The Gender Gap in Mobility Data

The well-known story of snow clearing in Karlskoga, Sweden highlights exactly why it’s important to consider how we collect and analyze data, and who’s involved in decision-making (Perez, 2019). In 2011, Karlskoga discovered that its seemingly gender-neutral snow-clearing policy (prioritizing major roads before sidewalks and bike paths) was unintentionally disadvantaging women. Because men were more likely to drive and women more likely to walk or use public transit while managing complex, care-related “trip-chaining” travel patterns, women faced greater risks from icy conditions. When the town reversed its snow-clearing order to prioritize pedestrians and public transport users, it not only improved safety but also saved money, since pedestrian injuries (mostly among women) cost about twice as much as winter road maintenance. The issue revealed a broader gender data gap in transport planning, where men’s simpler commuting patterns dominate data collection and decision-making, while women’s more varied, unpaid care-related travel remains under-counted or mislabeled. 

Over the decades, Montreal’s origin-destination (OD) data collection has shifted from traditional telephone household surveys conducted since the 1970s to a hybrid system that includes large web-enabled OD surveys, passive transaction data from the OPUS smart-card system, BIXI trip logs, and mobile-app-based traces. This evolution has profoundly affected how, and whether, gender appears in travel data and analysis. Early OD surveys often under-counted short pedestrian and “trip-chained” care-related trips, frequently designating a single household respondent as representative, which obscured women’s more complex mobility patterns (Ravensbergen et al., 2023). The 2018 ARTM/MTQ survey introduced web questionnaires and more complex trip coding that enabled the identification of “mobility of care” and sex-disaggregated analyses (Balarezo et al., 2024).

However, newer passive sources like OPUS smart-card transactions and BIXI trip logs provide high-resolution temporal and spatial data without direct gender attributes, forcing researchers to infer or link demographics from other sources, an approach that can introduce selection and inference bias (Delisle, 2024). Recent micromobility and phone-trace studies continue to expose a persistent gender data gap, where women are underrepresented and short, non-motorized trips remain under-counted (Preston et al., 2022). Moreover, inconsistencies in how “sex” versus “gender” are recorded, if at all, limit comparability and reinforce analytic blind spots. Analytical pipelines, from data cleaning to machine-learning predictions, risk amplifying these biases when gender is not explicitly considered (Nadeem et al., 2020).

Montreal’s data collection has become more diversified and technologically sophisticated, but the visibility of gender in data remains uneven. Only recently have methodologies begun to address the systemic underrepresentation of women’s mobility patterns and to highlight how care work, safety concerns, and trip-chaining continue to shape gendered mobility outcomes (Ravensbergen et al.,2023; Balarezo et al., 2024).

On Curbcut’s platforms, we’ve incorporated several methodological choices in our analysis of accessibility to services to mitigate this unevenness:

• assess accessibility across all travel modes (walking, cycling, public transit, and driving) rather than focusing solely on car commutes
• include a wide range of everyday destinations, including childcare, groceries, health services, and recreation, not just workplace
• incorporate time-of-day variations such as transit frequency and opening hours, avoiding assumptions about 9-to-5 mobility
• use fine-grained spatial data to detect inequalities within neighbourhoods, not just across large zones

Of course, this is only a start. Methodological adjustments on their own can’t resolve the structural issues that produce gendered mobility inequities. Closing the gender data gap requires both a bigger shift in how cities collect and interpret data, and involving women in planning and designing transport systems within broader urban policies that reflect how women actually move through cities. What might our cities look like when we start designing mobility systems for everyone?