Project Description
PI
Student Lead
Accessible parking is critical for people with disabilities (PwDs), allowing
equitable access to destinations, independent mobility, and community
participation. Despite mandates, there has been no large-scale investigation
of the quality or allocation of disability parking in the US nor significant
research on PwD perspectives and uses of disability parking. In this paper,
we first present a semi-structured interview study with 11 PwDs to advance
understanding of disability parking uses, concerns, and relevant technology
tools. We find that PwDs often adapt to disability parking challenges
according to their personal mobility needs and value reliable, real-time
accessibility information. Informed by these findings, we then introduce a new
deep learning pipeline, called AccessParkCV, and parking
dataset for automatically detecting disability parking and inferring
quality characteristics (e.g., width) from orthrectified aerial
imagery. We achieve a micro-F1=0.89 and demonstrate how our pipeline
can support new urban analytics and end-user tools. Together, we contribute
new qualitative understandings of disability parking, a novel detection
pipeline and open dataset, and design guidelines for future tools.
Funding
Publications
"Where Can I Park?" Understanding Human Perspectives and Scalably Detecting Disability Parking from Aerial Imagery
Proceedings of ASSETS 2025 To Appear | Acceptance Rate: 29.7% (83 / 279)
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