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虚拟人群疏散中动态路径选择过程中人类的信息使用情况。

Information use by humans during dynamic route choice in virtual crowd evacuations.

作者信息

Bode Nikolai W F, Kemloh Wagoum Armel U, Codling Edward A

机构信息

Department of Mathematical Sciences , University of Essex , Colchester CO4 3SQ, UK ; Department of Engineering Mathematics , University of Bristol , Bristol BS8 1UB, UK.

Jülich Supercomputing Centre , Forschungszentrum Jülich GmbH , 52428 Jülich, Germany.

出版信息

R Soc Open Sci. 2015 Jan 21;2(1):140410. doi: 10.1098/rsos.140410. eCollection 2015 Jan.

Abstract

We conducted a computer-based experiment with over 450 human participants and used a Bayesian model selection approach to explore dynamic exit route choice mechanisms of individuals in simulated crowd evacuations. In contrast to previous work, we explicitly explore the use of time-dependent and time-independent information in decision-making. Our findings suggest that participants tended to base their exit choices on time-dependent information, such as differences in queue lengths and queue speeds at exits rather than on time-independent information, such as differences in exit widths or exit route length. We found weak support for similar decision-making mechanisms under a stress-inducing experimental treatment. However, under this treatment participants were less able or willing to adjust their original exit choice in the course of the evacuation. Our experiment is not a direct test of behaviour in real evacuations, but it does highlight the role different types of information and stress play in real human decision-making in a virtual environment. Our findings may be useful in identifying topics for future study on real human crowd movements or for developing more realistic agent-based simulations.

摘要

我们对450多名人类参与者进行了一项基于计算机的实验,并使用贝叶斯模型选择方法来探索模拟人群疏散中个体的动态出口路线选择机制。与之前的研究不同,我们明确探讨了决策中时间相关信息和时间无关信息的使用情况。我们的研究结果表明,参与者倾向于基于时间相关信息做出出口选择,例如出口处队列长度和队列速度的差异,而不是基于时间无关信息,例如出口宽度或出口路线长度的差异。我们发现,在一种诱导压力的实验处理下,对类似决策机制的支持较弱。然而,在这种处理下,参与者在疏散过程中不太能够或不太愿意调整他们最初的出口选择。我们的实验并非对实际疏散行为的直接测试,但它确实凸显了不同类型的信息和压力在虚拟环境中真实人类决策中所起的作用。我们的研究结果可能有助于确定未来关于真实人类人群运动研究的主题,或用于开发更逼真的基于智能体的模拟。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd4f/4448793/5e8c1ae75f6e/rsos140410-g1.jpg

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