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 investigates the relationship between street-built environment and the subjective perception of older residents with different function levels, focusing on safety, comfort, and interest (Figure 1.). The demographics of the older adults are classified into three types based on their physical condition. The TrueSkill algorithm is used to develop an online image selection website to obtain perception scores for sampled pictures from these three types of older adults (Figure 2.). Street view segmentation and deep learning are combined to extract indices of street public space characteristics, and machine learning is used to train a scoring prediction model for all streetscape pictures of the area (Figure 3.). The study found differences in the subjective perception amongst all three types of older adults, namely independent elderly (A), assisted elderly (B), dependent elderly (C). Type A older adults with better walking ability are more concerned about factors related to the interest of walking, while type B older adults with certain walking limitations focus on safety and comfort. Type C older adults who have lost their independent walking ability pay more attention to the convenience of barrier-free access. 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.

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