Apeltauer Tomáš, Uhlík Ondřej, Apeltauer Jiří, Juřík Vojtěch
Institute of Computer Aided Engineering and Computer Science, Faculty of Civil Engineering, Brno University of Technology, Brno, Czech Republic.
Department of Psychology, Faculty of Arts, Masaryk University, Brno, Czech Republic.
Sci Data. 2024 Nov 20;11(1):1254. doi: 10.1038/s41597-024-04071-9.
Understanding pedestrian movement remains crucial for designing efficient and safe transportation structures such as terminals, stations, or airports. The significance of conducting a granular analysis in pedestrian mobility dynamics research is evident in refining crowd behavior modeling. It is essential for gaining insights into potential terminal layouts, crowd management strategies, and evacuation procedures, all of which enhance safety and efficiency. In this context, we offer an original empirical dataset of 24,000,000 samples of trajectory spatial movement at traffic terminals in Havlíčkův Brod and Pardubice, Czech Republic. The dataset was collected using a high-resolution camera system installed at the railway station. Subsequently, algorithmic post-processing was applied to extract anonymous data on the spatial movement of recorded pedestrians. Thanks to this dataset, researchers can delve into the distances between pedestrians in a transportation terminal, considering factors such as group composition, group-to-group distances, and movement speed.
了解行人移动情况对于设计高效且安全的交通设施（如航站楼、车站或机场）仍然至关重要。在行人移动动态研究中进行细致分析的重要性在完善人群行为建模方面显而易见。这对于深入了解潜在的航站楼布局、人群管理策略和疏散程序至关重要，所有这些都能提高安全性和效率。在此背景下，我们提供了一个原始实证数据集，该数据集包含捷克共和国哈夫利奇科夫布罗德和帕尔杜比采交通枢纽2400万个轨迹空间移动样本。该数据集是使用安装在火车站的高分辨率摄像系统收集的。随后，通过算法后处理来提取所记录行人空间移动的匿名数据。借助这个数据集，研究人员可以深入研究交通枢纽中行人之间的距离，同时考虑群体构成、群体间距离和移动速度等因素。
