基于密度聚类的多向行人流群集区域分布比较

为预防公共场所的行人安全事故，优化和改善人群安全管理，基于情景实验的数据，利用密度峰值算法和具有噪声的密度聚类算法，从不同时刻分布变化的角度，分别选取单走廊双向行人流、90°和120°交叉路口的行人流场景研究行人流群集区域的分布状态，并比较了两种算法的聚类效果和参数差异,得出场景实验数据中行人流群集区域的分布规律和变化特征。研究发现聚类簇在3个场景的行人移动过程中均是动态变化的，不会处在某个稳定的聚类状态。使用该方法识别密集人群的潜在群集区域及位置，可以观察场景内安全隐患区域，提前在这些区域放置引导疏散设施，同时做好全路段防护，提高行人群集疏散的效率及安全性。

Comparison among the collection region distributions of multidirectional pedestrian flows based on density clustering

To prevent pedestrian safety accidents and optimize and help crowd security management in public places, the collection region distributions of crowd are studied at three common multidirectional flow intersections of 90°, 120° and single corridor two-way pedestrian flow using the density-based spatial clustering of applications with noise (DBSCN) algorithms at different times based on a dataset from scenario experiments. Moreover, the clustering effect and parameter difference of the two algorithms are compared. Study results show the collection region distribution patterns and characteristic changes of the crowd. The clusters cannot remain in a stable clustering state but vary dynamically in 3 subject scenarios. The proposed method can be used to identify potential locations of crowd, observe hidden danger areas, provide evacuation facilities in advance in such areas, and protect the entire section to improve the efficiency and safety of the evacuation process.