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噪音污染会影响出行方式的选择吗?随机森林应用。

Does noise pollution influence modal choices? A random forest application.

作者信息

Calafiore Alessia, Tong Ki

机构信息

Edinburgh School of Architecture and Landscape Architecture, University of Edinburgh, Edinburgh, United Kingdom.

出版信息

PLoS One. 2025 Jun 23;20(6):e0325249. doi: 10.1371/journal.pone.0325249. eCollection 2025.

Abstract

This work investigates the relationship between noise pollution and modal choices exploring and comparing two different urban contexts: Greater London and Brisbane. To achieve this, data on commuting flows by mode of transport and estimated noise pollution have been obtained and combined with measures to characterise the built environment which demonstrated to have an influence on modal choices. Random forest models have shown very good performances in solving classification problems to predict transport modes and allow the exploration of non-linear relationships between the predicted classes and explanatory variables. Two random forest models have been tuned, trained and tested to investigate the association between modal choices and contextual variables, including noise pollution, in Greater London and Brisbane. Results have shown that noise levels play a role in predicting modal choices in Greater London, while the characteristics of the built environment are more relevant when predicting modal choices in Brisbane. Furthermore, we find that walking and cycling, despite being both active travel modes, are influenced by very different factors, with cycling displaying patterns more similar to those characterising driving. Evidence showing the varying relationships between walking and cycling with contextual variables, e.g. noise levels, building and street density, presence of amenities can inform more targeted policies to encourage active travel.

摘要

本研究探讨并比较了大伦敦和布里斯班这两个不同城市环境中,噪声污染与出行方式选择之间的关系。为此,获取了按交通方式划分的通勤流量数据以及估计的噪声污染数据,并将其与用于描述建成环境特征的指标相结合,这些指标已被证明会对出行方式选择产生影响。随机森林模型在解决分类问题以预测交通方式方面表现出色,并能探索预测类别与解释变量之间的非线性关系。为研究大伦敦和布里斯班出行方式选择与包括噪声污染在内的环境变量之间的关联,对两个随机森林模型进行了调优、训练和测试。结果表明,噪声水平在预测大伦敦的出行方式选择时发挥作用,而在预测布里斯班的出行方式选择时,建成环境的特征更为重要。此外,我们发现步行和骑行虽然都是主动出行方式,但受不同因素影响,骑行显示出与驾车更为相似的模式。表明步行和骑行与环境变量(如噪声水平、建筑和街道密度、便利设施的存在)之间存在不同关系的证据,可为鼓励主动出行的更具针对性的政策提供参考。

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