Abstract
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 built-environment 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 non-linear 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.
Original language | English |
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Article number | 103099 |
Journal | Journal of Transport Geography |
Volume | 94 |
DOIs | |
Publication status | Published - Jun 2021 |
Keywords
- Big data
- Machine learning
- Population aging
- Random forest
- Streetscape greenery
- Travel behavior
- Walking behavior
ASJC Scopus subject areas
- Geography, Planning and Development
- Transportation
- General Environmental Science