A Data Driven Approach to Simulate Pedestrian Competitiveness Using the Social Force Model
A Data Driven Approach to Simulate Pedestrian Competitiveness Using the Social Force Model
复制标题
DOI：
10.17815/cd.2021.118
复制
发表时间：
2022-02
影响因子：
--
通讯作者：
Gengshen Cui;D. Yanagisawa;Nishinari Katsuhiro
中科院分区：
文献类型：
--
作者：
Gengshen Cui;D. Yanagisawa;Nishinari Katsuhiro
行人疏散动力学的研究对于认识和预防人体踩踏事故具有重要意义。由于安全问题，再现真实紧急疏散的经验方法是不可能的。基于数值模拟的理论方法引起了研究者的关注。在行人疏散模拟中，如何模拟行人竞争以再现紧急疏散是一个关键问题。基于社会力模型，研究者尝试通过调整模型参数来模拟行人竞争力。然而，在大多数情况下，采用手工制作的值而不进行校准，因此可能会产生不切实际的结果。在这项研究中，我们采用了差分进化算法来确定最佳的参数规格的社会力量模型调整的经验数据。我们进行了行人实验，其中五名参与者，包括病人和不耐烦的人进行通过一个狭窄的走廊。以仿真结果与经验数据之间的距离为目标函数，生成最小化问题。采用差分进化算法搜索最优参数组合。我们发现，虽然在初始化时所有的参数值是随机确定的，病人和不耐烦的行人之间的差异可以通过调整经验数据来捕获。这突出了需要更好地理解和研究行人竞争力方面的异质性。
The research of pedestrian evacuation dynamics is of significance to understanding and preventing human stampedes. Since empirical approach of reproducing true emergency evacuations is impossible due to safety issues. Theoretical approach based on numerical simulation has called the attention from researchers. In the simulation of pedestrian evacuation, a critical problem is how to simulate pedestrian competitiveness to reproduce emergency evacuation. Based on the social force model, researchers have tried to simulate pedestrian competitiveness through adjusting some model parameters. However, in most cases handcrafted values are adopted without calibration, thus unrealistic results might be produced. In this study, we applied a differential evolutionary algorithm to determine the optimal parameter specifications of the social force model by adjustment to empirical data. We conducted pedestrian experiments where five participants including patient and impatient individuals proceeded through a narrow corridor. Taking the distance between simulation results and empirical data as objective function, a minimization problem was generated. A differential evolutionary algorithm was adopted to search for the optimal combination of parameters. We found that though at initialization all the parameter values were randomly determined, the difference between patient and impatient pedestrians could be captured by adjustment to empirical data. This highlights the need to better understand and research pedestrian heterogeneity in terms of competitiveness.