<p>The spatiotemporal nature of urban environments — encompassing both, interactions among stationary features of the built and natural surroundings, and dynamic elements such as road users — pose significant challenges to the adoption of active mobility. Previously, influences of the urban environment on active mobility users have been typically investigated in unimodal approaches, thus greatly neglecting the diverse range of urban stressors that adversely affect (sustainable) mobility experiences, which can be inferred from multimodal data. For this reason, we propose a multimodal approach to investigate urban stress factors. Our methodology integrates data from wearable sensors and visual urban media to gain a more comprehensive understanding of spatiotemporal stressors in urban environments and active mobility. Spatially clustered stress measurements, i.e., hotspots and coldspots, derived from physiological reactions of the body, are used as labels for the classification of high-stress and low-stress urban areas. Semantic segmentation-based visual features, describing the immediate surroundings, are derived from real-time videos and snapshots of street scenes, captured through Street View Imagery (SVI). By comparing isovist features, i.e., visual impressions of dynamically changing urban scenes from a cyclists’ point of view (POV), with scenes captured through SVI, we show that SVI provides a valuable data source for urban visual intelligence and relating high-stress cycling experiences to the surrounding environmental characteristics. While our Random Forest (RF) model trained on SVI-based features outperformed a POV video-based model by 3.9 percentage points in accuracy and 5.3 percentage points in recall (accuracy: 72.6% vs. 68.7%, recall: 72.2% vs. 66.9%), we encourage future studies to validate our findings in regions with higher environmental diversity, lower coverage of SVI data, and with additional data sources to account for confounding factors.</p>