The Strava Metro Academic Research Program launched in 2025 to support applied research using de-identified, aggregated activity data. During its first year, participating institutions examined a wide range of active transportation and recreation topics across different geographic and planning contexts.
The highlights below show how Strava Metro data was used to complement traditional datasets, surface patterns that are difficult to capture with counts alone, and support research with practical planning relevance
This project examined how cycling activity aligns with the proposed Interborough Express corridor in New York City. Researchers used Strava Metro to compare current cycling patterns with future station areas and surrounding streets. The analysis showed where protected infrastructure already supports riding and where gaps may limit access. The work helps planners understand how cycling demand can inform transit-oriented design around new rail investments.
Researchers studied where people run and how access differs across neighborhoods. By combining Strava Metro data with information on green space, traffic, and income, the team identified uneven access to supportive running environments. Higher activity appeared in areas with more green space and fewer barriers. The findings offer a way to assess who benefits from existing conditions and where improvements may matter most.
This research focused on how e-mountain bikes affect trail use over time. Using multi-year Strava Metro data, the team observed changes in speed, route choice, and trail demand. E-mountain bike activity appeared to support higher overall use and different riding patterns. The work helps land managers better understand how new bike technology shapes trail behavior and planning needs.
This project paired community-led mapping with observed travel patterns. Students with cognitive disabilities identified routes that felt safe or stressful, while Strava Metro data showed where people travel today. Together, these inputs revealed gaps between perceived safety and actual use. The approach demonstrates how lived experience and activity data can inform safer route planning.
Researchers explored how the built environment supports physical activity among older adults. Using Strava Metro alongside data on footpaths, crossings, and green space, the team identified features linked with higher activity levels. The work connects mobility data with public health outcomes and highlights where everyday infrastructure supports active aging.
This study examined how cyclists share limited space in dense urban settings. Researchers combined field observation with Strava Metro patterns to understand how commuters, recreational riders, and everyday cyclists interact. The findings show how different uses overlap on constrained networks and why traditional counts miss these dynamics. The work supports more inclusive design of shared cycling space.
This project focused on a small-city context with limited existing data. Researchers compared Strava Metro activity with streets residents identified as uncomfortable or hard to use. The analysis highlighted gaps between preferred routes and actual behavior. The work provides a baseline for planning conversations in places without extensive counting systems.
In a city with few official cycling counts, researchers used Strava Metro to map where riding already occurs. The data revealed high-use corridors and emerging routes across districts. This visibility helped identify where continuous infrastructure could have the greatest impact. The project shows how activity data can support planning in data-limited environments.
Together, the Year 1 cohort shows how activity data supports planning and research when paired with local context and expertise. Helping to surface gaps, test ideas, and inform decisions
