A Multimodal Human-Centered Framework for Assessing Pedestrian Well-Being in the Wild
Abstract
Pedestrian well-being is a critical yet rarely measured component of sustainable urban mobility and livable city design. Existing approaches to evaluating pedestrian environments often rely on static, infrastructure-based indices or retrospective surveys, which overlook the dynamic, subjective, and psychophysiological dimensions of everyday walking experience. This paper introduces a multimodal, human-centered framework for assessing pedestrian well-being in the wild by integrating three complementary data streams: continuous physiological sensing, geospatial tracking, and momentary self-reports collected using the Experience Sampling Method. The framework conceptualizes pedestrian experience as a triangulation enabling a holistic understanding of how urban environments influence well-being. The utility of our framework is then demonstrated through a naturalistic case study conducted in the Greater Philadelphia region, in which participants wore research-grade wearable sensors and carried GPS-enabled smartphones during their regular daily activities. Physiological indicators of autonomic nervous system activity, including heart rate variability and electrodermal activity, were synchronized with spatial trajectories and in situ self-reports of stress, affect, and perceived infrastructure conditions. Results illustrate substantial inter- and intra-individual variability in both subjective experience and physiological response, as well as context-dependent patterns associated with traffic exposure, pedestrian infrastructure quality, and environmental enclosure. The findings also suggest that commonly used walkability indices may not fully capture experiential dimensions of pedestrian well-being. By enabling real-world, multimodal measurement of pedestrian experience, the proposed framework offers a scalable and transferable approach for advancing human-centered urban analytics.
Summary
This paper presents a novel multimodal, human-centered framework for assessing pedestrian well-being in real-world urban environments. The authors argue that existing methods, such as walkability indices and retrospective surveys, fail to capture the dynamic, subjective, and psychophysiological aspects of the walking experience. The framework integrates three data streams: continuous physiological sensing (heart rate variability and electrodermal activity via wearable sensors), geospatial tracking (GPS-enabled smartphones), and momentary self-reports (Experience Sampling Method) to provide a holistic understanding of how urban environments influence pedestrian well-being. The authors conducted a case study in the Greater Philadelphia region where participants wore research-grade wearable sensors and used GPS-enabled smartphones during their daily activities. The data was synchronized to analyze the relationship between physiological indicators, spatial trajectories, and self-reported experiences of stress, affect, and perceived infrastructure conditions. The key findings highlighted substantial inter- and intra-individual variability in both subjective experience and physiological response, with context-dependent patterns linked to traffic exposure, pedestrian infrastructure quality, and environmental enclosure. The study concluded that commonly used walkability indices may not fully capture the experiential dimensions of pedestrian well-being. This research matters to the field of urban analytics because it offers a scalable and transferable approach for real-world, multimodal measurement of pedestrian experience, enabling more human-centered urban design.
Key Insights
- •The framework integrates physiological sensing, geospatial tracking, and experience sampling to triangulate pedestrian experience, capturing environmental context, objective physiological responses, and subjective perceptions.
- •Results show substantial inter- and intra-individual variability in both subjective experience and physiological response, highlighting the need for personalized assessments of pedestrian well-being.
- •Commonly used walkability indices may not fully capture experiential dimensions of pedestrian well-being, suggesting the need for more comprehensive evaluation methods.
- •The study identified context-dependent patterns associated with traffic exposure, pedestrian infrastructure quality, and environmental enclosure, revealing specific urban factors that impact pedestrian well-being.
- •The case study in Philadelphia found that 61% of participants reported encountering at least one walking-related problem during the study period, highlighting the prevalence of infrastructure challenges.
- •Text mining analysis of open-ended responses revealed that "sidewalk," "crosswalk," "walking," and "traffic" were the most frequently mentioned terms, indicating widespread concerns related to pedestrian infrastructure and road interactions.
- •The implementation used a 5-minute rolling window with a 1-second step size to compute RMSSD (Root Mean Square of Successive Differences) and extract EDA (Electrodermal Activity) features, providing a high-resolution time series of autonomic activity.
Practical Implications
- •Urban planners and designers can use the framework to identify specific areas where pedestrian well-being is compromised, informing targeted interventions to improve infrastructure and environmental conditions.
- •The framework can be used to evaluate the impact of urban design interventions on pedestrian well-being, providing data-driven evidence to support policy decisions.
- •The multimodal data collected can be used to develop predictive models of pedestrian stress and well-being, enabling proactive interventions to mitigate negative impacts.
- •The framework opens up future research directions, including exploring the impact of specific environmental factors (e.g., noise, air pollution) on pedestrian well-being and investigating the effectiveness of different interventions to improve the walking experience.
- •The study's findings can inform the development of more comprehensive walkability indices that incorporate experiential dimensions, leading to more accurate and human-centered assessments of urban environments.