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基于视觉轨迹下的细胞自动机的行人流动模型和多场景疏散模拟研究
Yueyue Chen1, Jinbao Yao1, Chenze Gao1
1School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究将计算机视觉和蜂自动机 (CA) 集成为行人流动模拟. 新方法通过模拟人群行为 (包括恐慌和群体动态) 来提高安全设计,以更好地管理紧急情况.
科学领域:
- 计算社会科学 计算社会科学
- 计算机视觉 计算机视觉
- 交通工程是交通工程.
背景情况:
- 准确的行人流动建模对于公共安全和应急规划至关重要.
- 现有的模型往往缺乏对细微人群行为的现实世界数据集成.
研究的目的:
- 开发一种结合计算机视觉和蜂自动机 (CA) 的跨学科方法,用于精确的行人流动模拟.
- 以数据驱动的过渡概率和行为因素来增强疏散模型.
主要方法:
- 采集和分析了使用YOLOv11和DeepSORT进行高精度轨迹跟踪的单向行人流动视频数据.
- 将轨迹数据转换为CA模型的摩尔社区的数据驱动的过渡概率.
- 开发了一种改进的疏散模型,包含真实数据转换概率,速度密度,恐慌,生命价值和危险动态.
主要成果:
- 模拟显示,适度的恐慌可以缩短疏散时间,但过度的恐慌会导致行为障碍.
- 群体运动受到最慢的个体的限制,更快的危险来源减少了安全行人数.
- 该模型提供了各种紧急情况下人群动态的洞察力.
结论:
- 综合计算机视觉和CA方法为精细的行人疏散模拟提供了一种新方法.
- 研究结果支持改善公共空间的安全设计和应急管理策略.
- 数据驱动的过渡概率显著提高了基于CA的疏散模型的现实性.
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