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Clearing the path to transcend barriers to walking: Analysis of associations between perceptions and walking behaviour

机译：清理路径以超越行走障碍：看法与行走行为之间的关联分析

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Walkability is much studied, but the relative importance of perceptions and motivations is still not consensual. This study took a holistic approach to examine the comparative importance of a range of possible perceptions, motivations and individual characteristics on walking levels. Data from Auckland Transport's Active Modes online survey (AT survey, N = 4,114) cap tured environmental perceptions and travel behaviour. Machine learning (gradient boost ing) was used to predict walking levels from perceptual data and individual characteristics and determine the relative importance of each variable. Strong predictors of walking included the use of public transport, walking perceived as saving money and avoiding parking hassle, age group, and overall satisfaction with walking. Surprisingly, the importance of expected dimensions such as perceived availability of destinations or internal motivations was null in the general model. These findings suggest a more holistic view of walking behaviour is needed, one that moves beyond the pure availability of destinations. (c) 2021 Elsevier Ltd. All rights reserved.
机译：步行能力很多，但感知和动机的相对重要性仍然不一致。本研究采用整体方法来检查一系列可能的感知，动机和行走水平特征的比较重要性。来自奥克兰运输的积极模式的数据在线调查（在调查中，N = 4,114）盖帽的环境看法和旅行行为。机器学习（梯度提升）用于预测来自感知数据和单个特征的步行级别，并确定每个变量的相对重要性。行走的强大预测因子包括使用公共交通工具，走路被认为是节省金钱，避免停车麻烦，年龄组，以及走路的总体满意度。令人惊讶的是，预期尺寸的重要性，例如感知到目的地或内部动机的可用性是常规模型中的空。这些调查结果表明需要更全面的行走行为观点，这是一个超越目的地纯粹可用性的人。 （c）2021 elestvier有限公司保留所有权利。

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