街道空间宜步行性的精细化测度与导控——基于虚拟现实与可穿戴生理传感器的循证分析

The Measurements of Fine-scale Street Walkability and Precise Design Control: An Evidence-based Approach Based on Virtual Reality and Wearable Bio-sensors

随着城市化进程步入"下半场",日益提升的空间品质需求对街道空间宜步行性提出了更高要求。但目前传统的城市更新与设计手法已难以满足人本尺度的精准设计与导控需求,亟须立足于公众感知的精准更新与介入。以广州新、旧城区2类典型街道为案例,立足新技术开展具身循证型的精准分析。一方面基于虚拟现实技术和可视化陈述性偏好法,从主观视角计算各类街道空间特征要素的宜步行性效用水平。另一方面,结合生理传感器技术从客观视角开展建成环境评估,形成对主观视角结论的补充。基于上述两方面,系统性地构建街道空间界面要素与宜步行性的关联性模型,为街道空间的宜步行性提升提供人本尺度的精细化支持,探索数据支持下的人本视角街道设计精准化提升路径。

Accompanying with China’s further urbanization, the public’s increasing demand on spatial quality brings the rising call of street walkability. Nevertheless, current urban design is difficult to achieve the human-based precise design and guidance needs. In this context, it is important to achieve precise urban design interventions based on the public’s perceptions. As a response to this situation,this study attempts to conduct an accurate, evidence-based analysis based on new analytical tools. Two typical streets in Guangzhou’s old and new urban areas are selected as research cases. On one hand, the combination of virtual reality and stated preference is applied to measure the walkability utility of various street space features from the subjective perspective. On the other hand, built environment audit is achieved via wearable biosensors from the objective perspective to verify and further develop the previous results. This analytical approach integrating both subjective and objective perspectives help to build a statistical model between elements of street interface and walkability systematically. The insights achieved in this process helps to provide fine-scale support of street walkability improvement, which illustrates the emerging of a data-informed street design approach.