TY - JOUR
T1 - Exploring non-linear and synergistic effects of green spaces on active travel using crowdsourced data and interpretable machine learning
AU - Yang, Linchuan
AU - Yang, Haosen
AU - Yu, Bingjie
AU - Lu, Yi
AU - Cui, Jianqiang
AU - Lin, Dong
N1 - This study was supported by the National Natural Science Foundation of China (Nos. 52278080 and 52308081). The authors are grateful to the reviewers for their constructive comments.
PY - 2024/1
Y1 - 2024/1
N2 - The relationship between green spaces and active travel has been extensively studied. However, the majority of previous studies relied on small datasets concerning active travel and inadequately explored non-linear and/or synergistic effects. This study uses multi-source data and interpretable machine learning techniques to identify the non-linear and synergistic effects of green spaces in Chengdu (China) on two types of active travel: cycling and running. Crowdsourced data from Strava collected in December 2021 is used to measure city-wide active travel levels. Meanwhile, green spaces are evaluated from two viewpoints: overhead view and eye level, with the latter assessed using Baidu Street View imagery. The findings demonstrate that green spaces can account for up to 20% of the variance in active travel. Generally, the effect of the area of green spaces on active travel is positive. When the area of green spaces reaches a certain threshold, its effect becomes marginal and even negative. The green view index displays complex effects on cycling. Furthermore, this study identifies synergistic effects among predictors (e.g., green view index & river line length).
AB - The relationship between green spaces and active travel has been extensively studied. However, the majority of previous studies relied on small datasets concerning active travel and inadequately explored non-linear and/or synergistic effects. This study uses multi-source data and interpretable machine learning techniques to identify the non-linear and synergistic effects of green spaces in Chengdu (China) on two types of active travel: cycling and running. Crowdsourced data from Strava collected in December 2021 is used to measure city-wide active travel levels. Meanwhile, green spaces are evaluated from two viewpoints: overhead view and eye level, with the latter assessed using Baidu Street View imagery. The findings demonstrate that green spaces can account for up to 20% of the variance in active travel. Generally, the effect of the area of green spaces on active travel is positive. When the area of green spaces reaches a certain threshold, its effect becomes marginal and even negative. The green view index displays complex effects on cycling. Furthermore, this study identifies synergistic effects among predictors (e.g., green view index & river line length).
U2 - 10.1016/j.tbs.2023.100673
DO - 10.1016/j.tbs.2023.100673
M3 - Article
VL - 34
JO - Travel Behaviour and Society
JF - Travel Behaviour and Society
M1 - 100673
ER -