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


  • Huyen TK Le Virginia Tech
  • Alyson West The University of North Carolina Highway and Safety Research Center
  • Fionnuala Quinn The Bureau of Good Roads
  • Steve Hankey Virginia Tech



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


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.


Abasahl, F., Kelarestaghi, K. B., & Ermagun, A. (2018). Gender gap generators for bicycle mode choice in Baltimore college campuses. Travel Behavior and Society, 11, 78–85.

Akar, G., Fischer, N., & Namgung, M. (2013). Bicycling choice and gender case study: The Ohio State University. International Journal of Sustainable Transportation, 7(5), 347–365.

Aldred, R. (2016). Cycling near misses: Their frequency, impact, and prevention. Transportation Research Part A: Policy and Practice, 90, 69–83.

Antoniou, C., & Koutsopoulos, H. (2006). Estimation of traffic dynamics models with machine-learning methods. Transportation Research Record: Journal of the Transportation Research Board, 1965, 103–111.

Arentze, T. A., & Timmermans, H. J. P. (2004). A learning-based transportation-oriented simulation system. Transportation Research Part B: Methodological, 38(7), 613–633.

Bonham, J., & Wilson, A. (2012). Bicycling and the life course: The start-stop-start experiences of women cycling. International Journal of Sustainable Transportation, 6(4), 195–213.

Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. doi org/10.1023/


Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

Bühlmann, P., & Hothorn, T. (2007). Boosting algorithms: Regularization, prediction and model fitting. Statistical Science, 22(4), 477–505.

Damant-Sirois, G., & El-Geneidy, A. M. (2015). Who cycles more? Determining cycling frequency through a segmentation approach in Montreal, Canada. Transportation Research Part A: Policy and Practice, 77, 113–125.

Dill, J. (2009a). Bicycling for transportation and health: The role of infrastructure. Journal of Public Health Policy, 30(1), S95–S110.

Dill, J. (2009b). Comfort + convenience = more women biking. The League of American Bicyclists Webinar. Retrieved from on May 8, 2019.

Dill, J., & McNeil, N. (2013). Four types of cyclists? Examination of typology for better understanding of bicycling behavior and potential. Transportation Research Record, 2387(1), 129–138.

Dill, J., & McNeil, N. (2016). Revisiting the four types of cyclists. Transportation Research Record: Journal of the Transportation Research Board, 2587, 90–99.

Ding, A., Zhao, X., & Jiao, L. (2002). Traffic flow time series prediction based on statistics learning theory. In Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems (pp. 727–730). Retrieved from

Ding, C., Cao, X., & Næss, P. (2018). Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo. Transportation Research Part A: Policy and Practice, 110, 107–117.

Emond, C., Tang, W., & Handy, S. (2009). Explaining gender difference in bicycling behavior. Transportation Research Record: Journal of the Transportation Research Board, 2125, 16–25.

Garrard, J. (2003). Healthy revolutions: Promoting cycling among women. Health Promotion Journal of Australia, 14(3), 213–215.

Garrard, J., Handy, S., & Dill, J. (2012). Women and cycling. In City Cycling (pp. 211–234). Cambridge, MA: MIT Press. Retrieved from

Garrard, J., Rose, G., & Lo, S. K. (2008). Promoting transportation cycling for women: The role of bicycle infrastructure. Preventive Medicine, 46(1), 55–59.

Geller, R. (2006). Four types of transportation cyclists. Retrieved from

Gong, L., Kanamori, R., & Yamamoto, T. (2017). Data selection in machine learning for identifying trip purposes and travel modes from longitudinal GPS data collection lasting for seasons. Travel Behavior and Society.

Gong, L., Morikawa, T., Yamamoto, T., & Sato, H. (2014). Deriving personal trip data from GPS data: A literature review on the existing methodologies. Procedia - Social and Behavioral Sciences, 138, 557–565.

Grudgings, N., Hagen-Zanker, A., Hughes, S., Gatersleben, B., Woodall, M., & Bryans, W. (2018). Why don’t more women cycle? An analysis of female and male commuter cycling mode-share in England and Wales. Journal of Transport & Health.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (2nd ed.). New York: Springer-Verlag.

Heesch, K. C., Sahlqvist, S., & Garrard, J. (2012). Gender differences in recreational and transport cycling: A cross-sectional mixed-methods comparison of cycling patterns, motivators, and constraints. International Journal of Behavioral Nutrition and Physical Activity, 9(1), 106.

Jahangiri, A., & Rakha, H. A. (2015). Applying machine learning techniques to transportation mode recognition using mobile phone sensor data. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2406–2417.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An Introduction to statistical learning: With Applications in R. New York: Springer.

Ma, L., Dill, J., & Mohr, C. (2014). The objective versus the perceived environment: What matters for bicycling? Transportation, 41(6), 1135–1152.

McGuckin, N., Zmud, J., & Nakamoto, Y. (2005). Trip-chaining trends in the United States: Understanding travel behavior for policy making. Transportation Research Record: Journal of the Transportation Research Board, 1917, 199–204.

Morris, E. A., & Guerra, E. (2014). Mood and mode: Does how we travel affect how we feel? Transportation, 42(1), 25–43.

Moudon, A. V., Lee, C., Cheadle, A. D., Collier, C. W., Johnson, D., Schmid, T. L., & Weather, R. D. (2005). Cycling and the built environment, a US perspective. Transportation Research Part D: Transport and Environment, 10(3), 245–261.

Nordbakke, S., & Schwanen, T. (2014). Well-being and mobility: A theoretical framework and literature review focusing on older people. Mobilities, 9(1), 104–129.

Páez, A., & Whalen, K. (2010). Enjoyment of commute: A comparison of different transportation modes. Transportation Research Part A: Policy and Practice, 44(7), 537–549.

Pucher, J., Buehler, R., & Seinen, M. (2011). Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies. Transportation Research Part A: Policy and Practice, 45(6), 451–475.

Sibley, A. (2010). Women’s cycling survey: Analysis of results. University of North Carolina Greensboro. Retrieved from

Singleton, P. A. (2017). Exploring the positive utility of travel and mode choice. Retrieved from

Singleton, P. A., & Goddard, T. (2016). Cycling by choice or necessity? Transportation Research Record: Journal of the Transportation Research Board, 2598, 110–118.

Smith, O. (2017). Commute well-being differences by mode: Evidence from Portland, Oregon, USA. Journal of Transport & Health, 4(Supplement C), 246–254.

Teschke, K., Chinn, A., & Brauer, M. (2017). Proximity to four bikeway types and neighborhood-level cycling mode share of male and female commuters. Journal of Transport and Land Use, 10(1) 695–713.

Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28.

Xing, Y., Handy, S. L., & Mokhtarian, P. L. (2010). Factors associated with proportions and miles of bicycling for transportation and recreation in six small US cities. Transportation Research Part D: Transport and Environment, 15(2), 73–81.




How to Cite

Le, H. T., West, A., Quinn, F., & Hankey, S. (2019). Advancing cycling among women: An exploratory study of North American cyclists. Journal of Transport and Land Use, 12(1).