Journal
JOURNAL OF TRANSPORT GEOGRAPHY
Volume 94, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jtrangeo.2021.103099
Keywords
Streetscape greenery; Big data; Machine learning; Random forest; Travel behavior; Walking behavior; Population aging
Categories
Funding
- Fundamental Research Funds for the Central Universities of China [2682021CX097]
- Education and Scientific Research Grant of Sichuan Province [SCJG20A110]
- Talent Cultivation Quality and Teaching Reform Project of Ideological and Political Theory Course of Chengdu University of Technology [208002020SZ009]
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This study analyzed the impact of streetscape greenery on the walking behavior of older adults using neural network technology and machine learning methods. The results showed that streetscape greenery has a positive effect on walking propensity within a certain range, but the positive association no longer holds outside the range.
Population aging is a conspicuous demographic trend shaping the world profoundly. Walking is a critical travel mode and physical activity for older adults. As such, there is a need to determine the factors influencing the walking behavior of older people in the era of population aging. Streetscape greenery is an easily perceived builtenvironment attribute and can promote walking behavior, but it has received insufficient attention. More importantly, the non-linear effects of streetscape greenery on the walking behavior of older adults have not been examined. We therefore use readily available Google Street View imagery and a fully convolutional neural network to evaluate human-scale, eye-level streetscape greenery. Using data from the Hong Kong Travel Characteristic Survey, we adopt a machine learning technique, namely random forest modeling, to scrutinize the nonlinear effects of streetscape greenery on the walking propensity of older adults. The results show that streetscape greenery has a positive effect on walking propensity within a certain range, but outside the range, the positive association no longer holds. The non-linear associations of other built-environment attributes are also examined.
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