Advancing cycling among women: An exploratory study of North American cyclists

Huyen TK Le, Alyson West, Fionnuala Quinn, Steve Hankey


Past studies show that women cycle at a lower rate than men due to various factors; few studies examine attitudes and perceptions of women cyclists on a large scale. This study aims to fill that gap by examining the cycling behaviors of women cyclists across multiple cities in North America. We analyzed an online survey of 1,868 women cyclists in the US and Canada, most of whom were confident when cycling. The survey recorded respondents’ cycling skills, attitude, perceptions of safety, surrounding environment, and other factors that may affect the decision to bicycle for transport and recreation. We utilized tree-based machine learning methods (e.g., bagging, random forests, boosting) to select the most common motivations and concerns of these cyclists. Then we used chi-squared and non-parametric tests to examine the differences among cyclists of different skills and those who cycled for utilitarian and non-utilitarian purposes. Tree-based model results indicated that concerns about the lack of bicycle facilities, cycling culture, cycling’s practicality, sustainability, and health were among the most important factors for women to cycle for transport or recreation. We found that very few cyclists cycled by necessity. Most cyclists, regardless of their comfort level, preferred cycling on facilities that were separated from vehicular traffic (e.g., separated bike lanes, trails). Our study suggests opportunities for designing healthy cities for women. Cities may enhance safety to increase cycling rates of women by tailoring policy prescriptions for cyclists of different skill groups who have different concerns. Strategies that were identified as beneficial across groups, such as investing in bicycle facilities and building a cycling culture in communities and at the workplace, could be useful to incorporate in long-range planning efforts.


gender; perception; physical activity; active travel; machine learning

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