Detecting older pedestrians and aging-friendly walkability using computer vision technology and street view imagery
Detecting older pedestrians and aging-friendly walkability using computer vision technology and street view imagery
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DOI：
10.1016/j.compenvurbsys.2023.102027
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Dongwei Liu;Ruoyu Wang;George Grekousis;Ye Liu;Yi Lu
中科院分区：
文献类型：
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作者：
Dongwei Liu;Ruoyu Wang;George Grekousis;Ye Liu;Yi Lu
作为一种新兴的、可免费获取的城市大数据，街景图像（SVI）已被证明是研究人类行为、建筑环境及其相互作用等各种城市现象的有用资源。然而，由于技术上的限制，以往的研究往往集中在普通行人身上，而忽略了某些人群亚群，如老年人。在本研究中，我们开发了一种使用SVI检测老年行人的创新方法。我们采用迁移学习训练了一个能够准确检测SVI上老年行人的模型，准确率达到87.1%。以香港为例，我们创建了一个由72,689张街景全景图组成的数据集，并检测到7763名老年行人和29,231名非老年行人。我们进一步可视化了检测到的老年行人的分布，发现老年行人与老年居住人口之间存在显著的空间差异。为了解释这种空间差异，本研究提出了一种基于行人数量与居住人口比例的步行需求和步行环境评价指标。我们还发现，与人口层面的出行调查相比，用该指数评估的行人需求与建成环境的相关性更强。这种新颖的方法可以用来评估老年人的步行需求，以及老年人友好的步行环境。
As an emerging and freely available urban big data, Street View Imagery (SVI) has proven to be a useful resource to examine various urban phenomena in human behavior, the built environment and their interactions. However, due to technical limitations, previous studies often focused on general pedestrians and ignored certain population subgroups such as older adults. In this study, we develop an innovative method for detecting older pedestrians using SVI. We adopted transfer learning to train a model which can accurately detect older pedestrians on SVI with an accuracy of 87.1%.Using Hong Kong as a case study, we created a dataset consisting of 72,689 street view panoramas and detected 7763 older pedestrians and 29,231 non-older pedestrians. We further visualized the distribution of detected older pedestrians and found a significant spatial discrepancy between older pedestrians and residential population of older adults. To account for this spatial discrepancy, this study proposed a novel index to assess pedestrian demand and walking environment based on the ratio of the number of pedestrians and the residential population. We also found pedestrian demand assessed with this index has a stronger correlation with the built environment compared with population-level travel survey. This novel approach can be used to assess pedestrian demand for older adults, as well as aging-friendly walking environment.