女性骑自行车者面临的自然障碍以及如何克服这些障碍:对自行车共享计划的跨国研究

Richard Bean, Dorina Pojani, Jonathan Corcoran
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摘要

在世界范围内,城市自行车运动中的性别差距相当大,大多数骑车者都是中青年男性。在当前的研究中,我们首先捕捉了女性面临的一系列骑行障碍,然后对三种自然障碍(恶劣天气、坡度和黑暗)是否以及在多大程度上影响了共享单车系统的女性用户进行了实证调查。为了进行分析,我们将 2 亿多次共享单车出行的性别与精细的天气、坡度和日落/日出数据进行了空间整合。通过对全球十个城市 14 年间的广义加法模型进行计算,我们发现风力和降水不利于骑车出行,对女性的影响比男性更大。同样,在许多城市,较陡的坡度也是女性共享单车用户的一大障碍。在每个城市,女性在黑暗中(即日出前和日落后)的出行次数都少于男性。与其他城市相比,在自行车骑行率较高的城市,无论是否存在自然障碍,女性的自行车骑行率随着年龄的增长而下降。为了克服恶劣天气、山地和黑暗带来的障碍,我们建议:(a)共享单车车队部分电气化;(b)减少自行车道沿线的暴露(通过人造遮阳棚或树冠);以及(c)自行车道沿线充足的夜间照明。本着开放科学的精神,本文所依据的所有数据和代码均已在 Mendeley 上提供:https://data.mendeley.com/datasets/vmy42hywwx/1。
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Natural barriers facing female cyclists and how to overcome them: A cross national examination of bikesharing schemes

Worldwide, the gender gap in urban cycling is considerable, with most cyclists being young to middle-aged men. In the current study, we first capture the suite of cycling barriers facing women before empirically investigating whether and how much three natural barriers (inclement weather, hilliness, and darkness) impact female users of bikesharing systems. For the analysis, we spatially integrate gender for more than 200 million bikesharing trips with fine-grained weather, gradient, and sunset/sunrise data. Computing a suite of the generalized additive models for ten cities worldwide covering a period of 14 years, we find that wind and precipitation disincentivise cycling, and more so for women than for men. Similarly, steeper gradients are a significant barrier for female bikeshare users for many cities. In every city, women make fewer trips in the dark (i.e., before sunrise and after sunset) compared to men. In higher-cycling cities, regardless of natural barriers, cycling declines less with age for women compared to other cities. To overcome the barriers presented by inclement weather, hilliness, and darkness we recommend (a) partial electrification of bikesharing fleets, (b) reduced exposure along bicycle paths (through manufactured shelters or tree canopies), and (c) adequate nighttime lighting along cycling paths. In the spirit of open science, all data and code on which this paper is based have been provided on Mendeley: https://data.mendeley.com/datasets/vmy42hywwx/1.

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