访者的数据，考虑了教育水平并排除了存在重大技术问题的受访者。通过鼠标点击不可见的感兴趣区域记录的关于用户交互和疏散路线选择的定量数据包括决策时间、鼠标旋转以及所选走廊。受访者还以李克特量表自我报告了他们选择的信心。此外，记录了对最终回顾性评估问卷的回答。该数据集提供了多样的研究机会，特别是在紧急疏散规划方面，在模拟中了解疏散选择可为现实世界的策略提供参考。它支持使用机器学习和预测建模来开发预测紧急情况下人类行为的模型，并且可供学术和商业使用。
