Understanding the influence of urban characteristics on cyclists’ stress measured through wearable sensors: A quantitative open data approach

The complexity of environmental factors experienced in active mobility presents unique challenges for the design of sustainable urban mobility environments. Particularly, active mobility modes are frequently associated with increased stress and unsafety. Most studies apply qualitative assessment methods for evaluating cyclists’ stress levels and subjective cycling experiences. Quantitative approaches are either limited in sample size, or conducted over short periods of time. This study introduces a transferable methodology that combines physiological measurements from wearable sensors with openly available spatial data to assess environmental stressors in urban cycling. A field study was conducted in Osnabrück, Germany, and involved 89 participants, 1,780 cycling trips, and 2,104,109 geo-referenced data points. Stress levels were quantified through processed Electrodermal Activity (EDA) measurements to identify Moment of Stress (MOS) along mapped road segments. We derived features from OpenStreetMap (OSM), Sentinel-2 Remote Sensing (RS), and Mapillary Street View Imagery (SVI) to characterise spatial elements of the built and natural environment. Using feature importance methods on top of a Random Forest (RF) Machine Learning (ML) model, we identified key environmental aspects associated with cyclists’ stress. Results show that the availability of cycling infrastructure, traffic regulations and other road users, are of higher importance than the availability of green space, when it comes to predicting the stress potential of individual road segments. The proposed methodology offers a multi-faceted and extensible approach to evaluate environmental characteristics related to stress, providing information for creating safer and more comfortable cycling environments. While our approach investigated spatiotemporal stress factors in cycling, the use and the availability of open data sources restricts the feature set that can be derived and evaluated in a particular region. We encourage future research to apply and extend this approach in diverse urban contexts, incorporating temporally dynamic features to support evidence-based mobility planning.