探索虚拟环境以评估公共空间的质量

Algorithms Pub Date : 2024-03-16 DOI:10.3390/a17030124
R. Belaroussi, Elie Issa, Leonardo Cameli, C. Lantieri, Sonia Adelé
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引用次数: 0

摘要

在有效设计支持步行和骑自行车等主动交通的基础设施时,人的印象起着至关重要的作用。通过让用户尽早参与设计过程,可以在物理环境建成之前收集到宝贵的意见。这种积极主动的方法可增强设计空间对用户的吸引力和安全性。本研究进行了一项实验,将真实街道观察与沉浸式虚拟现实(VR)访问进行比较,以评估用户感知和公共空间质量。在这项实验中,利用建筑信息模型(BIM)和地理信息系统(GIS)数据,创建了一个大型街区的高分辨率三维城市模型。该模型包含代表各种城市环境的动态元素:带有电车站的公共区域、带有道路的商业街以及带有绿地的住宅操场。参与者在现实中和通过头戴式显示器(HMD)重建的三维场景中看到了相同的现有城市场景。他们被问及与街景质量、步行能力和骑车能力相关的问题。通过问卷调查,计算出了评估公共空间的算法,即可持续交通指标(SUMI)和行人服务水平(PLOS)。该研究量化了这些指标在虚拟现实设置中的相关性,并将其与影响街道使用和停留体验的关键因素联系起来。这项研究有助于了解这些算法在 VR 环境中的适用性,以便在入住前预测未来空间的质量。
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Exploring Virtual Environments to Assess the Quality of Public Spaces
Human impression plays a crucial role in effectively designing infrastructures that support active mobility such as walking and cycling. By involving users early in the design process, valuable insights can be gathered before physical environments are constructed. This proactive approach enhances the attractiveness and safety of designed spaces for users. This study conducts an experiment comparing real street observations with immersive virtual reality (VR) visits to evaluate user perceptions and assess the quality of public spaces. For this experiment, a high-resolution 3D city model of a large-scale neighborhood was created, utilizing Building Information Modeling (BIM) and Geographic Information System (GIS) data. The model incorporated dynamic elements representing various urban environments: a public area with a tramway station, a commercial street with a road, and a residential playground with green spaces. Participants were presented with identical views of existing urban scenes, both in reality and through reconstructed 3D scenes using a Head-Mounted Display (HMD). They were asked questions related to the quality of the streetscape, its walkability, and cyclability. From the questionnaire, algorithms for assessing public spaces were computed, namely Sustainable Mobility Indicators (SUMI) and Pedestrian Level of Service (PLOS). The study quantifies the relevance of these indicators in a VR setup and correlates them with critical factors influencing the experience of using and spending time on a street. This research contributes to understanding the suitability of these algorithms in a VR environment for predicting the quality of future spaces before occupancy.
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