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Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo

机译:应用梯度增强决策树来检查建筑环境对奥斯陆行驶距离的非线性影响

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摘要

Although many studies have explored the relationship between the built environment and travel behavior, the literature offers limited evidence about the collective influence of built environment attributes, and their non-linear effects on travel. This study innovatively adopts gradient boosting decision trees to fill the gaps. Using data from Oslo, we apply this method to the data on both weekdays and weekends to illustrate the differential effects of built environment characteristics on driving distance. We found that they have a stronger effect on weekdays than on weekends. On weekdays, their collective influence is larger than that of demographics. Furthermore, they show salient non-linear effects on driving distance in both models, challenging the linearity assumption commonly adopted in the literature. This study also identifies effective ranges of distance to different centers and population density, and highlights the important role of sub-centers in driving reduction.
机译:尽管许多研究探索了建筑环境与出行行为之间的关系,但文献提供了关于建筑环境属性的集体影响及其对旅行的非线性影响的有限证据。这项研究创新地采用了梯度提升决策树来填补空白。使用来自奥斯陆的数据,我们将此方法应用于工作日和周末的数据,以说明建筑环境特征对行驶距离的不同影响。我们发现它们对工作日的影响要大于对周末的影响。在工作日,他们的集体影响力大于人口统计信息的影响力。此外,它们在两个模型中均显示出对行驶距离的明显非线性影响,从而挑战了文献中通常采用的线性假设。这项研究还确定了到不同中心和人口密度的有效距离范围,并强调了子中心在减少污染方面的重要作用。

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