一个新的数据驱动的多因素行人流动风险评估框架,以避免 stampede 事件
Zi-Xuan Zhou1, Kai Liu2, Pei-Yang Wu3
1College of Engineering, Peking University, Beijing, China.
Accident; analysis and prevention
|August 30, 2025
概括
这项研究引入了一个数据驱动的框架来监测高密度的人群,提高公共空间的安全性. 通过分析行人流量和建筑物容量,可以及早发现过度拥挤的风险.
科学领域:
- * 计算社会科学
- 城市规划与安全
- 在人群管理中使用人工智能
背景情况:
- 现有的人群模拟工具难以应对现实世界的复杂性,
- 在公共场所监控高密度的人群对于防止事故和确保安全至关重要.
- 公共区域通常具有复杂的空间设计,这挑战了当前的监测方法.
研究的目的:
- * 引入新的数据驱动,多代理框架来评估人群动态和早期预警条件.
- 开发一个能够处理各种空间布局和行人行为的系统.
- 加强预测和预防公共空间过度拥挤的事件.
主要方法:
- 使用实时视觉信息和强化学习进行智能决策.
- 使用自代算法进行轨迹规划,模仿现实世界的行人运动.
- 确保模型在各种场景中的适应性,而无需对参数进行微调.
主要成果:
- 框架准确地复制了各种复杂的场景中的行人流动.
- * 随着密度的增加,发现了行人流量的不连续状态转换.
- 提出了一种检测建筑物交通容量的方法,并建立拥挤警告值.
结论:
- * 开发的框架有效地评估人群动态,并预测早期预警条件.
- 合理的空间布局和信息指导可以显著改善流动性并减少跑风险.
- 这种方法提供了一种无需扩展架构的积极人群安全管理方法.
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