Data shapes policy. Governments, transport authorities and local councils rely on evidence to identify problems, allocate funding and evaluate whether interventions are working. So when people's experiences are absent from datasets, they are often absent from decision-making too.¹

For much of modern history, women were largely invisible in research. The experiences of men were repeatedly treated as the norm against which everyone else was measured, with women's experiences overlooked or even assumed to be the same. Although decades of feminist advocacy have led to the wider collection of sex-disaggregated data across fields such as health, employment, crime and transport, data gaps persist and the experiences of women and other marginalised genders continue to be underrepresented or inadequately understood.² 

Nevertheless, the collection of sex-disaggregated data has been an important step forward. It has made gender inequalities more visible and enabled policymakers to identify where women experience different outcomes from men.¹ In cycling, this matters because cycling is not experienced equally by everyone. The National Travel Survey routinely reports cycling by sex, and its latest figures show a substantial difference between men and women: in 2024, males made more cycling trips on average and travelled further by bicycle than females.³ Research using large-scale cycling data has similarly identified a persistent gender gap in cycling across cities.⁴

But this is also where the limitations of existing data become apparent. National transport surveys can tell us how cycling differs between males and females, but they do not provide the same picture of cycling by gender identity. The National Travel Survey's published active-travel statistics are broken down by sex, while the National Travel Attitudes Study likewise reports people's safety-related walking and cycling behaviours by male and female. For example, the National Travel Attitudes Study found that women were more likely than men to report choosing particular routes, travelling only at certain times, or travelling with others in order to feel safe.⁵

These findings demonstrate the value of collecting disaggregated data: they make differences in travel behaviour and experiences visible. But they also highlight a gap. The Office for National Statistics now collects gender identity data through the Census, including categories such as trans man, trans woman and non-binary.⁶ Yet this does not mean that national transport datasets routinely connect gender identity with cycling behaviour and safety. We therefore have considerably less evidence about how trans and gender-diverse people experience cycling.

Safety is far more than the likelihood of being involved in a collision. People's willingness to cycle is also shaped by how safe they feel from harassment, intimidation and discrimination. The National Travel Attitudes Study demonstrates that safety concerns can influence how people travel: women were more likely than men to report changing when, where and with whom they walked or cycled in order to feel safe.⁵ Research by the London Cycling Campaign has also documented widespread experiences of abuse and intimidation among women who cycle.⁷

The absence of inclusive gender data also limits our ability to understand how different aspects of identity interact. A disabled non-binary cyclist, for example, may face barriers that differ significantly from those experienced by either disabled men or disabled women. Similarly, race, age, sexuality and socioeconomic status can all influence how people experience transport. Without inclusive data collection, these intersecting experiences can remain hidden, making it more difficult to design transport systems that work for everyone.¹

This invisibility has practical consequences. Local authorities prioritise investment based on evidence. Researchers identify problems through datasets. Campaigners rely on statistics to demonstrate need and argue for change. If trans and gender-diverse cyclists are missing from those datasets, it becomes harder to demonstrate the scale of any barriers they face, influence policy or monitor whether interventions improve safety.

Inclusive data collection can take many forms. Surveys can allow respondents to self-describe their gender or include options beyond simply "male" and "female", while respecting privacy and giving people the option not to disclose. The experience of the 2021 Census shows that it is possible to collect gender identity data at a national level: the Census included a voluntary question asking whether people's gender identity was the same as their sex registered at birth, with an option to provide a gender identity where it differed.⁶ Researchers should also consider whether sex, gender identity or both are relevant to the research question, ensuring that data collection is meaningful rather than tokenistic.¹

Binary categories alone are not sufficient to understand the diversity of people who use, or would like to use, our streets. Creating safer cycling environments requires understanding the experiences of everyone who uses them. We cannot address barriers that we have never measured, nor identify inequalities that remain statistically invisible. If cycling is to become a genuinely inclusive mode of transport, our data must become more inclusive too.

Because if some people are missing from the evidence, they are too often missing from the solutions.

References

¹ UK Statistics Authority. (2021). Inclusive Data Taskforce recommendations report: Leaving no one behind – How can we be more inclusive in our data? https://uksa.statisticsauthority.gov.uk/publication/inclusive-data-taskforce-recommendations-report-leaving-no-one-behind-how-can-we-be-more-inclusive-in-our-data/ 

² Criado Perez, C. (2019). Invisible Women: Data Bias in a World Designed for Men. Abrams Press.

³ Department for Transport. (2025). National Travel Survey 2024: Active travel. https://www.gov.uk/government/statistics/national-travel-survey-2024/nts-2024-active-travel 

⁴ Battiston, Alice, Ludovico Napoli, Paolo Bajardi, André Panisson, Alan Perotti, Michael Szell and Rossano Schifanella. (2022). “Revealing the determinants of gender inequality in urban cycling with large-scale data.” https://doi.org/10.1140/epjds/s13688-023-00385-7

⁵ Department for Transport. (2023). National Travel Attitudes Study: Wave 8. https://www.gov.uk/government/statistics/national-travel-attitudes-study-wave-8/national-travel-attitudes-study-wave-8 

⁶ Office for National Statistics. (2023). Gender identity, England and Wales: Census 2021. https://www.ons.gov.uk/peoplepopulationandcommunity/culturalidentity/genderidentity/bulletins/genderidentityenglandandwales/census2021 

⁷ London Cycling Campaign Women's Network. (2024). What Stops Women Cycling? https://lcc.org.uk/what-stops-women-cycling-report/