Capturing Environmental Distress of Pedestrians Using Multimodal Data: The Interplay of Biosignals and Image-Based Data
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 2
Abstract
Urban built environments often include many negative stimuli (e.g., unleashed dogs, dead animals, litter, graffiti, abandoned vehicles) that are linked with stress symptomatology among urban populations. Biosignals (e.g., electrodermal activity, gait patterns, and blood volume pulse) can help assess pedestrian distress levels induced by negative environmental stimuli by overcoming the measurement limitations of traditional self-reporting methods and field observations. Despite their potential, biosignals from naturalistic outdoor environments are often contaminated by uncontrollable extraneous factors (e.g., movement artifacts, physiological reactivity due to unintended stimuli, and individual variability). Thus, more quantitative evidence and novel methodological approaches are required to accurately capture pedestrian environmental distress resulting from negative environmental stimuli. In this context, we investigate the interplay between pedestrians’ biosignal data and image-based data (built environment feature information and perceptual distress levels identified from images) in a machine learning model. Results from the statistical model estimated with the biosignal data demonstrated significant physiological responses to the negative environmental stimuli. The use of the features from image-based data increased the prediction accuracy of the computational model. This method can be applied to geospatial intelligence, further advancing built environmental assessments and evidence-based approaches to promote walking and walkable communities.
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Data Availability Statement
Some codes and models (e.g., saliency detection method and decision tree model) that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study was partially supported by Texas A&M University X-grants and Institute of Construction and Environmental Engineering (ICEE) at Seoul National University. Any opinions, findings, conclusions, or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the Texas A&M University and ICEE.
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Journal of Computing in Civil Engineering
Volume 36 • Issue 2 • March 2022
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© 2021 American Society of Civil Engineers.
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Received: Jun 13, 2021
Accepted: Oct 31, 2021
Published online: Dec 15, 2021
Published in print: Mar 1, 2022
Discussion open until: May 15, 2022
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