Exploring the non-linear associations between spatial attributes and walking distance to transit

被引:164
作者
Tao, Tao [1 ]
Wang, Jueyu [1 ]
Cao, Xinyu [1 ]
机构
[1] Univ Minnesota, Humphrey Sch Publ Affairs, 301 19th Ave S, Minneapolis, MN 55455 USA
关键词
Machine learning; Walking behavior; Station area planning; Built environment; Land use; BOOSTING DECISION TREES; BUILT ENVIRONMENT; COMPACT DEVELOPMENT; ACCESS DISTANCE; SERVICE; STATIONS;
D O I
10.1016/j.jtrangeo.2019.102560
中图分类号
F [经济];
学科分类号
02 ;
摘要
When examining environmental correlates of walking distance to transit stops, few studies report the importance of spatial attributes relative to other factors. Furthermore, previous studies often assume that they have linear relationships with walking distance. Using the 2016 Transit On Board Survey in the Minneapolis and St. Paul Metropolitan Area, this study adopted the gradient boosting decision trees method to examine the relationships between walking distance and spatial attributes. Results showed that spatial attributes collectively have larger predictive power than other factors. Moreover, they tend to have non-linear associations with walking distance. We further identified the most effective ranges of spatial attributes to guide stop area planning and stop location choice in the region.
引用
收藏
页数:11
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