Citation

Yang L, Ao Y, Ke J, Lu Y, Liang Y. J. Transp. Geogr. 2021; 94: e103099.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.jtrangeo.2021.103099

PMID

unavailable

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.

Language: en

Keywords

Big data; Machine learning; Population aging; Random forest; Streetscape greenery; Travel behavior; Walking behavior