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Data on pedestrian infrastructure is essential for improving the mobility environment and for planning efficiency. Although governmental agencies are responsible for capturing data on pedestrian infrastructure mostly by field audits, most have not completed such audits. In recent years, virtual auditing based on street view imagery (SVI), specifically through geo-crowdsourcing platforms, offers a more inclusive approach to pedestrian movement planning, but concerns about the quality and reliability of opensource geospatial data pose barriers to use by governments. Limited research has compared opensource data in relation to traditional government approaches. In this study, we compare pedestrian infrastructure data from an opensource virtual sidewalk audit platform (Project Sidewalk) with government data. We focus on neighborhoods with diverse walkability and income levels in the city of Seattle, Washington and in DuPage County, Illinois. Our analysis shows that Project Sidewalk data can be a reliable alternative to government data for most pedestrian infrastructure features. The agreement for different features ranges from 75% for pedestrian signals to complete agreement (100%) for missing sidewalks. However, variations in measuring the severity of barriers challenges dataset comparisons.
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This content will become publicly available on June 22, 2026
Urban Walkability and Pedestrian Stress: A Sensor-Based Study Across Three Sites
This study investigates the relationship between urban walkability and human stress across three distinct sites, utilizing data collected from wearable sensors. The objective is to assess how urban design and environmental factors influence human stress while walking. Participants were equipped with wearable sensors to monitor physiological indicators of stress (e.g., heart rate variability, etc.) as they walked through different urban environments. Data was collected in realtime to capture fluctuations in stress levels and provide insights into how specific urban design features impact pedestrian well-being. To facilitate data collection and analysis, walking areas were divided into blocks, and urban design features were grouped into six categories such as imageability, enclosure, human scale, transparency, complexity, and safety. Each city has different features, depending on the issues that were considered most pressing for that city. To supplement sensor stress data, the study also utilized surveys to gather participants’ perceptions of safety, comfort, and environmental quality. Using regression analysis, researchers identified the urban design categories that have a significant impact on stress scores and their frequency. Machine learning models were built to predict stress scores based on the urban design aspects and air quality data as input features. Results showed that increased stress is correlated with poorly designed walkways, while lower stress was linked to well-maintained paths and green spaces. Transparency and enclosure were identified as significant contributors to pedestrian stress. The findings from one of the three cities add another dimension to the understanding of walkability and stress, highlighting that there are factors beyond basic infrastructure, such as noise levels and tree canopy can play a significant role in influencing pedestrian well-being. Findings from this research can facilitate targeted infrastructure planning and investment, better mobility, and ultimately improve the quality of life in urban areas. Future research should consider a wider range of environmental and social factors and how different factors interact over time to influence stress levels.
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- Award ID(s):
- 2111386
- PAR ID:
- 10613396
- Publisher / Repository:
- ASEE Annual Conference
- Date Published:
- Format(s):
- Medium: X
- Location:
- Montreal, QC, Canada
- Sponsoring Org:
- National Science Foundation
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