On the use of virtual immersive reality for discrete choice experiments to modelling pedestrian behaviour
On the use of virtual immersive reality for discrete choice experiments to modelling pedestrian behaviour
复制标题
DOI：
10.1016/j.jocm.2020.100251
复制
发表时间：
2020-12-01
影响因子：
2.4
通讯作者：
Cantillo, V.
中科院分区：
文献类型：
--
作者：
Arellana, J.;Garzon, L.;Cantillo, V.
模拟人的行为是一项复杂的任务，不仅因为其内在的复杂性，而且因为其与环境和其他个人的互动。离散选择实验的传统形式涉及使用文本和图像。然而，在离散选择实验中，越来越多的人倾向于使用能够更真实地表示复杂和动态属性的工具。虚拟沉浸式现实环境（维雷）是一种更好地理解和呈现使用纯文本实验难以理解的变量的资源。尽管维雷实验有其优点，但其成本很高，并且在实际使用中具有额外的复杂性。本研究旨在分析使用维雷离散选择建模的好处，比较传统的纯文本格式与图像调查在两种情况下。第一个背景考虑在选择穿过城市街道的替代方案时对行人行为的研究。第二种情况涉及在有遮盖的运动竞技场内的紧急出口情况下人们的行为。结果表明，协助使用维雷允许受访者感知环境动态比传统的选择实验。维雷增加了现实主义，似乎提高了被访者对建模者所创建的环境中复杂元素的认知理解。
Modelling people's behaviour is a complex task, not only because of their intrinsic complexity but also because of their interaction with the environment and other individuals. The traditional format for discrete choice experiments involves the use of text and images. However, there is a growing tendency for using tools that offer a more realistic representation of complex and dynamic attributes in discrete choice experiments. The Virtual Immersive Reality Environment (VIRE) emerges as a resource for a better understanding and presentation of variables that are difficult to understand using text-only experiments. Despite its advantages, VIRE experiments are costly and have additional complications in their practical use. This study aims to analyse the benefits of using VIRE in discrete choice modelling, comparing with the traditional text-only format with image surveys in two contexts. The first context considers the study of pedestrian behaviour when choosing alternatives for crossing an urban street. The second context deals with the behaviour of people during an emergency exit situation inside a covered sports arena. The results suggest that the assisted use of VIRE allows respondents to perceive environmental dynamics better than traditional choice experiments. VIRE adds realism and seems to improve a respondent's cognitive understanding of complex elements in the environment created by the modeller.