期刊
JOURNAL OF PLANNING EDUCATION AND RESEARCH
卷 43, 期 3, 页码 637-652出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/0739456X20915765
关键词
machine learning; travel behavior; land use; walking; community design
This study uses Gradient Boosting Decision Trees to explore the nonlinear relationships between the built environment and active travel in the Twin Cities. It found that the built environment has more predictive power for active travel than demographics, and parks, proximity to downtown, and transit access have important influences. The threshold effects of built environment variables provide insights for planning practice.
Active travel is important to public health and the environment. Previous studies substantiate built environment influences active travel, but they seldom assess its overall contribution. Most of the studies assume that built environment characteristics have linear associations with active travel. This study uses Gradient Boosting Decision Trees to explore nonlinear relationships between the built environment and active travel in the Twin Cities. Collectively, the built environment has more predictive power for active travel than demographics, and parks, proximity to downtown, and transit access have important influences. The threshold effects of built environment variables help inform planning practice.
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