Assessing pedestrian stress with biometric sensing and survey responses

Abstract

Recent advances in biometric sensing technologies, such as eye tracking, heart rate trackers, and galvanic skin response (GSR) sensors, offer new opportunities to measure pedestrian stress level and their travel experiences in real-time. Uncertainty remains about whether biometric sensor measurements of stress align with self-reported stress. We investigate the association between pedestrians’ sensor-measured stress and survey-reported stress, as well as the temporal sensitivity of sensor metrics across varying time intervals. We conducted a semi-naturalistic walking experiment along a 1.2-mile route featuring six streets with distinct built environment features. Thirty participants, equipped with sensors to measure heart rate variability (HRV), electrodermal activities (EDA), and gaze behaviors (with eye-tracking glasses), walked the route and completed post-experiment surveys rating stress levels for each street. Forty-eight stress-related sensor metrics were compared to survey ratings using bivariate and multivariate methods. Our findings emphasize the importance of a within-subject analytical approach and controlling for confounding factors to robustly associate sensor results with survey outcomes. EDA metrics, collected from GSR sensors, responded more quickly to acute stress, while HRV and gaze metrics are more reliable over longer intervals (30–120 s) to reflect walking stress. We discuss challenges in analyzing and interpreting our sensor measurements and how they measure stress. We draw from the theory of risk homeostasis to explain discrepancies between sensor and survey results. Our methodological framework and findings provide guidance on whether and how biometric sensors can be used to identify pedestrian stress levels.

Citation

Shiyu Ma, Wenwen Zhang, Robert B. Noland, Clinton J. Andrews, Hannah Younes, Leigh Ann Von Hagen, Assessing pedestrian stress with biometric sensing and survey responses,
Transportation Research Part F: Traffic Psychology and Behaviour, Volume 115, 2025, 103347, ISSN 1369-8478, https://doi.org/10.1016/j.trf.2025.103347.