Crowdsourced Data Applications in Active Transportation Systems

Crowdsourced data have emerged as a pivotal resource for understanding and enhancing active transportation systems—modes of travel powered by human effort, such as walking and cycling. By harvesting digital traces from millions of users of mobile applications and wearable devices, researchers can map routes, quantify flows and identify temporal patterns with unprecedented spatial and temporal detail. These datasets enable the evaluation of infrastructure performance, the detection of under-served areas and the analysis of environmental exposures, including air and noise pollution, experienced by pedestrians and cyclists. Despite their richness, crowdsourced datasets exhibit systematic biases, such as over-representation of certain demographic groups or recreational users, necessitating robust correction techniques to ensure representativeness. Integrative approaches combine crowdsourced streams with official counts, survey data and environmental layers to produce refined models of active travel demand. In turn, these models inform urban planning, public health assessments and real-time management of transportation networks. Global case studies—from major European capitals to North American and Australasian cities—demonstrate the capacity of user-generated data to guide infrastructure investment, optimise route networks and promote equity in access to healthy, sustainable mobility options.

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Crowdsourced Data Applications in Active Transportation Systems publication trend

The graph below shows the total number of articles in crowdsourced data applications in active transportation systems across all publications each year (not limited to Nature Index journals).

Technical terms

Crowdsourced data: Information collected from a large number of individual users, typically via mobile applications or online platforms, providing high-resolution spatio-temporal records of travel behaviour.

Active transportation systems: Networks and infrastructure that support human-powered travel modes, principally walking and cycling, for commuting, recreation or utility purposes.

Location-based services (LBS): Digital services that leverage the geographic position of a user’s mobile device to collect or provide information tied to specific locations.

Geospatial mapping: The process of visualising and analysing spatial data using geographic information systems to reveal patterns and relationships across geographic areas.

References

1. Bias and precision of crowdsourced recreational activity data from Strava. Landscape and Urban Planning (2023).
2. Re-examining the role of street network configuration on bicycle commuting using crowdsourced data. Journal of Transport Geography (2024).
3. Crowdsourced cycling data applications to estimate noise pollution exposure during urban cycling. Heliyon (2024).
4. Correcting Bias in Crowdsourced Data to Map Bicycle Ridership of All Bicyclists. Urban Science (2019).
5. Association between passively collected walking and bicycling data and purposefully collected active commuting survey data—United States, 2019. Health & Place (2023).