A study on street walkability for older adults with different mobility abilities combining street view image recognition and deep learning - The case of Chengxianjie Community in Nanjing (China)

IF 7.1 1区 地球科学 Q1 ENVIRONMENTAL STUDIES
Yinan Chen , Xiaoran Huang , Marcus White
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Abstract

China is on the brink of transitioning into an aged society, resulting in a growing demand for an age-friendly street-built environment. However, previous research has paid limited attention to the differentiated walking needs of older adults. To address this gap, this study investigated the relationship between street-built environments and the subjective perception of older adults with different physical capabilities, focusing on safety, comfort, and interest. The older adults were classified into three types based on their physical mobility abilities. The TrueSkill algorithm was used to develop an online image selection website to obtain perception scores for sampled pictures from these three types of older adults. Image segmentation and deep learning were combined to extract indices of street view factors, and machine learning was used to train a scoring prediction model for all streetscape pictures of the area. The study found differences in the subjective perception among all three types of older adults, namely independent elderly (A), mediated-assisted elderly (B), and dependent elderly (C). Type A older adults might be attracted to factors related to the interest of walking despite their negative impact on safety and comfort; Type B older adults were more concerned about street conditions for safety and comfort. Type C older adults were prone to the convenience of barrier-free access and visibility. This study contributes to the study of walkability by providing a research framework for the subjective walking perceptions of older adults with different physical capabilities. Additionally, the visualized walkability map can serve as a reference for architects and urban designers, further strengthening the development of age-friendly communities with the aid of human-centric computational analysis, evaluation, and design.

结合街景图像识别和深度学习,研究不同行动能力老年人的街道步行便利性--以南京市城厢街道社区(中国)为例
中国正处于向老龄化社会过渡的边缘,因此对老年友好型街道环境的需求日益增长。然而,以往的研究对老年人不同的步行需求关注有限。针对这一空白,本研究调查了街道环境与不同体能老年人主观感知之间的关系,重点关注安全性、舒适性和趣味性。根据老年人的身体活动能力将其分为三种类型。我们使用 TrueSkill 算法开发了一个在线图片选择网站,以获得这三类老年人对采样图片的感知评分。结合图像分割和深度学习来提取街景因素指数,并使用机器学习来训练该地区所有街景图片的评分预测模型。研究发现,独立型老年人(A)、介助型老年人(B)和依赖型老年人(C)这三种类型的老年人在主观感知上存在差异。A 型老年人可能会被与步行兴趣有关的因素所吸引,尽管这些因素会对安全和舒适产生负面影响;B 型老年人则更关注街道的安全和舒适条件。C 型老年人则更倾向于无障碍通道的便利性和可视性。本研究为不同体能的老年人的主观步行感知提供了一个研究框架,从而为步行能力研究做出了贡献。此外,可视化步行能力地图可作为建筑师和城市设计师的参考,借助以人为本的计算分析、评估和设计,进一步加强老年友好社区的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
13.30
自引率
7.40%
发文量
111
审稿时长
32 days
期刊介绍: Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics.
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