Juřík Vojtěch, Uhlík Ondřej, Snopková Dajana, Kvarda Ondřej, Apeltauer Tomáš, Apeltauer Jiří
Department of Psychology, Faculty of Arts, Masaryk University, Brno, Czech Republic.
Institute of Computer Aided Engineering and Computer Science, Faculty of Civil Engineering, Brno University of Technology, Brno, Czech Republic.
Heliyon. 2023 Mar 4;9(3):e14275. doi: 10.1016/j.heliyon.2023.e14275. eCollection 2023 Mar.
Agent-based evacuation modeling represents an effective tool for making predictions about evacuation aspects of buildings such as evacuation times, congestions, and maximum safe building capacity. Collection of real behavioral data for calibrating agent-based evacuation models is time-consuming, costly, and completely impossible in the case of buildings in the design phase, where predictions about evacuation behavior are especially needed. In recent years evacuation experiments conducted in virtual reality (VR) have been frequently proposed in the literature as an effective tool for collecting data about human behavior. However, empirical studies which would assess validity of VR-based data for such purposes are still rare and considerably lacking in the agent-based evacuation modeling domain. This study explores opportunities that the VR behavioral data may bring for refining outputs of agent evacuation models. To this end, this study employed multiple input settings of agent-based evacuation models (ABEMs), including those based on the data gathered from the VR evacuation experiment that mapped out evacuation behaviors of individuals within the building. Calibration and evaluation of models was based on empirical data gathered from an original evacuation exercise conducted in a real building (N = 35) and its virtual twin (N = 38). This study found that the resulting predictions of single agent models using data collected in the VR environment after proposed corrections have the potential to better predict real-world evacuation behavior while offering desirable variance in the data outputs necessary for practical applications.
基于智能体的疏散建模是一种有效的工具，可用于预测建筑物疏散方面的情况，如疏散时间、拥堵情况和建筑物的最大安全容量。收集用于校准基于智能体的疏散模型的实际行为数据既耗时又昂贵，对于处于设计阶段的建筑物而言更是完全不可能，而在设计阶段，对疏散行为的预测尤为重要。近年来，虚拟现实（VR）中进行的疏散实验在文献中经常被提议作为收集人类行为数据的有效工具。然而，评估基于VR的数据用于此类目的有效性的实证研究仍然很少，在基于智能体的疏散建模领域也相当缺乏。本研究探讨了VR行为数据可能为改进智能体疏散模型输出带来的机会。为此，本研究采用了基于智能体的疏散模型（ABEMs）的多种输入设置，包括基于从VR疏散实验收集的数据的设置，该实验描绘了建筑物内人员的疏散行为。模型的校准和评估基于从真实建筑物中进行的原始疏散演习（N = 35）及其虚拟孪生体（N = 38）收集的实证数据。本研究发现，在进行提议的修正后，使用VR环境中收集的数据的单智能体模型的预测结果有可能更好地预测现实世界中的疏散行为，同时在实际应用所需的数据输出中提供理想的方差。
