建筑环境
目的地
TRIPS体系结构
旅游行为
梯度升压
积极生活
运输工程
业务
地理
营销
计算机科学
工程类
旅游
体力活动
人工智能
土木工程
医学
物理医学与康复
考古
随机森林
作者
Jixiang Liu,Bo Wang,Longzhu Xiao
标识
DOI:10.1016/j.jtrangeo.2021.103034
摘要
Active travel has environmental, social, and public health-related benefits. Researchers from diverse domains have extensively studied built-environment associations with active travel. However, limited attention has been paid to distinguishing the associations between built environment characteristics at both the origins and destinations and active travel for working and shopping. Scholars have started to examine non-linear associations of built environment with travel behaviour, but active travel has seldom been a focus. Therefore, this study, selecting Xiamen, China, as the case, utilises a state-of-the-art machine learning method (i.e., extreme gradient boosting) to explore the non-linear associations between built environment and active travel for working and shopping. Our findings are as follows. (1) For both purposes, trip characteristics contribute the greatest, and the built environment is also quite important and has larger collective contributions for active travel than does socioeconomics. (2) The relative importance of built environment on active travel for shopping is evidently larger than that for working. (3) All built-environment variables have non-linear associations with active travel, and associations with active travel for working are generally in inverted U or V shapes, while those with shopping trips have much more complex patterns. (4) Differences in the threshold value and gradient exist between built-environment associations with active travel for working and shopping and between variables at origins and destinations. Decision makers are recommended to meticulously disentangle the complex influences of built environment on active travel and distinguish between diverse purposes to make informed and targeted interventions.
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